Steve HorvathpersName (2013date). “DNA methylation age of human tissues and cell types.” Genome Biologytitle·j.
Object of study — DNA methylation ageterm as a cross-tissue predictor, epigenetic clock, and candidate aging biomarker
Research question — whether DNA methylationterm can accurately predict age across human tissues and yield a biologically meaningful measure
Contribution — a freely available 353-CpG multi-tissue age predictorterm plus validation and biological characterization across healthy, stem, primate, and cancer samplesterm
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | cross-tissue epigenetic aging, age acceleration, and the proposed work of an epigenetic maintenance system — “I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research” | |
| claim-type | method — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | |
| method — evidence | — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | |
| object of study — evidence | — “It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure” | |
| research question — evidence | — “It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure” | |
| contribution — evidence | — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | |
| mechanism | elastic-net CpG selection Penalized regression selects 353 CpGs from probes shared by the 27K and 450K arrays. — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | mechanisms[0] |
| mechanism | weighted clock construction Regression coefficients form a weighted average of the 353 clock CpGs. — “Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylation data from healthy tissues, cancer tissues, and cancer cell lines. The healthy tissue data allowed me to develop a multi-tissue predictor of age (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate” | mechanisms[1] |
| mechanism | age calibration A calibration function transforms the weighted clock score into DNAm age. — “Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylation data from healthy tissues, cancer tissues, and cancer cell lines. The healthy tissue data allowed me to develop a multi-tissue predictor of age (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate” | mechanisms[2] |
| mechanism | leave-one-data-set-out validation Each data set is predicted by a separate model fitted to all remaining data sets. — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | mechanisms[3] |
| mechanism | taxonomically broad training Training spans many tissues and both Illumina platforms to support tissue-independent prediction. — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | mechanisms[4] |
| mechanism | clock reset by reprogramming Reprogramming primary cells to iPS state is accompanied by a near-zero DNAm age. — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | mechanisms[5] |
| mechanism | passage-associated clock increase Repeated cell passaging increases DNAm age in three independent data sets. — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | mechanisms[6] |
| mechanism | age-conditioned tissue variance Regression and ANOVA measure tissue effects after adjustment for age. — “To measure tissue effects in the training data (Additional file 8 ), I used analysis of variance (ANOVA) to calculate an F statistic as follows. First, a multivariate regression model was used to regress each CpG (dependent variable) on age and tissue type. The analysis adjusted for age since the different data sets have very different mean ages (Additional file 1 ). Next, ANOVA based on the multivariate regression model was used to calculate an F statistic, F.tissueTraining , for measuring the tissue effect in the training data. This F statistic measures the tissue effect after adjusting for age in the training data sets. I did not translate the F statistic into a corresponding P -value since the latter turned out to be extremely significant for most CpGs. Additional file 8 D shows that F.tissueTraining is highly correlated with an independent measure of tissue variance” | mechanisms[7] |
| mechanism | early-life nonlinear calibration The calibration curve changes logarithmically until adulthood and linearly thereafter. — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | mechanisms[8] |
| mechanism | epigenetic maintenance response The EMS model links perturbations of epigenetic stability to accelerated clock work. — “If this EMS model of DNAm age is correct, then DNAm age should be accelerated by many perturbations that affect epigenetic stability. Further, age acceleration should have some beneficial effects given the protective role of the EMS. In particular, the EMS model of DNAm age entails the following testable predictions. First, cancer tissue should show signs of accelerated age, reflecting the protective actions of the EMS. Second, many mitogens, genomic aberrations, and oncogenes, which trigger the response of the EMS, should be associated with accelerated DNAm age. Third, high age acceleration of cancer tissue should be associated with fewer somatic mutations given the protective role of the EMS. Fourth, mutations in TP53 should be associated with a lower age acceleration of cancer tissue if one further assumes that p53 signaling helps trigger the EMS” | mechanisms[9] |
| mechanism | twin-based heritability estimation MZ and DZ twin correlations enter Falconer's broad-sense heritability formula. — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | mechanisms[10] |
| mechanism | cancer genomic stratification Tumors are stratified by mutation, receptor, grade, stage, and cancer type to compare acceleration. — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | mechanisms[11] |
| extends | single-tissue DNA methylation age predictors — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | claim_relations[0] |
| builds-on | public Illumina methylation data sets — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | claim_relations[1] |
| extends | epigenetic biomarkers of aging — “DNAm age is arguably a promising marker for studying human development, aging, and cancer. It may become a useful surrogate marker for evaluating rejuvenation therapies. The most salient feature of DNAm age is its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease (for example, cancer). It remains to be seen whether the DNAm age of easily accessible fluids/tissues (for example, saliva, buccal cells, blood, skin) can serve as a surrogate marker for inaccessible tissues (for example, brain, kidney, liver). It is noteworthy that DNAm age is applicable to chimpanzee tissues. Given the high heritability of age acceleration in young subjects, I expect that age acceleration will mainly be a relevant measure in older subjects. Using a relatively small data set, I did not find any evidence that a premature aging disease (progeria) is associated with accelerated DNAm age (Figure 2 T). In Additional file 2 , I discuss whether DNAm age fulfils the biomarker criteria developed by the American Federation for Aging Research” | claim_relations[2] |
| builds-on | elastic-net age prediction — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | claim_relations[3] |
| sense | DNAm age as age predicted by a 353-CpG penalized-regression clock and calibration function — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | senses[0] |
| sense | age correlation as Pearson correlation between DNAm age and chronological age — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | senses[1] |
| sense | median error as median absolute difference between DNAm and chronological age — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | senses[2] |
| sense | age acceleration as DNAm age minus chronological age — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | senses[3] |
| sense | epigenetic clock as the regression-weighted combination of 353 clock CpGs — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | senses[4] |
| sense | clock ticking rate as the rate of change of the age-calibration curve — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | senses[5] |
| sense | EMS model as DNAm age measuring cumulative epigenetic-maintenance work — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | senses[6] |
| sense | positive clock CpGs as sites that gain methylation with age — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | senses[7] |
| sense | negative clock CpGs as sites that lose methylation with age — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | senses[8] |
| sense | LOOCV as refitting on all data sets except the one being evaluated — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | senses[9] |
| sense | multi-tissue applicability as prediction without tissue-specific offsets for most tissues — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | senses[10] |
| sense | DNAm age as an aging marker rather than an established aging effector — “Future research will need to clarify whether DNAm age is only a marker of aging or relates to an effector of aging. In conclusion, the epigenetic clock described here is likely to become a valuable addition to the telomere clock” | senses[11] |
| control | training data alone used to define the predictor — held — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” — rules out test-set leakage into age-predictor construction — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | controls[0] |
| control | held-out test data performance assessment — held — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” — rules out reporting only optimistically biased training accuracy — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” | controls[1] |
| control | leave-one-data-set-out cross-validation — held — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” — rules out data-set-specific training leakage in per-data-set accuracy estimates — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | controls[2] |
| control | cancer tissues excluded from the initial healthy-tissue data — held — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” — rules out cancer methylation effects contaminating predictor construction — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | controls[3] |
| control | probes required on both 27K and 450K platforms with fewer than ten missing values — held — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” — rules out platform-specific or highly missing predictor inputs — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | controls[4] |
| control | three independent iPS and passaging data sets — held — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” — rules out a cell-reprogramming or passage result confined to one experiment — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | controls[5] |
| control | Bonferroni correction of tumor morphology tests — held — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” — rules out treating nominal grade/stage findings as family-wise significant — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | controls[6] |
| note | single-tissue DNA methylation age predictors — The predictor extends age estimation across most tissues without tissue-specific offsets. — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | claim_relations[0] |
| weight | single-tissue DNA methylation age predictors — 0.95 — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | claim_relations[0] |
| note | public Illumina methylation data sets — Predictor construction and validation combine 82 public 27K/450K data sets. — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | claim_relations[1] |
| note | epigenetic biomarkers of aging — The paper proposes broad developmental, aging, cancer, and possible therapy-evaluation uses. — “DNAm age is arguably a promising marker for studying human development, aging, and cancer. It may become a useful surrogate marker for evaluating rejuvenation therapies. The most salient feature of DNAm age is its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease (for example, cancer). It remains to be seen whether the DNAm age of easily accessible fluids/tissues (for example, saliva, buccal cells, blood, skin) can serve as a surrogate marker for inaccessible tissues (for example, brain, kidney, liver). It is noteworthy that DNAm age is applicable to chimpanzee tissues. Given the high heritability of age acceleration in young subjects, I expect that age acceleration will mainly be a relevant measure in older subjects. Using a relatively small data set, I did not find any evidence that a premature aging disease (progeria) is associated with accelerated DNAm age (Figure 2 T). In Additional file 2 , I discuss whether DNAm age fulfils the biomarker criteria developed by the American Federation for Aging Research” | claim_relations[2] |
| weight | epigenetic biomarkers of aging — 0.7 — “DNAm age is arguably a promising marker for studying human development, aging, and cancer. It may become a useful surrogate marker for evaluating rejuvenation therapies. The most salient feature of DNAm age is its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease (for example, cancer). It remains to be seen whether the DNAm age of easily accessible fluids/tissues (for example, saliva, buccal cells, blood, skin) can serve as a surrogate marker for inaccessible tissues (for example, brain, kidney, liver). It is noteworthy that DNAm age is applicable to chimpanzee tissues. Given the high heritability of age acceleration in young subjects, I expect that age acceleration will mainly be a relevant measure in older subjects. Using a relatively small data set, I did not find any evidence that a premature aging disease (progeria) is associated with accelerated DNAm age (Figure 2 T). In Additional file 2 , I discuss whether DNAm age fulfils the biomarker criteria developed by the American Federation for Aging Research” | claim_relations[2] |
| note | elastic-net age prediction — A calibrated chronological-age response is regressed on shared CpG probes with glmnet. — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | claim_relations[3] |
| note | cytosine-5 methylation —same-as→ DNA methylation — Explicit naming equivalence. — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concept_edges[0] |
| relation | cytosine-5 methylation —same-as→ DNA methylation — Cytosine-5 methylation within CpG dinucleotides is also known as DNA methylation. — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concept_edges[0] |
| audit | Average age acceleration —operationalised-as→ chronological age — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but average age acceleration is DEFINED as the average difference between DNAm age and chronological age; operationalised-as is loose. — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concept_edges[1] |
| note | Average age acceleration —operationalised-as→ chronological age — Exact operational definition; DNAm age is the other term in the difference and remains named in evidence. — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concept_edges[1] |
| relation | Average age acceleration —operationalised-as→ chronological age — Average age acceleration is defined by the average difference between DNAm age and chronological age. — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concept_edges[1] |
| note | positively related CpGs —contrasted-with→ negatively related CpGs — Direct categorical contrast between the two signed groups. — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | concept_edges[3] |
| relation | positively related CpGs —contrasted-with→ negatively related CpGs — Positive and negative clock CpGs are contrasted by age direction, tissue variance, shores, and Polycomb binding. — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | concept_edges[3] |
| note | DNAm age —distinct-from→ mitotic age — Explicit distinction supported by non-proliferative tissue and blood-cell results. — “Since epigenetic somatic errors in somatic replications appear to be readily detected as age-related changes in methylation [ 35 , 36 ], it is a plausible hypothesis that DNAm age measures the number of somatic cell replications. In other words, that it measures mitotic age (which assigns a cell copy number to every cell) [ 35 , 37 ]. While DNAm age is correlated with cell passage number (Figure 5 ) and the clock ticking rate is highest during organismal growth (Figure 6 B,C), it is clearly different from mitotic age since it tracks chronological age in non-proliferative tissue (for example, brain tissue) and assigns similar ages to both short and long lived blood cells” | concept_edges[4] |
| relation | DNAm age —distinct-from→ mitotic age — DNAm age is clearly different from mitotic age. — “Since epigenetic somatic errors in somatic replications appear to be readily detected as age-related changes in methylation [ 35 , 36 ], it is a plausible hypothesis that DNAm age measures the number of somatic cell replications. In other words, that it measures mitotic age (which assigns a cell copy number to every cell) [ 35 , 37 ]. While DNAm age is correlated with cell passage number (Figure 5 ) and the clock ticking rate is highest during organismal growth (Figure 6 B,C), it is clearly different from mitotic age since it tracks chronological age in non-proliferative tissue (for example, brain tissue) and assigns similar ages to both short and long lived blood cells” | concept_edges[4] |
| effect direction | cell passaging —causes→ DNAm age — increases — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | concept_edges[5] |
| note | cell passaging —causes→ DNAm age — Confirmed in three independent data sets. — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | concept_edges[5] |
| relation | cell passaging —causes→ DNAm age — Cell passaging increases DNAm age. — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | concept_edges[5] |
| weight | cell passaging —causes→ DNAm age — 0.95 — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | concept_edges[5] |
| effect direction | organismal growth —causes→ ticking rate — increases — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | concept_edges[6] |
| note | organismal growth —causes→ ticking rate — Author interpretation of the calibration curve, so below a flat measured assertion. — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | concept_edges[6] |
| relation | organismal growth —causes→ ticking rate — Organismal growth and concomitant cell division lead to a high ticking rate. — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | concept_edges[6] |
| weight | organismal growth —causes→ ticking rate — 0.8 — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | concept_edges[6] |
| audit | steroid receptors —causes→ DNA methylation age — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but it is MUTATIONS in steroid receptors that accelerate DNAm age; the edge drops 'mutations'. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concept_edges[7] |
| effect direction | steroid receptors —causes→ DNA methylation age — increases — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concept_edges[7] |
| note | steroid receptors —causes→ DNA methylation age — Direct acceleration statement with replicated breast-cancer analyses. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concept_edges[7] |
| relation | steroid receptors —causes→ DNA methylation age — Mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concept_edges[7] |
| weight | steroid receptors —causes→ DNA methylation age — 0.95 — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concept_edges[7] |
| audit | MLH1 —causes→ age acceleration — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says PROMOTER HYPERMETHYLATION of MLH1 leads to age acceleration; the endpoint names the gene, not the change. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concept_edges[8] |
| effect direction | MLH1 —causes→ age acceleration — increases — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concept_edges[8] |
| note | MLH1 —causes→ age acceleration — Direct leads-to statement with a strong measured association. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concept_edges[8] |
| relation | MLH1 —causes→ age acceleration — Promoter hypermethylation of MLH1 leads to increased age acceleration. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concept_edges[8] |
| weight | MLH1 —causes→ age acceleration — 0.95 — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concept_edges[8] |
| note | cancer tissues —contrasted-with→ cancer cell lines — Direct while-contrast, qualified for individual lines. — “I propose that DNAm age measures the cumulative work done by an epigenetic maintenance system. This EMS model of DNAm age leads to several testable model predictions that I validate using cancer data. But irrespective of the validity of the EMS model, the findings in cancer are interesting in their own right. While all cancer tissues exhibit signs of severe age acceleration, this is not necessarily the case for individual cancer cell lines. Overall, high age acceleration is associated with fewer somatic mutations in cancer tissue. Mutations in TP53 are associated with lower DNAm age. To provide a glimpse of how DNAm age can inform cancer research, I relate it to several widely used genomic aberrations in breast cancer, colorectal cancer, GBM, and AML” | concept_edges[9] |
| relation | cancer tissues —contrasted-with→ cancer cell lines — Severe age acceleration across cancer tissues is contrasted with its non-universality in individual cancer cell lines. — “I propose that DNAm age measures the cumulative work done by an epigenetic maintenance system. This EMS model of DNAm age leads to several testable model predictions that I validate using cancer data. But irrespective of the validity of the EMS model, the findings in cancer are interesting in their own right. While all cancer tissues exhibit signs of severe age acceleration, this is not necessarily the case for individual cancer cell lines. Overall, high age acceleration is associated with fewer somatic mutations in cancer tissue. Mutations in TP53 are associated with lower DNAm age. To provide a glimpse of how DNAm age can inform cancer research, I relate it to several widely used genomic aberrations in breast cancer, colorectal cancer, GBM, and AML” | concept_edges[9] |
| weight | cancer tissues —contrasted-with→ cancer cell lines — 0.9 — “I propose that DNAm age measures the cumulative work done by an epigenetic maintenance system. This EMS model of DNAm age leads to several testable model predictions that I validate using cancer data. But irrespective of the validity of the EMS model, the findings in cancer are interesting in their own right. While all cancer tissues exhibit signs of severe age acceleration, this is not necessarily the case for individual cancer cell lines. Overall, high age acceleration is associated with fewer somatic mutations in cancer tissue. Mutations in TP53 are associated with lower DNAm age. To provide a glimpse of how DNAm age can inform cancer research, I relate it to several widely used genomic aberrations in breast cancer, colorectal cancer, GBM, and AML” | concept_edges[9] |
| note | DNAm age —operationalised-as→ predicted age — Exact formal definition in the statistical method. — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | concept_edges[10] |
| relation | DNAm age —operationalised-as→ predicted age — DNAm age was defined as predicted age. — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | concept_edges[10] |
| outcome | control — held — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | controls[0] |
| outcome | control — held — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” | controls[1] |
| outcome | control — held — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | controls[2] |
| outcome | control — held — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | controls[3] |
| outcome | control — held — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | controls[4] |
| outcome | control — held — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | controls[5] |
| outcome | control — held — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | controls[6] |
| relation | DNA methylation age → cumulative effect of an epigenetic maintenance system — DNA methylation age is proposed to measure the cumulative effect of an epigenetic maintenance system. — “I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research” | unhoused_relations[0] |
| weight | DNA methylation age → cumulative effect of an epigenetic maintenance system — 0.6 — “I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research” | unhoused_relations[0] |
| relation | DNAm age → cell type composition — DNAm age does not reflect changes in cell type composition. — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | unhoused_relations[1] |
| weight | DNAm age → cell type composition — 0.95 — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | unhoused_relations[1] |
| relation | DNAm age → intrinsic changes in the methylome — DNAm age is interpreted as reflecting intrinsic methylome changes. — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | unhoused_relations[2] |
| weight | DNAm age → intrinsic changes in the methylome — 0.9 — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | unhoused_relations[2] |
| relation | DNAm age → chimpanzees — The human-trained DNAm age algorithm is applicable to chimpanzee tissues and blood. — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | unhoused_relations[3] |
| weight | DNAm age → chimpanzees — 0.95 — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | unhoused_relations[3] |
| relation | age acceleration → broad sense heritability — Age acceleration has substantial broad-sense heritability in twin data. — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | unhoused_relations[4] |
| weight | age acceleration → broad sense heritability — 0.95 — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | unhoused_relations[4] |
| relation | DNAm age → cellular senescence — DNAm age is not a marker of cellular senescence. — “Another plausible hypothesis is that DNAm age is a marker of cellular senescence. This turns out to be wrong as can be seen from the fact that DNAm age is highly related to chronological age in immortal, non-senescent cells - for example, immortalized B cells (Figure 2 T). Further, DNAm age and cell passage number are highly correlated in ES cells (Figure 5 H,J), which are also immortal” | unhoused_relations[5] |
| weight | DNAm age → cellular senescence — 0.95 — “Another plausible hypothesis is that DNAm age is a marker of cellular senescence. This turns out to be wrong as can be seen from the fact that DNAm age is highly related to chronological age in immortal, non-senescent cells - for example, immortalized B cells (Figure 2 T). Further, DNAm age and cell passage number are highly correlated in ES cells (Figure 5 H,J), which are also immortal” | unhoused_relations[5] |
| relation | maintenance methyltransferases → epigenetic maintenance system — Maintenance methyltransferases are hypothesized to play an important role in the EMS. — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | unhoused_relations[6] |
| weight | maintenance methyltransferases → epigenetic maintenance system — 0.6 — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | unhoused_relations[6] |
| relation | age acceleration → number of mutations per cancer sample — Age acceleration is inversely associated with somatic mutation count across seven cancers. — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | unhoused_relations[7] |
| weight | age acceleration → number of mutations per cancer sample — 0.9 — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | unhoused_relations[7] |
| relation | TP53 → age acceleration — TP53 mutation is associated with lower age acceleration in several cancers. — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | unhoused_relations[8] |
| weight | TP53 → age acceleration — 0.9 — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | unhoused_relations[8] |
| relation | p53 signaling → DNAm age — The results suggest p53 signaling can trigger processes that accelerate DNAm age. — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | unhoused_relations[9] |
| weight | p53 signaling → DNAm age — 0.6 — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | unhoused_relations[9] |
| relation | HER2/neu amplification → age acceleration — HER2/neu amplification has no significant relationship with age acceleration. — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | unhoused_relations[10] |
| weight | HER2/neu amplification → age acceleration — 0.95 — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | unhoused_relations[10] |
| relation | BRAF → age acceleration — BRAF mutation is associated with increased age acceleration in colorectal cancer. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | unhoused_relations[11] |
| weight | BRAF → age acceleration — 0.9 — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | unhoused_relations[11] |
| relation | K-RAS → age acceleration — K-RAS mutation is associated with decreased age acceleration. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | unhoused_relations[12] |
| weight | K-RAS → age acceleration — 0.9 — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | unhoused_relations[12] |
| relation | H3F3A → age acceleration — H3F3A mutation class is associated with age acceleration in glioblastoma. — “Interestingly, age acceleration in GBM samples is highly significantly ( P = 3.3E-7; Figure 9 J) associated with certain mutations in H3F3A , which encodes the replication-independent histone variant H3.3. These mutations are single-nucleotide variants changing lysine 27 to methionine (K27M) or changing glycine 34 to arginine (G34R) [ 40 ]. The fact that GBMs with a G34R mutation in H3F3A have a much higher age acceleration than those with a K27M mutation (Figure 9 J,L) makes sense since each H3F3A mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern and acts through a different set of genes [ 40 ]. Lysine 27 is a critical residue of histone 3 variants, and methylation at this position (H3K27me), which may be mimicked by the terminal CH3 of methionine substituted at this residue [ 40 ], is commonly associated with transcriptional repression [ 41 ] while H3K36 methylation or acetylation typically promotes gene transcription [ 42 ]. G34-mutant cells exhibit increased RNA polymerase II binding, and increased gene expression, most notably that of the oncogene MYCN [ 43 ]. Both H3F3A mutations are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup [ 44 ]. Age acceleration in GBM samples is also associated with the following genomic aberrations: TP53 mutation, ATRX mutation, chromosome 7 gain, chromosome 10 loss, CDKN2A deletion, and EGFR amplification (Figure 9 G-I). Reflecting these results for individual markers, age acceleration varies significantly ( P = 2E-7; Figure 9 L) across the GBM subtypes defined in [ 44” | unhoused_relations[13] |
| weight | H3F3A → age acceleration — 0.95 — “Interestingly, age acceleration in GBM samples is highly significantly ( P = 3.3E-7; Figure 9 J) associated with certain mutations in H3F3A , which encodes the replication-independent histone variant H3.3. These mutations are single-nucleotide variants changing lysine 27 to methionine (K27M) or changing glycine 34 to arginine (G34R) [ 40 ]. The fact that GBMs with a G34R mutation in H3F3A have a much higher age acceleration than those with a K27M mutation (Figure 9 J,L) makes sense since each H3F3A mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern and acts through a different set of genes [ 40 ]. Lysine 27 is a critical residue of histone 3 variants, and methylation at this position (H3K27me), which may be mimicked by the terminal CH3 of methionine substituted at this residue [ 40 ], is commonly associated with transcriptional repression [ 41 ] while H3K36 methylation or acetylation typically promotes gene transcription [ 42 ]. G34-mutant cells exhibit increased RNA polymerase II binding, and increased gene expression, most notably that of the oncogene MYCN [ 43 ]. Both H3F3A mutations are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup [ 44 ]. Age acceleration in GBM samples is also associated with the following genomic aberrations: TP53 mutation, ATRX mutation, chromosome 7 gain, chromosome 10 loss, CDKN2A deletion, and EGFR amplification (Figure 9 G-I). Reflecting these results for individual markers, age acceleration varies significantly ( P = 2E-7; Figure 9 L) across the GBM subtypes defined in [ 44” | unhoused_relations[13] |
| relation | weighted average of the 353 clock CpGs → DNAm age — The weighted CpG average is transformed to DNAm age by a calibration function. — “Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylation data from healthy tissues, cancer tissues, and cancer cell lines. The healthy tissue data allowed me to develop a multi-tissue predictor of age (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate” | unhoused_relations[14] |
| no type for this | DNA methylation age → cumulative effect of an epigenetic maintenance system — DNA methylation age is proposed to measure the cumulative effect of an epigenetic maintenance system. — “I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research” | unhoused_relations[0] |
| no type for this | DNAm age → cell type composition — DNAm age does not reflect changes in cell type composition. — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | unhoused_relations[1] |
| no type for this | DNAm age → intrinsic changes in the methylome — DNAm age is interpreted as reflecting intrinsic methylome changes. — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | unhoused_relations[2] |
| no type for this | DNAm age → chimpanzees — The human-trained DNAm age algorithm is applicable to chimpanzee tissues and blood. — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | unhoused_relations[3] |
| no type for this | age acceleration → broad sense heritability — Age acceleration has substantial broad-sense heritability in twin data. — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | unhoused_relations[4] |
| no type for this | DNAm age → cellular senescence — DNAm age is not a marker of cellular senescence. — “Another plausible hypothesis is that DNAm age is a marker of cellular senescence. This turns out to be wrong as can be seen from the fact that DNAm age is highly related to chronological age in immortal, non-senescent cells - for example, immortalized B cells (Figure 2 T). Further, DNAm age and cell passage number are highly correlated in ES cells (Figure 5 H,J), which are also immortal” | unhoused_relations[5] |
| no type for this | maintenance methyltransferases → epigenetic maintenance system — Maintenance methyltransferases are hypothesized to play an important role in the EMS. — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | unhoused_relations[6] |
| no type for this | age acceleration → number of mutations per cancer sample — Age acceleration is inversely associated with somatic mutation count across seven cancers. — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | unhoused_relations[7] |
| no type for this | TP53 → age acceleration — TP53 mutation is associated with lower age acceleration in several cancers. — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | unhoused_relations[8] |
| no type for this | p53 signaling → DNAm age — The results suggest p53 signaling can trigger processes that accelerate DNAm age. — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | unhoused_relations[9] |
| no type for this | HER2/neu amplification → age acceleration — HER2/neu amplification has no significant relationship with age acceleration. — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | unhoused_relations[10] |
| no type for this | BRAF → age acceleration — BRAF mutation is associated with increased age acceleration in colorectal cancer. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | unhoused_relations[11] |
| no type for this | K-RAS → age acceleration — K-RAS mutation is associated with decreased age acceleration. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | unhoused_relations[12] |
| no type for this | H3F3A → age acceleration — H3F3A mutation class is associated with age acceleration in glioblastoma. — “Interestingly, age acceleration in GBM samples is highly significantly ( P = 3.3E-7; Figure 9 J) associated with certain mutations in H3F3A , which encodes the replication-independent histone variant H3.3. These mutations are single-nucleotide variants changing lysine 27 to methionine (K27M) or changing glycine 34 to arginine (G34R) [ 40 ]. The fact that GBMs with a G34R mutation in H3F3A have a much higher age acceleration than those with a K27M mutation (Figure 9 J,L) makes sense since each H3F3A mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern and acts through a different set of genes [ 40 ]. Lysine 27 is a critical residue of histone 3 variants, and methylation at this position (H3K27me), which may be mimicked by the terminal CH3 of methionine substituted at this residue [ 40 ], is commonly associated with transcriptional repression [ 41 ] while H3K36 methylation or acetylation typically promotes gene transcription [ 42 ]. G34-mutant cells exhibit increased RNA polymerase II binding, and increased gene expression, most notably that of the oncogene MYCN [ 43 ]. Both H3F3A mutations are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup [ 44 ]. Age acceleration in GBM samples is also associated with the following genomic aberrations: TP53 mutation, ATRX mutation, chromosome 7 gain, chromosome 10 loss, CDKN2A deletion, and EGFR amplification (Figure 9 G-I). Reflecting these results for individual markers, age acceleration varies significantly ( P = 2E-7; Figure 9 L) across the GBM subtypes defined in [ 44” | unhoused_relations[13] |
| no type for this | weighted average of the 353 clock CpGs → DNAm age — The weighted CpG average is transformed to DNAm age by a calibration function. — “Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylation data from healthy tissues, cancer tissues, and cancer cell lines. The healthy tissue data allowed me to develop a multi-tissue predictor of age (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate” | unhoused_relations[14] |
| limitation | Age correlation cannot test calibration, is undefined when all subjects have one age, and depends strongly on age spread. — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | limitations[0] |
| limitation | DNAm age is poorly calibrated in breast, endometrium, dermal fibroblast, skeletal muscle, and heart tissue. — “DNAm age is poorly calibrated (that is, leads to a high error) in breast tissue (Figure 2 H), uterine endometrium (Figure 2 S), dermal fibroblasts (Figure 1 M), skeletal muscle tissue (Figure 2 P), and heart tissue (Figures 1 Q and 2 L). I can only speculate on the biological reasons that could explain the poor calibration. The high error in breast tissue (Additional file 6 ) may reflect hormonal effects or cancer field effects in this normal adjacent tissue from cancer samples. Note that the lowest error (8.9 years) in breast tissue is observed in normal breast tissue, that is, in samples from women without cancer (training data set 14; Additional file 6 ). The menstrual cycle and concomitant increases in cell proliferation may explain the high error in uterine endometrium. Myosatellite cells may effectively rejuvenate the DNAm age of skeletal muscle tissue. Similarly, the recruitment of stem cells into cardiomyocytes for new cardiac muscle formation could explain why human heart tissue tends to have a low DNAm age. Carefully designed studies will be needed to test these hypotheses” | limitations[1] |
| limitation | Processing, extraction, storage, batch, and chip effects can alter predictive accuracy. — “A host of technical artifacts could explain differences in predictive accuracy: for example, variations in sample processing, DNA extraction, DNA storage effects, batch effects, and chip effects” | limitations[2] |
| limitation | The within-person multi-tissue analysis had only three subjects and mostly one sample per tissue. — “A more interesting analysis is to compare the DNAm ages of tissues collected from the same subjects. DNAm age does not change significantly across different brain regions (temporal cortex, pons, frontal cortex, cerebellum) from the same subjects (Additional file 5 K,L). I could only find three human subjects from whom many tissues had been profiled (Figure 3 E-G). Although the limited sample sizes per tissue (mostly one sample per tissue per subject) did not allow for rigorous testing, these data can be used to estimate the coefficient of variation of DNAm age (that is, the standard deviation divided by the mean). Note that the coefficient of variations for the first and second adult male are relatively low (0.12 and 0.15 in Figure 3 E,F) even though the analysis involved several tissues that were not part of the training data - for example, jejunum, penis, pancreas, esophagus, spleen, pancreas, lymph node, diaphragm. The coefficient of variation in the adult female (Figure 3 G) is relatively high (0.21), which reflects the fact that her breast tissue shows signs of substantial age acceleration” | limitations[3] |
| limitation | Predictor performance is diminished in gorillas and no significant age correlation was found in sperm. — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | limitations[4] |
| limitation | DNAm age cannot be interpreted as mitotic age despite its association with cell passage number. — “Since epigenetic somatic errors in somatic replications appear to be readily detected as age-related changes in methylation [ 35 , 36 ], it is a plausible hypothesis that DNAm age measures the number of somatic cell replications. In other words, that it measures mitotic age (which assigns a cell copy number to every cell) [ 35 , 37 ]. While DNAm age is correlated with cell passage number (Figure 5 ) and the clock ticking rate is highest during organismal growth (Figure 6 B,C), it is clearly different from mitotic age since it tracks chronological age in non-proliferative tissue (for example, brain tissue) and assigns similar ages to both short and long lived blood cells” | limitations[5] |
| limitation | Cancer mutation-count analysis can be confounded by cancer and tissue type. — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | limitations[6] |
| limitation | Cancer cell-line DNAm age does not generally correlate with the donor patient's chronological age. — “Using seven publicly available cell line data sets (Additional files 12 and 13 ), I was able to estimate the DNAm age of 59 different cancer cell lines (from bladder, breast, gliomas, head/neck, leukemia, and osteosarcoma). Across all cell lines, DNAm age does not have a significant correlation with the chronological age of the patient from whom the cancer cell line was derived (Additional file 18 B). However, a marginally significant age correlation can be observed across osteosarcoma cell lines (cor = 0.41, P = 0.08; Additional file 18 C). Overall, DNAm age acceleration varies greatly across the cancer lines lines (Additional files 18 A and 19 ): the highest values can be observed for AML cell lines (KG1A, 182 years; HL-60, 177 years); the lowest values for head/neck squamous cell carcinoma cell line (UPCI SCC47, 6 years) and two breast cancer cell lines (SK-BR-3, 8 years; MDA-MB-468, 11 years). It will be interesting to test whether DNAm age relates to other characteristics of cancer cell lines” | limitations[7] |
| limitation | It is unknown whether accessible tissues can serve as surrogates for inaccessible organs. — “DNAm age is arguably a promising marker for studying human development, aging, and cancer. It may become a useful surrogate marker for evaluating rejuvenation therapies. The most salient feature of DNAm age is its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease (for example, cancer). It remains to be seen whether the DNAm age of easily accessible fluids/tissues (for example, saliva, buccal cells, blood, skin) can serve as a surrogate marker for inaccessible tissues (for example, brain, kidney, liver). It is noteworthy that DNAm age is applicable to chimpanzee tissues. Given the high heritability of age acceleration in young subjects, I expect that age acceleration will mainly be a relevant measure in older subjects. Using a relatively small data set, I did not find any evidence that a premature aging disease (progeria) is associated with accelerated DNAm age (Figure 2 T). In Additional file 2 , I discuss whether DNAm age fulfils the biomarker criteria developed by the American Federation for Aging Research” | limitations[8] |
| limitation | The progeria analysis used a relatively small data set and found no evidence of accelerated DNAm age. — “DNAm age is arguably a promising marker for studying human development, aging, and cancer. It may become a useful surrogate marker for evaluating rejuvenation therapies. The most salient feature of DNAm age is its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease (for example, cancer). It remains to be seen whether the DNAm age of easily accessible fluids/tissues (for example, saliva, buccal cells, blood, skin) can serve as a surrogate marker for inaccessible tissues (for example, brain, kidney, liver). It is noteworthy that DNAm age is applicable to chimpanzee tissues. Given the high heritability of age acceleration in young subjects, I expect that age acceleration will mainly be a relevant measure in older subjects. Using a relatively small data set, I did not find any evidence that a premature aging disease (progeria) is associated with accelerated DNAm age (Figure 2 T). In Additional file 2 , I discuss whether DNAm age fulfils the biomarker criteria developed by the American Federation for Aging Research” | limitations[9] |
| limitation | Future work must determine whether DNAm age is only a marker or an effector of aging. — “Future research will need to clarify whether DNAm age is only a marker of aging or relates to an effector of aging. In conclusion, the epigenetic clock described here is likely to become a valuable addition to the telomere clock” | limitations[10] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | The multi-tissue predictor was developed from roughly 8,000 samples across 82 arrays and 51 healthy tissues or cell types. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.findings[0] |
| finding | DNAm age is near zero in embryonic and induced pluripotent stem cells. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.findings[1] |
| finding | DNAm age correlates with cell passage number and yields a heritable acceleration measure. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.findings[2] |
| finding | All 20 considered cancer types showed significant acceleration averaging about 36 years. — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.findings[3] |
| finding | Held-out test performance reached age correlation 0.96 and median error 3.6 years. — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” | content_tags.findings[4] |
| finding | DNAm age performs in heterogeneous tissues and individual immune-cell types. — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” | content_tags.findings[5] |
| finding | Blood-cell results favor intrinsic methylome change over cell-composition change. — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | content_tags.findings[6] |
| finding | DNAm ages are aligned across corresponding human and chimpanzee tissues, with diminished performance in gorillas. — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | content_tags.findings[7] |
| finding | iPS cells are significantly younger than their corresponding primary cells in three independent data sets. — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | content_tags.findings[8] |
| finding | Cell passaging increases DNAm age in three independent data sets. — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | content_tags.findings[9] |
| finding | The 110-CpG shrunken predictor retained correlation 0.95 with errors near four years. — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | content_tags.findings[10] |
| finding | Positive and negative clock CpGs have different tissue variance, shore, Polycomb, promoter, and enhancer profiles. — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | content_tags.findings[11] |
| finding | Broad-sense acceleration heritability was estimated at 100 percent in newborns and 39 percent in older subjects. — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | content_tags.findings[12] |
| finding | Age effects on methylation showed little overlap with age effects on messenger-RNA abundance. — “Since DNA methylation is an important epigenetic mechanism for regulating gene expression levels (messenger RNA abundance), it is natural to wonder how age-related DNAm changes relate to those observed in gene expression levels. As described in Additional file 11 , I find very little overlap. Further, I do not find that age effects on DNAm levels affect genes known to be differentially expressed between naive CD8 T cells and CD8 memory cells (Additional file 11 ). These non-significant results reflect the fact that the relationship between DNAm levels and expression levels is complex” | content_tags.findings[13] |
| finding | The epigenetic clock has a high nonlinear tick rate before adulthood and a constant linear rate afterward. — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | content_tags.findings[14] |
| finding | DNAm age is empirically distinct from both mitotic age and cellular senescence. — “Since epigenetic somatic errors in somatic replications appear to be readily detected as age-related changes in methylation [ 35 , 36 ], it is a plausible hypothesis that DNAm age measures the number of somatic cell replications. In other words, that it measures mitotic age (which assigns a cell copy number to every cell) [ 35 , 37 ]. While DNAm age is correlated with cell passage number (Figure 5 ) and the clock ticking rate is highest during organismal growth (Figure 6 B,C), it is clearly different from mitotic age since it tracks chronological age in non-proliferative tissue (for example, brain tissue) and assigns similar ages to both short and long lived blood cells” | content_tags.findings[15] |
| finding | Each cancer tissue showed acceleration, but grade and stage had weak relationships in most cancers. — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | content_tags.findings[16] |
| finding | Age acceleration was inversely associated with somatic mutation count in seven cancer types. — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | content_tags.findings[17] |
| finding | TP53 mutation was associated with lower acceleration in five cancer types, with one GBM exception. — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | content_tags.findings[18] |
| finding | Mutated estrogen or progesterone receptor status marked higher breast-cancer acceleration in four data sets. — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | content_tags.findings[19] |
| finding | MLH1 promoter hypermethylation had the strongest colorectal acceleration increase reported in that analysis. — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | content_tags.findings[20] |
| finding | H3F3A mutation class strongly stratified glioblastoma age acceleration. — “Interestingly, age acceleration in GBM samples is highly significantly ( P = 3.3E-7; Figure 9 J) associated with certain mutations in H3F3A , which encodes the replication-independent histone variant H3.3. These mutations are single-nucleotide variants changing lysine 27 to methionine (K27M) or changing glycine 34 to arginine (G34R) [ 40 ]. The fact that GBMs with a G34R mutation in H3F3A have a much higher age acceleration than those with a K27M mutation (Figure 9 J,L) makes sense since each H3F3A mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern and acts through a different set of genes [ 40 ]. Lysine 27 is a critical residue of histone 3 variants, and methylation at this position (H3K27me), which may be mimicked by the terminal CH3 of methionine substituted at this residue [ 40 ], is commonly associated with transcriptional repression [ 41 ] while H3K36 methylation or acetylation typically promotes gene transcription [ 42 ]. G34-mutant cells exhibit increased RNA polymerase II binding, and increased gene expression, most notably that of the oncogene MYCN [ 43 ]. Both H3F3A mutations are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup [ 44 ]. Age acceleration in GBM samples is also associated with the following genomic aberrations: TP53 mutation, ATRX mutation, chromosome 7 gain, chromosome 10 loss, CDKN2A deletion, and EGFR amplification (Figure 9 G-I). Reflecting these results for individual markers, age acceleration varies significantly ( P = 2E-7; Figure 9 L) across the GBM subtypes defined in [ 44” | content_tags.findings[21] |
| finding | DNAm age varied greatly across 59 cancer cell lines and did not track donor age overall. — “Using seven publicly available cell line data sets (Additional files 12 and 13 ), I was able to estimate the DNAm age of 59 different cancer cell lines (from bladder, breast, gliomas, head/neck, leukemia, and osteosarcoma). Across all cell lines, DNAm age does not have a significant correlation with the chronological age of the patient from whom the cancer cell line was derived (Additional file 18 B). However, a marginally significant age correlation can be observed across osteosarcoma cell lines (cor = 0.41, P = 0.08; Additional file 18 C). Overall, DNAm age acceleration varies greatly across the cancer lines lines (Additional files 18 A and 19 ): the highest values can be observed for AML cell lines (KG1A, 182 years; HL-60, 177 years); the lowest values for head/neck squamous cell carcinoma cell line (UPCI SCC47, 6 years) and two breast cancer cell lines (SK-BR-3, 8 years; MDA-MB-468, 11 years). It will be interesting to test whether DNAm age relates to other characteristics of cancer cell lines” | content_tags.findings[22] |
| quantity | 8,000 samples — approximate predictor-development sample count — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.quantities[0] |
| quantity | 82 datasets — Illumina methylation data sets used for healthy-tissue development and evaluation — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.quantities[1] |
| quantity | 51 healthy tissues and cell types — healthy biological sources represented — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | content_tags.quantities[2] |
| quantity | 353 — CpG sites selected for the full clock — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | content_tags.quantities[3] |
| quantity | 0.96 — held-out test age correlation — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” | content_tags.quantities[4] |
| quantity | 3.6 years — held-out test median absolute error — “According to these three accuracy measures, the multi-tissue age predictor performs remarkably well in most tissues and cell types. Although its high accuracy in the training data (age correlation 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlation = 0.96, error = 3.6 years; Figure 2 ) in the test data is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T)” | content_tags.quantities[5] |
| quantity | 110 CpGs — sites in the shrunken predictor — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | content_tags.quantities[6] |
| quantity | 193 — clock CpGs positively correlated with age — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | content_tags.quantities[7] |
| quantity | 160 — clock CpGs negatively correlated with age — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | content_tags.quantities[8] |
| quantity | 100% — broad-sense age-acceleration heritability in newborns — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | content_tags.quantities[9] |
| quantity | 39% — broad-sense age-acceleration heritability in older subjects — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | content_tags.quantities[10] |
| quantity | 5,826 cancer samples — main-text cancer application collection — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | content_tags.quantities[11] |
| quantity | 32 cancer data sets — cancer tissue collections analyzed — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | content_tags.quantities[12] |
| quantity | 36.2 years — average acceleration across cancer tissues — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | content_tags.quantities[13] |
| quantity | 59 — cancer cell lines assigned DNAm ages — “Using seven publicly available cell line data sets (Additional files 12 and 13 ), I was able to estimate the DNAm age of 59 different cancer cell lines (from bladder, breast, gliomas, head/neck, leukemia, and osteosarcoma). Across all cell lines, DNAm age does not have a significant correlation with the chronological age of the patient from whom the cancer cell line was derived (Additional file 18 B). However, a marginally significant age correlation can be observed across osteosarcoma cell lines (cor = 0.41, P = 0.08; Additional file 18 C). Overall, DNAm age acceleration varies greatly across the cancer lines lines (Additional files 18 A and 19 ): the highest values can be observed for AML cell lines (KG1A, 182 years; HL-60, 177 years); the lowest values for head/neck squamous cell carcinoma cell line (UPCI SCC47, 6 years) and two breast cancer cell lines (SK-BR-3, 8 years; MDA-MB-468, 11 years). It will be interesting to test whether DNAm age relates to other characteristics of cancer cell lines” | content_tags.quantities[14] |
| quantity | 21,369 CpG probes — shared, low-missing probes entered into penalized regression — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | content_tags.quantities[15] |
| quantity | alpha 0.5 — elastic-net mixing parameter — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | content_tags.quantities[16] |
| quantity | lambda 0.0226 — cross-validated penalization parameter — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | content_tags.quantities[17] |
| variable | chronological age (prediction target and comparator) — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.variables[0] |
| variable | DNAm age (predicted outcome and biomarker) — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.variables[1] |
| variable | age correlation (ranking-oriented predictive-accuracy outcome) — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.variables[2] |
| variable | median error (calibration-oriented predictive-accuracy outcome) — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.variables[3] |
| variable | age acceleration (DNAm-minus-chronological age outcome) — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | content_tags.variables[4] |
| variable | standard deviation (age-spread determinant of the correlation metric) — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | content_tags.variables[5] |
| variable | cell passage number (culture-exposure predictor) — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | content_tags.variables[6] |
| variable | tissue type (heterogeneity and adjustment variable) — “To measure tissue effects in the training data (Additional file 8 ), I used analysis of variance (ANOVA) to calculate an F statistic as follows. First, a multivariate regression model was used to regress each CpG (dependent variable) on age and tissue type. The analysis adjusted for age since the different data sets have very different mean ages (Additional file 1 ). Next, ANOVA based on the multivariate regression model was used to calculate an F statistic, F.tissueTraining , for measuring the tissue effect in the training data. This F statistic measures the tissue effect after adjusting for age in the training data sets. I did not translate the F statistic into a corresponding P -value since the latter turned out to be extremely significant for most CpGs. Additional file 8 D shows that F.tissueTraining is highly correlated with an independent measure of tissue variance” | content_tags.variables[7] |
| variable | tumor morphology (grade and stage predictors of acceleration) — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | content_tags.variables[8] |
| variable | number of mutations per cancer sample (tumor mutation-burden correlate) — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | content_tags.variables[9] |
| variable | TP53 mutation (binary genomic predictor of acceleration) — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | content_tags.variables[10] |
| definition | DNAm age — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | content_tags.definitions[0] |
| definition | age correlation — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.definitions[1] |
| definition | median error — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.definitions[2] |
| definition | average age acceleration — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | content_tags.definitions[3] |
| definition | clock CpGs — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | content_tags.definitions[4] |
| definition | LOOCV — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | content_tags.definitions[5] |
| definition | coefficient of variation — “A more interesting analysis is to compare the DNAm ages of tissues collected from the same subjects. DNAm age does not change significantly across different brain regions (temporal cortex, pons, frontal cortex, cerebellum) from the same subjects (Additional file 5 K,L). I could only find three human subjects from whom many tissues had been profiled (Figure 3 E-G). Although the limited sample sizes per tissue (mostly one sample per tissue per subject) did not allow for rigorous testing, these data can be used to estimate the coefficient of variation of DNAm age (that is, the standard deviation divided by the mean). Note that the coefficient of variations for the first and second adult male are relatively low (0.12 and 0.15 in Figure 3 E,F) even though the analysis involved several tissues that were not part of the training data - for example, jejunum, penis, pancreas, esophagus, spleen, pancreas, lymph node, diaphragm. The coefficient of variation in the adult female (Figure 3 G) is relatively high (0.21), which reflects the fact that her breast tissue shows signs of substantial age acceleration” | content_tags.definitions[6] |
| definition | broad-sense heritability — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | content_tags.definitions[7] |
| definition | ticking rate — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | content_tags.definitions[8] |
| definition | EMS model — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | content_tags.definitions[9] |
| definition | CpG island methylator phenotype — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | content_tags.definitions[10] |
| concept | DNA methylation levels — not defined in this paper's own words; methylation measurements used to predict age — “It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure” | concepts[0] |
| concept | age prediction — not defined in this paper's own words; predicted biological or chronological-age estimate — “It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure” | concepts[1] |
| concept | multi-tissue predictor of age — not defined in this paper's own words; penalized-regression predictor applicable across tissues — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[2] |
| concept | DNA methylation age — not defined in this paper's own words; age estimated from the multi-tissue DNA-methylation predictor — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[3] |
| concept | DNAm age — not defined in this paper's own words; abbreviation for DNA methylation age — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concepts[4] |
| concept | embryonic — not defined in this paper's own words; embryonic stem-cell state with near-zero DNAm age — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[5] |
| concept | induced pluripotent stem cells — not defined in this paper's own words; reprogrammed cells whose clock is near zero — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[6] |
| concept | cell passage number — not defined in this paper's own words; number of culture passages associated with DNAm age — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[7] |
| concept | age acceleration — not defined in this paper's own words; DNAm age deviation from chronological age — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[8] |
| concept | chimpanzee tissues — not defined in this paper's own words; nonhuman primate tissues to which the predictor applies — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[9] |
| concept | cancer samples — not defined in this paper's own words; tumor methylation samples used for application analyses — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[10] |
| concept | somatic mutations — not defined in this paper's own words; tumor mutations inversely associated with age acceleration — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[11] |
| concept | TP53 — not defined in this paper's own words; tumor-suppressor gene whose mutations are associated with lower age acceleration — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[12] |
| concept | steroid receptors — not defined in this paper's own words; breast-cancer receptors whose mutations accompany accelerated DNAm age — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[13] |
| concept | 353 CpG sites — not defined in this paper's own words; methylation sites forming the predictor clock — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[14] |
| concept | aging clock — not defined in this paper's own words; the 353-CpG epigenetic age predictor — “I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance” | concepts[15] |
| concept | cumulative effect of an epigenetic maintenance system — not defined in this paper's own words; proposed biological quantity measured by DNA methylation age — “I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research” | concepts[16] |
| concept | epigenetic maintenance system — not defined in this paper's own words; hypothesized system whose cumulative work is measured by DNAm age — “I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research” | concepts[17] |
| concept | cytosine-5 methylation — not defined in this paper's own words; methylation at cytosine within CpG dinucleotides — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concepts[18] |
| concept | CpG dinucleotides — not defined in this paper's own words; cytosine-phosphate-guanine sites measured by the arrays — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concepts[19] |
| concept | DNA methylation — not defined in this paper's own words; epigenetic modification also called cytosine-5 methylation in CpGs — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concepts[20] |
| concept | age-dependent CpG signatures — not defined in this paper's own words; methylation signatures that generalize over covariates and platforms — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concepts[21] |
| concept | multi-tissue age predictor — not defined in this paper's own words; single predictor intended for many tissues — “An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylation within CpG dinucleotides (also known as DNA methylation). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue types [ 16 - 18 ] and many age-related changes depend on tissue type [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signatures can be defined independently of sex, tissue type, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue type using a single predictor. Here I use an unprecedented collection of publicly available DNA methylation data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictor. Its age prediction, referred to as DNAm age, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm age and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm age to cancer tissues and cancer cell lines” | concepts[22] |
| concept | Illumina 27K — not defined in this paper's own words; methylation-array platform included in the combined data — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | concepts[23] |
| concept | Illumina 450K — not defined in this paper's own words; methylation-array platform included in the combined data — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | concepts[24] |
| concept | cancer tissues — not defined in this paper's own words; tissues excluded from the initial healthy-data predictor construction — “I assembled a large DNA methylation data set by combining publicly available individual data sets measured on the Illumina 27K or Illumina 450K array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levels in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissues were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levels [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm age of embryonic stem and iPS cells. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training data were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test data, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450K platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed” | concepts[25] |
| concept | training data — not defined in this paper's own words; data used to fit the predictor — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | concepts[26] |
| concept | test data — not defined in this paper's own words; held-out data used for unbiased validation — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | concepts[27] |
| concept | penalized regression model — not defined in this paper's own words; elastic-net model used to select clock CpGs — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | concepts[28] |
| concept | elastic net — not defined in this paper's own words; penalized regression specification for predictor construction — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | concepts[29] |
| concept | clock CpGs — not defined in this paper's own words; 353 CpGs selected by elastic net and weighted into the clock — “To ensure an unbiased validation in the test data, I only used the training data to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological age was regressed on the CpGs using a penalized regression model (elastic net). The elastic net regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGs since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types” | concepts[30] |
| concept | age correlation — not defined in this paper's own words; Pearson correlation between predicted and chronological age — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concepts[31] |
| concept | Pearson correlation coefficient — not defined in this paper's own words; coefficient operationalizing age correlation — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concepts[32] |
| concept | chronological age — not defined in this paper's own words; calendar age used as prediction target and comparator — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concepts[33] |
| concept | median absolute difference — not defined in this paper's own words; error measure between DNAm and chronological age — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concepts[34] |
| concept | Average age acceleration — not defined in this paper's own words; average DNAm-minus-chronological age difference — “I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlation’, is the Pearson correlation coefficient between DNAm age (predicted age) and chronological age. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological age (for example, cord blood samples from newborns), and it strongly depends on the standard deviation of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute difference between DNAm age and chronological age. Thus, a test set error of 3.6 years indicates that DNAm age differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm age is poorly calibrated. 'Average age acceleration’, defined by the average difference between DNAm age and chronological age, can be used to determine whether the DNAm age of a given tissue is consistently higher (or lower) than expected” | concepts[35] |
| concept | cell type composition — not defined in this paper's own words; mixture explanation rejected for blood DNAm age — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | concepts[36] |
| concept | intrinsic changes in the methylome — not defined in this paper's own words; within-cell methylome change interpretation — “A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm ages. DNAm age does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm age does not reflect changes in cell type composition but rather intrinsic changes in the methylome. While I expect significant correlations between DNAm age and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm age but rather a confounding effect due to chronological age. This conclusion is also corroborated by the finding that DNAm age is highly related to chronological age in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions” | concepts[37] |
| concept | leave-one-data-set-out cross-validation — not defined in this paper's own words; data-set-level validation that refits without each held-out set — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | concepts[38] |
| concept | LOOCV — not defined in this paper's own words; abbreviation for leave-one-data-set-out cross-validation — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | concepts[39] |
| concept | standard deviation — not defined in this paper's own words; spread of chronological age affecting correlation — “The second approach, referred to as leave-one-data-set-out cross-validation (LOOCV) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm age for each data set (considered as test data set) separately by fitting a separate multi-tissue age predictor to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCV’ and 'Error LOOCV’, respectively. Data sets differ greatly with respect to the median chronological age and the standard deviation, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviation = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviation is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCV estimate of the age correlation. In contrast, the sample size of the data set has no significant relationship with the age correlation” | concepts[40] |
| concept | technical artifacts — not defined in this paper's own words; processing, storage, batch, and chip effects affecting accuracy — “A host of technical artifacts could explain differences in predictive accuracy: for example, variations in sample processing, DNA extraction, DNA storage effects, batch effects, and chip effects” | concepts[41] |
| concept | coefficient of variation of DNAm age — not defined in this paper's own words; within-person cross-tissue variability measure — “A more interesting analysis is to compare the DNAm ages of tissues collected from the same subjects. DNAm age does not change significantly across different brain regions (temporal cortex, pons, frontal cortex, cerebellum) from the same subjects (Additional file 5 K,L). I could only find three human subjects from whom many tissues had been profiled (Figure 3 E-G). Although the limited sample sizes per tissue (mostly one sample per tissue per subject) did not allow for rigorous testing, these data can be used to estimate the coefficient of variation of DNAm age (that is, the standard deviation divided by the mean). Note that the coefficient of variations for the first and second adult male are relatively low (0.12 and 0.15 in Figure 3 E,F) even though the analysis involved several tissues that were not part of the training data - for example, jejunum, penis, pancreas, esophagus, spleen, pancreas, lymph node, diaphragm. The coefficient of variation in the adult female (Figure 3 G) is relatively high (0.21), which reflects the fact that her breast tissue shows signs of substantial age acceleration” | concepts[42] |
| concept | chimpanzees — not defined in this paper's own words; Pan tissues and blood evaluated for cross-primate applicability — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | concepts[43] |
| concept | gorillas — not defined in this paper's own words; more distant primate with diminished predictor performance — “It is important to study whether there are inter-primate differences when it comes to DNAm age. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm age estimation algorithm to Illumina DNA methylation data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm age of heart, liver, and kidney tissue from chimpanzees ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm age of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzee) is highly correlated with chronological age (Figure 4 C). While DNAm age is applicable to chimpanzees, its performance appears to be diminished in gorillas (Figure 4 F), which may reflect the larger evolutionary distance” | concepts[44] |
| concept | iPS cells — not defined in this paper's own words; induced pluripotent stem cells reprogrammed from non-pluripotent cells — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | concepts[45] |
| concept | ES cells — not defined in this paper's own words; embryonic stem cells with near-zero DNAm age — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | concepts[46] |
| concept | primary cells — not defined in this paper's own words; source cells older than reprogrammed iPS cells — “The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonic stem cells have a DNAm age close to zero in five different data sets (Figure 5 ). iPS cells are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cells are similar to embryonic stem (ES) cells, I hypothesized that the DNAm age of iPS cells should be significantly younger than that of corresponding primary cells. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm age could be detected between ES cells and iPS cells” | concepts[47] |
| concept | cell passaging — not defined in this paper's own words; repeated culture splitting associated with increased DNAm age — “Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passaging (also known as splitting cells) increases DNAm age. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage number and DNAm age can also be observed when restricting the analysis to iPS cells (Figure 5 G) or when restricting the analysis to ES cells” | concepts[48] |
| concept | shrunken — not defined in this paper's own words; 110-CpG reduced form of the multi-tissue predictor — “As shown in Additional file 2 , the proposed multi-tissue predictor of age outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictor lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunken’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGs) is highly accurate in the training data (cor = 0.95, error = 4 years) and test data (cor = 0.95, error = 4.2 years)” | concepts[49] |
| concept | positively related CpGs — not defined in this paper's own words; 193 clock CpGs that hypermethylate with age — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | concepts[50] |
| concept | negatively related CpGs — not defined in this paper's own words; 160 clock CpGs that hypomethylate with age — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | concepts[51] |
| concept | CpG shores — not defined in this paper's own words; regions enriched for negatively related clock CpGs — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | concepts[52] |
| concept | Polycomb-group target genes — not defined in this paper's own words; targets enriched near positively related CpGs — “The 353 clock CpGs can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylation data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levels of the 193 positively related CpGs vary less across different tissues, those of the 160 negatively related CpGs vary more across tissues than the remaining CpGs on the Illumina 27K array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue type. The clock CpGs include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training data sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shores ( P = 9.3E-6; Additional file 9 K). Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGs are over-represented near Polycomb-group target genes (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship” | concepts[53] |
| concept | poised promoters — not defined in this paper's own words; chromatin state enriched for positively related CpGs — “Chromatin state profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. It provides a systematic means of detecting cis -regulatory elements (given the central role of chromatin in mediating regulatory signals and controlling DNA access) and can be used for characterizing non-coding portions of the genome, which contribute to cellular phenotypes [ 29 ]. While individual histone modifications are associated with regulator binding, transcriptional initiation, enhancer activity, combinations of chromatin modifications can provide even more precise insight into chromatin state [ 29 ]. Ernst et al . [ 29 ] distinguish six broad classes of chromatin states, referred to as promoter, enhancer, insulator, transcribed, repressed, and inactive states. Within them, active, weak and poised promoters (states 1 to 3) differ in expression levels, while strong and weak enhancers (states 4 to 7) differ in expression of proximal genes. The 193 positively related CpGs are more likely to be in poised promoters (chromatin state 3 regions; Additional file 9 B) while the 160 negatively related CpGs are more likely to be in either weak promoters (chromatin state 2; Additional file 9 D) or strong enhancers (chromatin state 4; Additional file 9 E)” | concepts[54] |
| concept | weak promoters — not defined in this paper's own words; chromatin state enriched for negatively related CpGs — “Chromatin state profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. It provides a systematic means of detecting cis -regulatory elements (given the central role of chromatin in mediating regulatory signals and controlling DNA access) and can be used for characterizing non-coding portions of the genome, which contribute to cellular phenotypes [ 29 ]. While individual histone modifications are associated with regulator binding, transcriptional initiation, enhancer activity, combinations of chromatin modifications can provide even more precise insight into chromatin state [ 29 ]. Ernst et al . [ 29 ] distinguish six broad classes of chromatin states, referred to as promoter, enhancer, insulator, transcribed, repressed, and inactive states. Within them, active, weak and poised promoters (states 1 to 3) differ in expression levels, while strong and weak enhancers (states 4 to 7) differ in expression of proximal genes. The 193 positively related CpGs are more likely to be in poised promoters (chromatin state 3 regions; Additional file 9 B) while the 160 negatively related CpGs are more likely to be in either weak promoters (chromatin state 2; Additional file 9 D) or strong enhancers (chromatin state 4; Additional file 9 E)” | concepts[55] |
| concept | strong enhancers — not defined in this paper's own words; chromatin state enriched for negatively related CpGs — “Chromatin state profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. It provides a systematic means of detecting cis -regulatory elements (given the central role of chromatin in mediating regulatory signals and controlling DNA access) and can be used for characterizing non-coding portions of the genome, which contribute to cellular phenotypes [ 29 ]. While individual histone modifications are associated with regulator binding, transcriptional initiation, enhancer activity, combinations of chromatin modifications can provide even more precise insight into chromatin state [ 29 ]. Ernst et al . [ 29 ] distinguish six broad classes of chromatin states, referred to as promoter, enhancer, insulator, transcribed, repressed, and inactive states. Within them, active, weak and poised promoters (states 1 to 3) differ in expression levels, while strong and weak enhancers (states 4 to 7) differ in expression of proximal genes. The 193 positively related CpGs are more likely to be in poised promoters (chromatin state 3 regions; Additional file 9 B) while the 160 negatively related CpGs are more likely to be in either weak promoters (chromatin state 2; Additional file 9 D) or strong enhancers (chromatin state 4; Additional file 9 E)” | concepts[56] |
| concept | broad sense heritability — not defined in this paper's own words; heritability of age acceleration estimated from twins — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | concepts[57] |
| concept | Falconer — not defined in this paper's own words; formula used to estimate broad-sense heritability — “Several authors have found that DNA methylation levels are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age acceleration (here defined as difference between DNAm age and chronological age) is heritable as well. I estimated the broad sense heritability of age acceleration using Falconer’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins. As detailed in Additional file 10 , the broad sense heritability of age acceleration is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life” | concepts[58] |
| concept | gene expression levels — not defined in this paper's own words; messenger-RNA abundance compared with methylation age effects — “Since DNA methylation is an important epigenetic mechanism for regulating gene expression levels (messenger RNA abundance), it is natural to wonder how age-related DNAm changes relate to those observed in gene expression levels. As described in Additional file 11 , I find very little overlap. Further, I do not find that age effects on DNAm levels affect genes known to be differentially expressed between naive CD8 T cells and CD8 memory cells (Additional file 11 ). These non-significant results reflect the fact that the relationship between DNAm levels and expression levels is complex” | concepts[59] |
| concept | ticking rate — not defined in this paper's own words; rate of change of the calibration curve — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | concepts[60] |
| concept | organismal growth — not defined in this paper's own words; developmental process accompanying the high early clock rate — “The linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock. Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood” | concepts[61] |
| concept | mitotic age — not defined in this paper's own words; somatic cell-copy-number construct distinguished from DNAm age — “Since epigenetic somatic errors in somatic replications appear to be readily detected as age-related changes in methylation [ 35 , 36 ], it is a plausible hypothesis that DNAm age measures the number of somatic cell replications. In other words, that it measures mitotic age (which assigns a cell copy number to every cell) [ 35 , 37 ]. While DNAm age is correlated with cell passage number (Figure 5 ) and the clock ticking rate is highest during organismal growth (Figure 6 B,C), it is clearly different from mitotic age since it tracks chronological age in non-proliferative tissue (for example, brain tissue) and assigns similar ages to both short and long lived blood cells” | concepts[62] |
| concept | cellular senescence — not defined in this paper's own words; hypothesized interpretation rejected for DNAm age — “Another plausible hypothesis is that DNAm age is a marker of cellular senescence. This turns out to be wrong as can be seen from the fact that DNAm age is highly related to chronological age in immortal, non-senescent cells - for example, immortalized B cells (Figure 2 T). Further, DNAm age and cell passage number are highly correlated in ES cells (Figure 5 H,J), which are also immortal” | concepts[63] |
| concept | genomic stability — not defined in this paper's own words; stability distinguished from epigenetic stability in the EMS model — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | concepts[64] |
| concept | maintenance methyltransferases — not defined in this paper's own words; enzymes hypothesized to contribute to the EMS — “I propose that DNAm age measures the cumulative work done by a particular kind of epigenetic maintenance system (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stability, I find it useful to distinguish these two concepts. If the EMS model of DNAm age is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cells. Maintenance methyltransferases are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate” | concepts[65] |
| concept | cancer tissue — not defined in this paper's own words; tumor tissue predicted to show accelerated DNAm age — “If this EMS model of DNAm age is correct, then DNAm age should be accelerated by many perturbations that affect epigenetic stability. Further, age acceleration should have some beneficial effects given the protective role of the EMS. In particular, the EMS model of DNAm age entails the following testable predictions. First, cancer tissue should show signs of accelerated age, reflecting the protective actions of the EMS. Second, many mitogens, genomic aberrations, and oncogenes, which trigger the response of the EMS, should be associated with accelerated DNAm age. Third, high age acceleration of cancer tissue should be associated with fewer somatic mutations given the protective role of the EMS. Fourth, mutations in TP53 should be associated with a lower age acceleration of cancer tissue if one further assumes that p53 signaling helps trigger the EMS” | concepts[66] |
| concept | tumor morphology — not defined in this paper's own words; grade and stage compared with age acceleration — “I assembled a large collection of cancer data sets composed of n = 5,826 cancer samples from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissues show relatively large correlations between DNAm age and patient age, the correlation between DNAm age and chronological age tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age acceleration with an average age acceleration of 36.2 years (Additional file 13 B). Tumor morphology (grade and stage) has only a weak relationship with age acceleration in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age acceleration in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction” | concepts[67] |
| concept | number of mutations per cancer sample — not defined in this paper's own words; tumor mutation burden inversely associated with acceleration — “Strikingly, the number of mutations per cancer sample tends to be inversely correlated with age acceleration (Figure 7 ), which may reflect that DNAm age acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age acceleration and the number of somatic mutations can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types” | concepts[68] |
| concept | p53 signaling — not defined in this paper's own words; signaling hypothesized to trigger age-accelerating EMS processes — “Additional file 16 presents the genes whose mutation has the strongest effect on age acceleration. Strikingly, TP53 was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutation is associated with significantly lower age acceleration in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53 are associated with a nominally significant increased age acceleration ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signaling can trigger processes that accelerate DNAm age, which supports the EMS model of DNAm age” | concepts[69] |
| concept | estrogen receptor — not defined in this paper's own words; breast-cancer receptor whose mutation status stratifies acceleration — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | concepts[70] |
| concept | progesterone receptor — not defined in this paper's own words; breast-cancer receptor whose mutation status stratifies acceleration — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | concepts[71] |
| concept | HER2/neu amplification — not defined in this paper's own words; breast-cancer aberration without a significant acceleration relation — “In the following, I show that DNAm age changes across different breast cancer types. Somatic mutations in steroid receptors have a pronounced effect on DNAm age in breast cancer samples: samples with a mutated estrogen receptor (ER) or mutated progesterone receptor (PR) exhibit a much higher age acceleration than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplification has no significant relationship with age acceleration. Age acceleration differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age acceleration. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age acceleration can be observed for basal-like tumors” | concepts[72] |
| concept | BRAF — not defined in this paper's own words; colorectal mutation associated with increased acceleration — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concepts[73] |
| concept | K-RAS — not defined in this paper's own words; colorectal mutation associated with decreased acceleration — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concepts[74] |
| concept | MLH1 — not defined in this paper's own words; mismatch-repair gene whose promoter hypermethylation raises acceleration — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concepts[75] |
| concept | CpG island methylator phenotype — not defined in this paper's own words; cancer hypermethylation phenotype associated with acceleration — “Colorectal cancer samples with a BRAF (V600E) mutation are associated with an increased age acceleration (Figure 9 A) whereas samples with a K-RAS mutation have a decreased age acceleration (Figure 9 C). Echoing previous results, TP53 mutations appear to be associated with decreased age acceleration (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1 leads to the most significant increase in age acceleration ( P = 5.7E-5; Figure 9 D), which supports the EMS model of DNAm age. The CpG island methylator phenotype, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age acceleration, which may reflect its association with MLH1 hypermethylation and BRAF mutations” | concepts[76] |
| concept | H3F3A — not defined in this paper's own words; histone-variant gene whose mutations stratify GBM acceleration — “Interestingly, age acceleration in GBM samples is highly significantly ( P = 3.3E-7; Figure 9 J) associated with certain mutations in H3F3A , which encodes the replication-independent histone variant H3.3. These mutations are single-nucleotide variants changing lysine 27 to methionine (K27M) or changing glycine 34 to arginine (G34R) [ 40 ]. The fact that GBMs with a G34R mutation in H3F3A have a much higher age acceleration than those with a K27M mutation (Figure 9 J,L) makes sense since each H3F3A mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern and acts through a different set of genes [ 40 ]. Lysine 27 is a critical residue of histone 3 variants, and methylation at this position (H3K27me), which may be mimicked by the terminal CH3 of methionine substituted at this residue [ 40 ], is commonly associated with transcriptional repression [ 41 ] while H3K36 methylation or acetylation typically promotes gene transcription [ 42 ]. G34-mutant cells exhibit increased RNA polymerase II binding, and increased gene expression, most notably that of the oncogene MYCN [ 43 ]. Both H3F3A mutations are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup [ 44 ]. Age acceleration in GBM samples is also associated with the following genomic aberrations: TP53 mutation, ATRX mutation, chromosome 7 gain, chromosome 10 loss, CDKN2A deletion, and EGFR amplification (Figure 9 G-I). Reflecting these results for individual markers, age acceleration varies significantly ( P = 2E-7; Figure 9 L) across the GBM subtypes defined in [ 44” | concepts[77] |
| concept | cancer cell lines — not defined in this paper's own words; in-vitro cancer lines with highly variable DNAm ages — “Using seven publicly available cell line data sets (Additional files 12 and 13 ), I was able to estimate the DNAm age of 59 different cancer cell lines (from bladder, breast, gliomas, head/neck, leukemia, and osteosarcoma). Across all cell lines, DNAm age does not have a significant correlation with the chronological age of the patient from whom the cancer cell line was derived (Additional file 18 B). However, a marginally significant age correlation can be observed across osteosarcoma cell lines (cor = 0.41, P = 0.08; Additional file 18 C). Overall, DNAm age acceleration varies greatly across the cancer lines lines (Additional files 18 A and 19 ): the highest values can be observed for AML cell lines (KG1A, 182 years; HL-60, 177 years); the lowest values for head/neck squamous cell carcinoma cell line (UPCI SCC47, 6 years) and two breast cancer cell lines (SK-BR-3, 8 years; MDA-MB-468, 11 years). It will be interesting to test whether DNAm age relates to other characteristics of cancer cell lines” | concepts[78] |
| concept | weighted average of the 353 clock CpGs — not defined in this paper's own words; linear predictor combined before age calibration — “Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylation data from healthy tissues, cancer tissues, and cancer cell lines. The healthy tissue data allowed me to develop a multi-tissue predictor of age (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate” | concepts[79] |
| concept | calibration function — not defined in this paper's own words; transformation from weighted CpG average to DNAm age — “Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylation data from healthy tissues, cancer tissues, and cancer cell lines. The healthy tissue data allowed me to develop a multi-tissue predictor of age (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age using a calibration function. The calibration function reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate” | concepts[80] |
| concept | glmnet — not defined in this paper's own words; R package implementing the penalized regression — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | concepts[81] |
| concept | calibrated version of chronological age — not defined in this paper's own words; transformed response regressed on CpG probes — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | concepts[82] |
| concept | predicted age — not defined in this paper's own words; formal definition of DNAm age in the model — “Using the training data sets, I used a penalized regression model (implemented in the R package glmnet [ 45 ]) to regress a calibrated version of chronological age on 21,369 CpG probes that a) were present both on the Illumina 450K and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnet was chosen to 0.5 (elastic net regression) and the lambda value was chosen using cross-validation on the training data (lambda = 0.0226). DNAm age was defined as predicted age. Mathematical details are provided in Additional file 2” | concepts[83] |
Full text — tagged
DNA methylation ageterm of human tissues and cell types Abstract It is not yet known whether DNA methylation levelsterm can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age predictionterm is a biologically meaningful measure.
I developed a multi-tissue predictor of ageterm that allows one to estimate the DNA methylation ageterm of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylationterm array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation ageterm has the following properties: first, it is close to zero for embryonicterm and induced pluripotent stem cellsterm; second, it correlates with cell passage numberterm; third, it gives rise to a highly heritable measure of age accelerationterm; and, fourth, it is applicable to chimpanzee tissuesterm. Analysis of 6,000 cancer samplesterm from 32 datasets showed that all of the considered 20 cancer types exhibit significant age accelerationterm, with an average of 36 years. Low age-acceleration of cancer tissueterm is associated with a high number of somatic mutationsterm and TP53 mutationsterm·var, while mutations in steroid receptorsterm greatly accelerate DNA methylation ageterm in breast cancer. Finally, I characterize the 353 CpG sitesterm that together form an aging clockterm in terms of chromatin states and tissue variance.
I propose that DNA methylation ageterm measures the cumulative effect of an epigenetic maintenance systemterm. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
Background An increasing body of evidence suggests that many manifestations of aging are epigenetic [ 1 , 2 ]. This article focuses on one particular type of epigenetic control: cytosine-5 methylationterm within CpG dinucleotidesterm (also known as DNA methylationterm). Age-related DNA hypomethylation has long been observed in a variety of species, including salmon [ 3 ], rats [ 4 ], and mice [ 5 ]. More recent studies have shown that many CpGs are subject to age-related hypermethylation. A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [ 6 - 14 ]. Previous studies have shown that age-related hypermethylation occurs preferentially at CpG islands [ 8 ], at bivalent chromatin domain promoters that are associated with key developmental genes [ 15 ], and at Polycomb-group protein targets [ 10 ]. The epigenomic landscape varies markedly across tissue typesterm·var [ 16 - 18 ] and many age-related changes depend on tissue typeterm·var [ 8 , 19 ]. But several recent studies have shown that age-dependent CpG signaturesterm can be defined independently of sex, tissue typeterm·var, disease state and array platform [ 10 , 13 - 15 , 20 - 22 ]. While several recent articles describe age predictors based on DNAm levels in specific tissues (for example, saliva or blood [ 23 , 24 ]), it is not yet known whether age can be predicted irrespective of tissue typeterm·var using a single predictor. Here I use an unprecedented collection of publicly available DNA methylationterm data sets for defining and evaluating an age predictor. Its astonishing accuracy across most tissues and cell types justifies its designation as a multi-tissue age predictorterm. Its age predictionterm, referred to as DNAm ageterm, can be used as a biomarker for addressing a host of questions arising in aging research and related fields. For example, I show that premature aging diseases (such as progeria) do not resemble healthy normal aging according to DNAm ageterm and that interventions used for creating induced pluripotent stem (iPS) cells reset the epigenetic clock to zero. I also describe what can be learnt from applying DNAm ageterm to cancer tissuesterm and cancer cell linesterm.
Results and discussion Description of the (non-cancer) DNA methylationterm data sets I assembled a large DNA methylationterm data set by combining publicly available individual data sets measured on the Illumina 27Kterm or Illumina 450Kterm array platform. In total, I analyzed n = 7,844 non-cancer samples from 82 individual data sets (Additional file 1 ), which assess DNA methylation levelsterm in 51 different tissues and cell types. Although many data sets were collected for studying certain diseases (Additional file 2 ), they largely involved healthy tissues. In particular, cancer tissuesterm were excluded from this first large data set since it is well known that cancer has a profound effect on DNA methylation levelsterm [ 6 , 7 , 24 - 26 ]. The Cancer Genome Atlas (TCGA) data sets mentioned in Additional file 1 involved normal adjacent tissue from cancer patients. Details on the individual data sets and data pre-processing steps are provided in Materials and methods and Additional file 2 . As described in Additional file 1 , the first 39 data sets were used to construct ('train’) the age predictor. Data sets 40 to 71 were used to test (validate) the age predictor. Data sets 72 to 82 served other purposes - for example, to estimate the DNAm ageterm of embryonicterm stem and iPS cellsterm. The criteria used for selecting the training sets are described in Additional file 2 . Briefly, the training dataterm were chosen i) to represent a wide spectrum of tissues/cell types, ii) to involve samples whose mean age (43 years) is similar to that in the test dataterm, and iii) to involve a high proportion of samples (37%) measured on the Illumina 450Kterm platform since many on-going studies use this recent Illumina platform. Here I only studied 21,369 CpGs (measured with the Infinium type II assay) that were present on both Illumina platforms (Infinium 450K and 27K) and had fewer than 10 missing values across the data sets. Several important limitations of this study are discussed in Additional file 2 .
The multi-tissue age predictorterm used for defining DNAm ageterm To ensure an unbiased validation in the test dataterm, I only used the training dataterm to define the age predictor. As detailed in Materials and methods and Additional file 2 , a transformed version of chronological ageterm was regressed on the CpGs using a penalized regression modelterm (elastic netterm). The elastic netterm regression model automatically selected 353 CpGs (Additional file 3 ). I refer to the 353 CpGs as (epigenetic) clock CpGsterm since their weighted average (formed by the regression coefficients) amounts to an epigenetic clock. Before characterizing them, I will show that the resulting age predictor performs remarkably well across a wide spectrum of tissues and cell types.
Predictive accuracy across different tissues I considered several measures of predictive accuracy since each measure has distinct advantages. The first, referred to as 'age correlationterm’, is the Pearson correlation coefficientterm between DNAm ageterm (predicted ageterm) and chronological ageterm. It has the following limitations: it cannot be used for studying whether DNAm is well calibrated, it cannot be calculated in data sets whose subjects have the same chronological ageterm (for example, cord blood samples from newborns), and it strongly depends on the standard deviationterm of age (as described below). The second accuracy measure, referred to as (median) 'error’, is the median absolute differenceterm between DNAm ageterm and chronological ageterm. Thus, a test set error of 3.6 years indicates that DNAm ageterm differs by less than 3.6 years in 50% of subjects. The error is well suited for studying whether DNAm ageterm is poorly calibrated. 'Average age accelerationterm’, defined by the average difference between DNAm ageterm and chronological ageterm, can be used to determine whether the DNAm ageterm of a given tissue is consistently higher (or lower) than expected.
According to these three accuracy measures, the multi-tissue age predictorterm performs remarkably well in most tissues and cell types. Although its high accuracy in the training dataterm (age correlationterm 0.97, error = 2.9 years; Figure 1 ) is probably overly optimistic, its performance assessment (age correlationterm = 0.96, error = 3.6 years; Figure 2 ) in the test dataterm is unbiased. Note that the age predictor performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, buccal epithelium, colon, adipose, liver, lung, saliva, uterine cervix) as well as in individual cell types such as CD4 T cells and CD14 monocytes (Figure 2 C) and immortalized B cells (Figure 2 T).
Chronological ageterm (y-axis) versus DNAm ageterm (x-axis) in the training dataterm. Each point corresponds to a DNA methylationterm sample (human subject). Points are colored and labeled according to the underlying data set as described in Additional file 1 . (A) Across all training dataterm, the correlation between DNAm ageterm (x-axis) and chronological ageterm (y-axis) is 0.97 and the error (median absolute differenceterm) is 2.9 years. Results for (B) peripheral blood mononuclear cells (cor = 0.97, error <1 year), (C) whole blood (cor = 0.98, error = 2.7 years), (D) cerebellum (cor = 0.92, error = 4.5), (E) pons (cor = 0.96, error = 3.3), (F) pre-frontal cortex (cor = 0.98, 1.4), (G) temporal cortex (cor = 0.99, error = 2.2), (H) brain samples, composed of 58 glial cell, 58 neuron cell, 20 bulk, and 9 mixed samples (cor = 0.94, error = 3.1), (I) normal breast tissue (cor = 0.73, error = 8.9), (J) buccal cells (cor = 0.95, error <1 year), (K) cartilage (cor = 0.79, error = 4), (L) colon (cor = 0.98, error = 3.7), (M) dermal fibroblasts (cor = 0.92, error = 12), (N) epidermis (cor = 0.96, error = 3.1), (O) gastric tissue (cor = 0.83, error = 5.3), (P) normal adjacent tissue from head/neck cancers (cor = 0.73, error = 5.8), (Q) heart (cor = 0.82, error = 9.2), (R) kidney (cor = 0.88, error = 3.8), (S) liver (cor = 0.90, error = 4.5), (T) lung (cor = 0.80, error = 3.1), (U) mesenchymal stromal cells (cor = 0.95, error = 5.2), (V) prostate (cor = 0.55, error = 4.2), (W) saliva (cor = 0.89, error = 2.9), (X) stomach (cor = 0.84, error = 3.7), (Y) thyroid (cor = 0.96, error = 4.1).
Chronological ageterm (y-axis) versus DNAm ageterm (x-axis) in the test dataterm. (A) Across all test dataterm, the age correlationterm is 0.96 and the error is 3.6 years. Results for (B) CD4 T cells measured at birth (age zero) and at age 1 (cor = 0.78, error = 0.27 years), (C) CD4 T cells and CD14 monocytes (cor = 0.90, error = 3.7), (D) peripheral blood mononuclear cells (cor = 0.96, error = 1.9), (E) whole blood (cor = 0.95, error = 3.7), (F) cerebellar samples (cor = 0.92, error = 5.9), (G) occipital cortex (cor = 0.98, error = 1.5), (H) normal adjacent breast tissue (cor = 0.87, error = 13), (I) buccal epithelium (cor = 0.83, error = 0.37), (J) colon (cor = 0.85, error = 5.6), (K) fat adipose (cor = 0.65, error = 2.7), (L) heart (cor = 0.77, error = 12), (M) kidney (cor = 0.86, error = 4.6), (N) liver (cor = 0.89, error = 6.7), (O) lung (cor = 0.87, error = 5.2), (P) muscle (cor = 0.70, error = 18), (Q) saliva (cor = 0.83, error = 2.7), (R) uterine cervix (cor = 0.75, error = 6.2), (S) uterine endometrium (cor = 0.55, 11), (T) various blood samples composed of 10 Epstein Barr Virus transformed B cell, three naive B cell, and three peripheral blood mononuclear cell samples (cor = 0.46, error = 4.4). Samples are colored by disease status: brown for Werner progeroid syndrome, blue for Hutchinson-Gilford progeria, and turquoise for healthy control subjects.
The age predictor is particularly accurate in data sets composed of adolescents and children - for example, blood (Figures 1 B and 2 B; Additional file 4 P,S), brain data (Figures 1 F and 2 F,G), and buccal epithelium (Figure 2 I).
The DNAm ageterm of blood and brain cells A detailed analysis of blood tissue can be found in Additional file 4 . Human blood cells have different life spans: while CD14+ monocytes (myeloid lineage) only live several weeks, CD4+ T cells (lymphoid lineage) represent a variety of cell types that can live from months to years. An interesting question is whether blood cell types have different DNAm agesterm. DNAm ageterm does not vary significantly across sorted blood cells from healthy male subjects (Additional file 4 T). These results combined with the fact that the age predictor works well in individual cell types (Figure 2 C; Additional file 4 ) strongly suggest that DNAm ageterm does not reflect changes in cell type compositionterm but rather intrinsic changes in the methylometerm. While I expect significant correlations between DNAm ageterm and abundance measures of some blood cell types (that are known to change with age), these correlations do not reflect a direct causal effect of cell type abundance on DNAm ageterm but rather a confounding effect due to chronological ageterm. This conclusion is also corroborated by the finding that DNAm ageterm is highly related to chronological ageterm in other types of cells - for example, glial cells and neurons (Figure 1 H) and various brain regions (Additional file 5 ).
DNAm ageterm and progeria DNAm ageterm can be used to study whether cells from patients with accelerated aging diseases such as progeria (including Werner progeroid syndrome, Hutchinson-Gilford progeria) truly look old at an epigenetic level. I find that progeria disease status is not related to DNAm-based age accelerationterm in Epstein-Barr virus-transformed B cells (Figure 2 T).
Tissues where DNAm ageterm is poorly calibrated DNAm ageterm is poorly calibrated (that is, leads to a high error) in breast tissue (Figure 2 H), uterine endometrium (Figure 2 S), dermal fibroblasts (Figure 1 M), skeletal muscle tissue (Figure 2 P), and heart tissue (Figures 1 Q and 2 L). I can only speculate on the biological reasons that could explain the poor calibration. The high error in breast tissue (Additional file 6 ) may reflect hormonal effects or cancer field effects in this normal adjacent tissue from cancer samplesterm. Note that the lowest error (8.9 years) in breast tissue is observed in normal breast tissue, that is, in samples from women without cancer (training dataterm set 14; Additional file 6 ). The menstrual cycle and concomitant increases in cell proliferation may explain the high error in uterine endometrium. Myosatellite cells may effectively rejuvenate the DNAm ageterm of skeletal muscle tissue. Similarly, the recruitment of stem cells into cardiomyocytes for new cardiac muscle formation could explain why human heart tissue tends to have a low DNAm ageterm. Carefully designed studies will be needed to test these hypotheses.
The age correlationterm in a data set is determined by the standard deviationterm of age In the following, I describe non-biological reasons that affect the accuracy (age correlationterm) of the age predictor. To address how well the age predictor works in individual data sets, I used two different approaches. First, I applied the age predictor to individual data sets (see columns 'Cor(Age, DNAmAge)’ and 'Median Errorterm·var(Age, DNAmAge)’ in Additional file 1 ). An obvious limitation of this approach is that it leads to biased results in the training dataterm sets.
The second approach, referred to as leave-one-data-set-out cross-validationterm (LOOCVterm) analysis, leads to unbiased estimates of the predictive accuracy for each data set. As suggested by its name, this approach estimates the DNAm ageterm for each data set (considered as test dataterm set) separately by fitting a separate multi-tissue age predictorterm to the remaining (left out) data sets. The resulting unbiased estimates of predictive accuracy can be found in Additional file 1 : columns 'Cor LOOCVterm’ and 'Error LOOCVterm’, respectively.
Data sets differ greatly with respect to the median chronological ageterm and the standard deviationterm, which is defined as the square root of the variance of age. Some data sets only involve samples with the same age (standard deviationterm = 0) while others involve both young and old subjects (Additional file 1 ). As expected, the standard deviationterm is significantly correlated (r = 0.49, P = 4E-5; Figure 3 A) with the corresponding LOOCVterm estimate of the age correlationterm. In contrast, the sample size of the data set has no significant relationship with the age correlationterm (Figure 3 B).
Factors affecting the relation between age and DNAm ageterm. (A-C) Factors influencing prediction accuracy in the training and test sets. (A) The standard deviationterm of age (x-axis) has a strong relationship (cor = 0.49, P = 4E-5) with age correlationterm (y-axis). To arrive at an unbiased measure of prediction accuracy, I estimated the age correlationterm using a leave-one-data-set-out cross validation (LOOCVterm) analysis. Each point is labeled and colored according to the underlying data set (Additional file 1 ). (B) Sample size (x-axis) is not significantly correlated with the age correlationterm (y-axis). (C) Mean DNAm ageterm per tissue (x-axis) versus mean chronological ageterm (y-axis). Points correspond to the human tissue data mentioned in Additional file 1 . Breast tissue shows signs of accelerated aging. (D,E) The effect of tissue typeterm·var on the age predictionterm in test dataterm set 71 even for tissues that were not part of the training dataterm (for example, esophagus, jejunum, penis). (E) The horizontal bars report the DNAm ageterm (x-axis) of a single tissue from a single donor ( H12817 ). Only one sample per tissue (grey axis numbers) was available. DNAm ageterm has a low coefficient of variationterm·def (0.12). The red vertical line corresponds to the true chronological ageterm. (F-H) DNAm ageterm for various tissues from data set 77 but chronological ageterm was not available. (F,G) A multi-tissue analysis of somatic adult tissue data from an adult male and an adult female, respectively. (H) Neonatal tissues tend to have low DNAm ageterm. (I,J) The DNAm ageterm of sperm is significantly lower than the chronological ageterm of the respective sperm donors in data sets 74 and 75, respectively. Error bars represent one standard error.
A host of technical artifactsterm could explain differences in predictive accuracy: for example, variations in sample processing, DNA extraction, DNA storage effects, batch effects, and chip effects.
DNAm ageterm of multiple tissues from the same subject In the following, I will address whether solid tissues can be found whose DNAm ageterm differs substantially from chronological ageterm. As a first step, I compared the mean DNAm ageterm per tissue with the corresponding mean chronological ageterm. As expected, mean DNAm ageterm per tissue is highly correlated (cor = 0.99; Figure 3 C) with mean chronological ageterm. But breast tissue shows evidence of significant age accelerationterm. The results of Figure 3 C should be interpreted with caution because the analysis included training dataterm sets and involved tissue samples from different subjects.
A more interesting analysis is to compare the DNAm agesterm of tissues collected from the same subjects. DNAm ageterm does not change significantly across different brain regions (temporal cortex, pons, frontal cortex, cerebellum) from the same subjects (Additional file 5 K,L). I could only find three human subjects from whom many tissues had been profiled (Figure 3 E-G). Although the limited sample sizes per tissue (mostly one sample per tissue per subject) did not allow for rigorous testing, these data can be used to estimate the coefficient of variation of DNAm ageterm (that is, the standard deviationterm divided by the mean). Note that the coefficient of variationsterm·def for the first and second adult male are relatively low (0.12 and 0.15 in Figure 3 E,F) even though the analysis involved several tissues that were not part of the training dataterm - for example, jejunum, penis, pancreas, esophagus, spleen, pancreas, lymph node, diaphragm. The coefficient of variationterm·def in the adult female (Figure 3 G) is relatively high (0.21), which reflects the fact that her breast tissue shows signs of substantial age accelerationterm (congruent with the previous results from Figure 3 C).
It remains to be seen how well DNAm ageterm performs in tissues and DNA sources that were not represented in the training dataterm set. Figure 3 D,E suggest that it also performs well in several other human tissues. As expected, I did not find a significant age correlationterm in sperm. The DNAm ageterm of sperm is significantly lower than the chronological ageterm of the donor (Figure 3 I, J).
DNAm ageterm is applicable to chimpanzeesterm It is important to study whether there are inter-primate differences when it comes to DNAm ageterm. These studies may not only help in identifying model organisms for rejuvenating interventions but might explain differences in primate longevity. While future studies could account for sequence differences, it is straightforward to apply the DNAm ageterm estimation algorithm to Illumina DNA methylationterm data sets 72 [ 27 ] and 73 [ 28 ]. Strikingly, the DNAm ageterm of heart, liver, and kidney tissue from chimpanzeesterm ( Pan troglodytes ) is aligned with that of the corresponding human tissues (Figure 4 A,B). Further, the DNAm ageterm of blood samples from two extant hominid species of the genus Pan (commonly referred to as chimpanzeeterm) is highly correlated with chronological ageterm (Figure 4 C). While DNAm ageterm is applicable to chimpanzeesterm, its performance appears to be diminished in gorillasterm (Figure 4 F), which may reflect the larger evolutionary distance.
Studying the conservation of DNAm ageterm in tissues from great apes. Analysis of two independent data sets involving tissues from great apes. (A,B) Results for data set 72 [ 27 ]. A high age correlationterm (cor = 0.84, error = 10 years) can be observed when studying both chimpanzeeterm heart (colored grey) and human heart tissue (colored turquoise) samples. To facilitate a comparison, I also added the heart tissue data from data set 25 (blue circles). (B) DNAm ageterm is closely related to chronological ageterm (cor = 0.75, error = 3.7) across kidney and liver samples from humans (turquoise) and chimpanzeesterm (grey). (C-F) Results for ape blood samples from data set 73. (C) Highly accurate results (cor = 0.9, error = 1.4) can be observed for blood samples from common chimpanzeesterm ( Pan troglodytes ; labeled C, colored blue) and bonobos ( Pan paniscus ; labeled B, colored turquoise). (D) Results for common chimpanzeesterm only. (E) Results for bonobos only. (F) Results for gorillasterm.
DNAm ageterm of induced pluripotent stem cellsterm and stem cells The billions of cells within an individual can be organized by genealogy into a single somatic cell tree that starts from the zygote and ends with differentiated cells. Cells at the root of this tree should be young. This is indeed the case: embryonicterm stem cells have a DNAm ageterm close to zero in five different data sets (Figure 5 ). iPS cellsterm are a type of pluripotent stem cell artificially derived from a non-pluripotent cell (typically an adult somatic cell) by inducing a set of specific genes. Since iPS cellsterm are similar to embryonicterm stem (ES) cells, I hypothesized that the DNAm ageterm of iPS cellsterm should be significantly younger than that of corresponding primary cellsterm. I confirm this hypothesis in three independent data sets (Figures 5 A-C). No significant difference in DNAm ageterm could be detected between ES cellsterm and iPS cellsterm (Figure 5 A,B).
Induced pluripotent stem cellsterm, embryonicterm stem cells and cell passagingterm. (A - C) Induced pluripotent stem (iPS) cells have a lower DNAm ageterm than corresponding primary cellsterm in (A) data set 77 (Kruskal Wallis P -value 1E-14), (B) data set 78 ( P = 8E-10), and (C) data set 79 ( P = 0.0062). (A,B) There is no significant difference in DNAm ageterm between ES cellsterm and iPS cellsterm (both restricted to cell passage numbersterm less than 15) in data sets 77 and 78, respectively. (D,E) DNAm ageterm of human ES cellterm lines and adult tissues in data sets 80 and 81, respectively. (F - J) Cell passage numberterm (y-axis) is significantly correlated with DNAm ageterm (x-axis). (F) Cell passage numberterm (y-axis) versus DNAm ageterm in data set 77. Points are colored by cell type (black for ES cellsterm, red for iPS cellsterm, blue for somatic cells). (G,H) Analogous results for iPS cellsterm (cor = 0.33, P = 0.025) and embryonicterm stem cells (cor = 0.28, P = 0.0023) from data set 77. (I,J) Validation of these findings in two independent data sets, 78 and 79, respectively. Panel (J) involves only stem cells. Panels (A-C) involve cells that had undergone fewer than 15 cell passages. Panels (C,J) are restricted to cells that were not irradiated. The bar plots show the mean value ±1 standard error.
Effect of cell passagingterm on DNAm ageterm Most cells lose their proliferation and differentiation potential after a limited number of cell divisions (Hayflick limit). I hypothesized that cell passagingterm (also known as splitting cells) increases DNAm ageterm. I confirmed this hypothesis in three independent data sets (data sets 77, 78, and 79) as shown in Figure 5 F-J. A significant correlation between cell passage numberterm and DNAm ageterm can also be observed when restricting the analysis to iPS cellsterm (Figure 5 G) or when restricting the analysis to ES cellsterm (Figure 5 H,J).
Comparing the multi-tissue predictor with other age predictors As shown in Additional file 2 , the proposed multi-tissue predictor of ageterm outperforms predictors described in other articles [ 21 , 23 ]. While further gains in accuracy can perhaps be achieved by focusing on a single tissue and considering more CpGs, the major strength of the proposed multi-tissue age predictorterm lies in its wide applicability: for most tissues it will not require any adjustments or offsets. I briefly mention that a 'shrunkenterm’ version of the multi-tissue predictor (Additional files 2 and 3 ), based on 110 CpGs (selected from the 353 clock CpGsterm) is highly accurate in the training dataterm (cor = 0.95, error = 4 years) and test dataterm (cor = 0.95, error = 4.2 years).
What is known about the 353 clock CpGsterm?
An Ingenuity Pathway analysis of the genes that co-locate with the 353 clock CpGsterm shows significant enrichment for cell death/survival, cellular growth/proliferation, organismal/tissue development, and cancer (Additional file 7 ).
The 353 clock CpGsterm can be divided into two sets according to their correlation with age. The 193 positively and 160 negatively correlated CpGs get hypermethylated and hypomethylated with age, respectively. Using DNA methylationterm data measured across many different adult and fetal tissues, I study the relationship between tissue variance and age effects (Additional file 8 ). While the DNA methylation levelsterm of the 193 positively related CpGsterm vary less across different tissues, those of the 160 negatively related CpGsterm vary more across tissues than the remaining CpGs on the Illumina 27Kterm array. To estimate 'pure’ age effects, I used a meta analysis method that implicitly conditions on data set, that is, it removes the confounding effects due to data set and tissue typeterm·var. The clock CpGsterm include those with the most significant meta analysis P -value for age irrespective of whether the meta analysis P -value was calculated using only training dataterm sets or all data sets (Additional file 8 E). While positively related markers do not show a significant relationship with CpG island status (Additional file 9 F), negatively related markers tend to be over-represented in CpG shoresterm ( P = 9.3E-6; Additional file 9 K).
Significant differences between positive and negative markers exist when it comes to Polycomb-group protein binding: positively related CpGsterm are over-represented near Polycomb-group target genesterm (reflecting results from [ 10 , 14 ]) while negative CpGs show no significant relationship (Additional file 9 H-J,M-O).
Chromatin state analysis Chromatin state profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. It provides a systematic means of detecting cis -regulatory elements (given the central role of chromatin in mediating regulatory signals and controlling DNA access) and can be used for characterizing non-coding portions of the genome, which contribute to cellular phenotypes [ 29 ]. While individual histone modifications are associated with regulator binding, transcriptional initiation, enhancer activity, combinations of chromatin modifications can provide even more precise insight into chromatin state [ 29 ]. Ernst et al . [ 29 ] distinguish six broad classes of chromatin states, referred to as promoter, enhancer, insulator, transcribed, repressed, and inactive states. Within them, active, weak and poised promotersterm (states 1 to 3) differ in expression levels, while strong and weak enhancers (states 4 to 7) differ in expression of proximal genes. The 193 positively related CpGsterm are more likely to be in poised promotersterm (chromatin state 3 regions; Additional file 9 B) while the 160 negatively related CpGsterm are more likely to be in either weak promotersterm (chromatin state 2; Additional file 9 D) or strong enhancersterm (chromatin state 4; Additional file 9 E).
Age accelerationterm is highly heritable Several authors have found that DNA methylation levelsterm are under genetic control [ 24 , 26 , 30 - 32 ]. Since many age-related diseases are heritable, it is interesting to study whether age accelerationterm (here defined as difference between DNAm ageterm and chronological ageterm) is heritable as well. I estimated the broad sense heritabilityterm of age accelerationterm using Falconerterm’s formula, H 2 = 2(cor(MZ)-cor(DZ)), in two twin data sets that included both monozygotic (MZ) and dizygotic (DZ) twins.
As detailed in Additional file 10 , the broad sense heritabilityterm of age accelerationterm is 100% in newborns (data set 50) and 39% in older subjects (data set 41), which suggests that non-genetic factors become more relevant later in life.
Aging effects on gene expression (messenger RNA) levels Since DNA methylationterm is an important epigenetic mechanism for regulating gene expression levelsterm (messenger RNA abundance), it is natural to wonder how age-related DNAm changes relate to those observed in gene expression levelsterm. As described in Additional file 11 , I find very little overlap. Further, I do not find that age effects on DNAm levels affect genes known to be differentially expressed between naive CD8 T cells and CD8 memory cells (Additional file 11 ). These non-significant results reflect the fact that the relationship between DNAm levels and expression levels is complex [ 33 , 34 ].
Age effects on individual CpGs For each CpG, I report the median DNAm level in subjects aged younger than 35 years and in subjects older than 55 years (Additional file 3 ). The age-related change in beta values is typically small (the average absolute difference across the 353 CpGs is only 0.032). The weak age effect on individual clock CpGsterm can also be observed in the heat map that visualizes how the DNAm levels change across subjects (Figure 6 A). The few vertical bands in the heat map suggest that the clock CpGsterm are relatively robust against tissue and data set effects.
Heat map of DNA methylation levelsterm of the 353 CpGs across all samples. (A) The heat map color-codes DNAm levels: blue and red for beta values close to zero and one, respectively. Note that DNA methylation levelsterm only change very gradually with age. The 353 clock CpGsterm (rows) are sorted according to their age correlationterm. The first row color band, denoted 'corAge’, color-codes whether a CpG has a negative (blue) or positive (red) correlation with age. 'CpG’ indicates whether a CpG is located in a CpG island (turquoise), shore (brown), or outside of CpG islands. 'PolyGr’: blue for CpGs near a Polycomb group target gene. 'Chr’ color-codes chromosomes. The DNA methylationterm samples (columns) for which chronological ageterm was available are sorted according to age, tissue, and data set. The column color bands visualize properties of the samples. 'Age’: white for age zero and dark brown for the maximum observed age of 101 years. 'Training’: black for training set samples. 'Tissue’ color codes tissue typeterm·var. 'Platform’: black for Illumina 450Kterm. Note that few data sets have a pronounced effect on the clock CpGsterm. The largest vertical band corresponds to the buccal epithelium samples from 15 year old subjects (data set 14, color-coded midnight blue in the column band 'Data’). (B) The weighted average of the 353 clock CpGsterm versus chronological ageterm in the training dataterm sets. The rate of change of the red curve can be interpreted as tick rate. Points are colored and labeled by data set. (C) Analogous results for the test dataterm sets.
The changing ticking rateterm of the epigenetic clock The linear combination of the 353 clock CpGsterm (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6 B,C. The red calibration curve (formula in Additional file 2 ) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6 B). I interpret the rate of change (of this red curve) as the ticking rateterm of the epigenetic clock. Using this terminology, I find that organismal growthterm (and concomitant cell division) leads to a high ticking rateterm that slows down to a constant ticking rateterm (linear dependence) after adulthood.
DNAm ageterm does not measure mitotic ageterm or cellular senescenceterm Since epigenetic somatic errors in somatic replications appear to be readily detected as age-related changes in methylation [ 35 , 36 ], it is a plausible hypothesis that DNAm ageterm measures the number of somatic cell replications. In other words, that it measures mitotic ageterm (which assigns a cell copy number to every cell) [ 35 , 37 ]. While DNAm ageterm is correlated with cell passage numberterm (Figure 5 ) and the clock ticking rateterm is highest during organismal growthterm (Figure 6 B,C), it is clearly different from mitotic ageterm since it tracks chronological ageterm in non-proliferative tissue (for example, brain tissue) and assigns similar ages to both short and long lived blood cells (Additional file 4 T).
Another plausible hypothesis is that DNAm ageterm is a marker of cellular senescenceterm. This turns out to be wrong as can be seen from the fact that DNAm ageterm is highly related to chronological ageterm in immortal, non-senescent cells - for example, immortalized B cells (Figure 2 T). Further, DNAm ageterm and cell passage numberterm are highly correlated in ES cellsterm (Figure 5 H,J), which are also immortal [ 38 ].
Model: DNAm ageterm measures the work done by an epigenetic maintenance systemterm I propose that DNAm ageterm measures the cumulative work done by a particular kind of epigenetic maintenance systemterm (EMS), which helps maintain epigenetic stability. While epigenetic stability is related to genomic stabilityterm, I find it useful to distinguish these two concepts. If the EMS modelterm·def of DNAm ageterm is correct, then this particular kind of EMS appears to be inactive in the perfectly young ES cellsterm. Maintenance methyltransferasesterm are likely to play an important role. In physics, 'work’ is defined by the integral of power over time. Using this terminology, I hypothesize that the power (defined as rate of change of the energy spent by this EMS) corresponds to the tick rate of the epigenetic clock. This model would explain the high tick rate during organismal development since a high power is required to maintain epigenetic stability during this stressful time. At the end of development, a constant amount of power is sufficient to maintain stability leading to a constant tick rate.
If this EMS modelterm·def of DNAm ageterm is correct, then DNAm ageterm should be accelerated by many perturbations that affect epigenetic stability. Further, age accelerationterm should have some beneficial effects given the protective role of the EMS. In particular, the EMS modelterm·def of DNAm ageterm entails the following testable predictions. First, cancer tissueterm should show signs of accelerated age, reflecting the protective actions of the EMS. Second, many mitogens, genomic aberrations, and oncogenes, which trigger the response of the EMS, should be associated with accelerated DNAm ageterm. Third, high age accelerationterm of cancer tissueterm should be associated with fewer somatic mutationsterm given the protective role of the EMS. Fourth, mutations in TP53term should be associated with a lower age accelerationterm of cancer tissueterm if one further assumes that p53 signalingterm helps trigger the EMS.
All of these model predictions turn out to be true as will be shown in the following cancer applications.
DNAm ageterm of cancer tissueterm versus tumor morphologyterm I assembled a large collection of cancer data sets composed of n = 5,826 cancer samplesterm from 32 individual cancer data sets (Additional file 12 ). Details on the cancer data sets can be found in Additional file 2 . While some cancer tissuesterm show relatively large correlations between DNAm ageterm and patient age, the correlation between DNAm ageterm and chronological ageterm tends to be weak (cor = 0.15, P = 1.9E-29; Additional file 13 A). Each cancer/affected tissue shows evidence of significant age accelerationterm with an average age accelerationterm of 36.2 years (Additional file 13 B). Tumor morphologyterm (grade and stage) has only a weak relationship with age accelerationterm in most cancers: only 4 out of 33 hypothesis tests led to a nominally ( P < 0.05) significant result (Additional file 14 ). Only the negative correlation between stage and age accelerationterm in thyroid cancer remains significant (uncorrected P = 8.7E-9; Additional file 14 Z) after applying a Bonferroni correction.
Cancer tissuesterm with high age accelerationterm exhibit fewer somatic mutationsterm Strikingly, the number of mutations per cancer sampleterm tends to be inversely correlated with age accelerationterm (Figure 7 ), which may reflect that DNAm ageterm acceleration results from processes that promote genome stability. Specifically, a significant negative relationship between age accelerationterm and the number of somatic mutationsterm can be observed in the following seven affected tissues/cancers: bone marrow (AML data from TCGA), breast carcinoma (BRCA data), kidney renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), ovarian cancer (OVAR), prostate (PRAD), and thyroid (THCA). Similar results can also be observed in several breast cancer types (Additional file 15 ).
Age accelerationterm versus number of somatic mutationsterm in the TCGA data. Mutation data from TCGA were used to count the number of mutations per cancer sampleterm. (A) Age accelerationterm versus (log transformed) mutation count per sample across all cancers. Note that this analysis is confounded by cancer/tissue typeterm·var. (B - P) A significant negative relationship between age accelerationterm and number of somatic mutationsterm can be observed in the following seven affected tissues/cancers: (C) bone marrow (AML), (D) breast carcinoma (BRCA), (G) kidney (KIRC), (H) kidney (KIRP), (K) ovarian cancer (OVAR), (L) prostate (PRAD), and (O) thyroid (THCA). No significant relationship could be found in the following six cancer types: (F) colon carcinoma (COAD), (I) lung adenocarcinoma (LUAD), (J) lung squamous cell carcinoma (LUSC), (P) uterine endometrioid, (M) rectal cancer (READ), (N) skin. Due to the low sample size, the results are inconclusive for (B) bladder cancer and (E) cervical cancer. Each point corresponds to a DNA methylationterm sample (cancer sampleterm from a human subject) analogous to Additional file 12 . The x-axis reports the log transformed (base 10) number of mutations observed per sample. The figure titles report the biweight midcorrelation, which is a robust measure of correlation.
TP53 mutationsterm·var are associated with lower age accelerationterm Additional file 16 presents the genes whose mutation has the strongest effect on age accelerationterm. Strikingly, TP53term was among the top 2 most significant genes in 4 out of the 13 cancer data sets. Further, TP53 mutationterm·var is associated with significantly lower age accelerationterm in five different cancer types (Additional file 17 ), including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian cancer ( P = 0.03), and uterine corpus endometrioid ( P = 0.00093). Further, marginally significant result can be observed in lung squamous cell carcinoma (Additional file 17 ) and colorectal cancer ( P = 0.073, below). I could only find one cancer type (glioblastoma multiforme (GBM)) where mutations in TP53term are associated with a nominally significant increased age accelerationterm ( P = 0.02; Figure 5 H). Overall, these results suggest that p53 signalingterm can trigger processes that accelerate DNAm ageterm, which supports the EMS modelterm·def of DNAm ageterm.
Somatic mutationsterm in steroid receptorsterm accelerate DNAm ageterm in breast cancer In the following, I show that DNAm ageterm changes across different breast cancer types. Somatic mutationsterm in steroid receptorsterm have a pronounced effect on DNAm ageterm in breast cancer samplesterm: samples with a mutated estrogen receptorterm (ER) or mutated progesterone receptorterm (PR) exhibit a much higher age accelerationterm than ER- or PR- samples in four independent data sets (Figure 8 ). In contrast, HER2/neu amplificationterm has no significant relationship with age accelerationterm. Age accelerationterm differs greatly across different breast cancer types (Figure 4 N): luminal A tumors (typically ER+ or PR+, HER2-, low Ki67), show the highest positive age accelerationterm. Luminal B tumors (typically ER+ or PR+, HER2+ or HER2- with high Ki67) show a similar effect. The lowest age accelerationterm can be observed for basal-like tumors (often triple negative ER-, PR-, HER2-) and HER2 type tumors (typically HER2+, ER-, PR-).
Age accelerationterm in breast cancer. Panels in the first column (A,E,I,M) show that estrogen receptorterm (ER)-positive breast cancer samplesterm have increased age accelerationterm in four independent data sets. Panels in the second column (B,F,J) show the same result for progesterone receptorterm (PR)-positive cancers. Panels in the third column (C,G,K) show that HER2/neu amplificationterm is not associated with age accelerationterm. Panels in the fourth column (D,H,L) show how combinations of these genomic aberrations affect age accelerationterm. (N) Age accelerationterm across the following breast cancer types: Basal-like, HER2-type, luminal A, luminal B, and healthy (normal) breast tissue. (O) Ki-67 expression versus age accelerationterm. (P) Tumor grade is not significantly related to age accelerationsterm, reflecting results from Additional file 14 . Vertical grey numbers on the x-axis report sample sizes. The figure titles report the data source (GSE identifier from Gene Expression Omnibus or TCGA), and the Kruskal Wallis test P -value (except for panels (O,P) , which report correlation test P -values). Error bars represent 1 standard error.
Proto-oncogenes affect DNAm ageterm in colorectal cancer Colorectal cancer samplesterm with a BRAFterm (V600E) mutation are associated with an increased age accelerationterm (Figure 9 A) whereas samples with a K-RASterm mutation have a decreased age accelerationterm (Figure 9 C). Echoing previous results, TP53 mutationsterm·var appear to be associated with decreased age accelerationterm (marginally significant P = 0.073; Figure 9 B). Promoter hypermethylation of the mismatch repair gene MLH1term leads to the most significant increase in age accelerationterm ( P = 5.7E-5; Figure 9 D), which supports the EMS modelterm·def of DNAm ageterm. The CpG island methylator phenotypeterm, defined by exceptionally high cancer-specific DNA hypermethylation [ 39 ], is also significantly ( P = 3.5E-5; Figure 9 F) associated with age accelerationterm, which may reflect its association with MLH1term hypermethylation and BRAFterm mutations.
Age accelerationterm in colorectal cancer, glioblastoma multiforme and acute myeloid leukemia. (A - F) Results for colorectal cancer. Mean age accelerationterm (y-axis) in colorectal cancer versus mutation status (denoted by a plus sign) in (A) BRAFterm , (B) TP53term , (C) K-RASterm . (D) Promoter hyper methylation of the mismatch repair gene MLH1term (denoted by a plus sign) is significantly ( P = 5.7E-5) associated with age accelerationterm. (E) Mean age accelerationterm across different patient groups defined by combinations of BRAFterm , TP53term , K-RASterm , MLH1term status. The first bar reports the age accelerationterm in normal adjacent colorectal tissue from cancer patients but the sample size of 4 is rather low. (F) CpG island methylator phenotypeterm is associated with age accelerationterm ( P = 3.5E-5). (G - R) Results for various genomic abnormalities in glioblastoma multiforme. (J) A highly significant ( P = 3.3E-7) relationship can be found between H3F3Aterm mutations and age accelerationterm. Samples with a G34R mutation have the highest age accelerationterm. (S - W) Results for various genomic aberrations in acute myeloid leukemia. (X) Thyroid cancer age accelerationterm versus RAS family mutation status is inconclusive since mutation status was largely unknown. Error bars represent 1 standard error.
DNAm ageterm in glioblastoma multiforme In general, the CpG island methylator phenotypeterm and age accelerationterm measure different properties as can be seen in GBM (Figure 9 M).
Interestingly, age accelerationterm in GBM samples is highly significantly ( P = 3.3E-7; Figure 9 J) associated with certain mutations in H3F3Aterm , which encodes the replication-independent histone variant H3.3. These mutations are single-nucleotide variants changing lysine 27 to methionine (K27M) or changing glycine 34 to arginine (G34R) [ 40 ]. The fact that GBMs with a G34R mutation in H3F3Aterm have a much higher age accelerationterm than those with a K27M mutation (Figure 9 J,L) makes sense since each H3F3Aterm mutation defines an epigenetic subgroup of GBM with a distinct global methylation pattern and acts through a different set of genes [ 40 ]. Lysine 27 is a critical residue of histone 3 variants, and methylation at this position (H3K27me), which may be mimicked by the terminal CH3 of methionine substituted at this residue [ 40 ], is commonly associated with transcriptional repression [ 41 ] while H3K36 methylation or acetylation typically promotes gene transcription [ 42 ]. G34-mutant cells exhibit increased RNA polymerase II binding, and increased gene expression, most notably that of the oncogene MYCN [ 43 ]. Both H3F3Aterm mutations are mutually exclusive with IDH1 mutations, which characterize a third mutation-defined subgroup [ 44 ]. Age accelerationterm in GBM samples is also associated with the following genomic aberrations: TP53 mutationterm·var, ATRX mutation, chromosome 7 gain, chromosome 10 loss, CDKN2A deletion, and EGFR amplification (Figure 9 G-I). Reflecting these results for individual markers, age accelerationterm varies significantly ( P = 2E-7; Figure 9 L) across the GBM subtypes defined in [ 44 ].
Acute myeloid leukemia Mutations in IDH1 (similar to the case of GBM), FLT , RAS , NPMc , and various well characterized translocations do not seem to relate to age accelerationterm in AML samples (Figure 9 S-W).
DNAm ageterm of cancer cell linesterm Using seven publicly available cell line data sets (Additional files 12 and 13 ), I was able to estimate the DNAm ageterm of 59 different cancer cell linesterm (from bladder, breast, gliomas, head/neck, leukemia, and osteosarcoma). Across all cell lines, DNAm ageterm does not have a significant correlation with the chronological ageterm of the patient from whom the cancer cell lineterm was derived (Additional file 18 B). However, a marginally significant age correlationterm can be observed across osteosarcoma cell lines (cor = 0.41, P = 0.08; Additional file 18 C). Overall, DNAm ageterm acceleration varies greatly across the cancer lines lines (Additional files 18 A and 19 ): the highest values can be observed for AML cell lines (KG1A, 182 years; HL-60, 177 years); the lowest values for head/neck squamous cell carcinoma cell line (UPCI SCC47, 6 years) and two breast cancer cell linesterm (SK-BR-3, 8 years; MDA-MB-468, 11 years). It will be interesting to test whether DNAm ageterm relates to other characteristics of cancer cell linesterm.
Conclusions Through the generosity of hundreds of researchers, I was able to analyze an unprecedented collection of DNA methylationterm data from healthy tissues, cancer tissuesterm, and cancer cell linesterm. The healthy tissue data allowed me to develop a multi-tissue predictor of ageterm (mathematical details are provided in Additional file 2 ). An R software tutorial can be found in Additional file 20 (which requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input). The basic approach is to form a weighted average of the 353 clock CpGsterm, which is then transformed to DNAm ageterm using a calibration functionterm. The calibration functionterm reveals that the epigenetic clock has a high tick rate until adulthood, after which it slows to a constant tick rate.
I propose that DNAm ageterm measures the cumulative work done by an epigenetic maintenance systemterm. This EMS modelterm·def of DNAm ageterm leads to several testable model predictions that I validate using cancer data. But irrespective of the validity of the EMS modelterm·def, the findings in cancer are interesting in their own right. While all cancer tissuesterm exhibit signs of severe age accelerationterm, this is not necessarily the case for individual cancer cell linesterm. Overall, high age accelerationterm is associated with fewer somatic mutationsterm in cancer tissueterm. Mutations in TP53term are associated with lower DNAm ageterm. To provide a glimpse of how DNAm ageterm can inform cancer research, I relate it to several widely used genomic aberrations in breast cancer, colorectal cancer, GBM, and AML.
DNAm ageterm is arguably a promising marker for studying human development, aging, and cancer. It may become a useful surrogate marker for evaluating rejuvenation therapies. The most salient feature of DNAm ageterm is its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age due to disease (for example, cancer). It remains to be seen whether the DNAm ageterm of easily accessible fluids/tissues (for example, saliva, buccal cells, blood, skin) can serve as a surrogate marker for inaccessible tissues (for example, brain, kidney, liver). It is noteworthy that DNAm ageterm is applicable to chimpanzee tissuesterm. Given the high heritability of age accelerationterm in young subjects, I expect that age accelerationterm will mainly be a relevant measure in older subjects. Using a relatively small data set, I did not find any evidence that a premature aging disease (progeria) is associated with accelerated DNAm ageterm (Figure 2 T). In Additional file 2 , I discuss whether DNAm ageterm fulfils the biomarker criteria developed by the American Federation for Aging Research.
Future research will need to clarify whether DNAm ageterm is only a marker of aging or relates to an effector of aging. In conclusion, the epigenetic clock described here is likely to become a valuable addition to the telomere clock.
Materials and methods Definition of DNAm ageterm using a penalized regression modelterm Using the training dataterm sets, I used a penalized regression modelterm (implemented in the R package glmnetterm [ 45 ]) to regress a calibrated version of chronological ageterm on 21,369 CpG probes that a) were present both on the Illumina 450Kterm and 27K platform and b) had fewer than 10 missing values. The alpha parameter of glmnetterm was chosen to 0.5 (elastic netterm regression) and the lambda value was chosen using cross-validation on the training dataterm (lambda = 0.0226). DNAm ageterm was defined as predicted ageterm. Mathematical details are provided in Additional file 2 .
Short description of the healthy tissue data sets All data are publicly available (Additional file 1 ). Many data sets involve normal adjacent tissue from TCGA. Details on the individual data sets can be found in Additional file 2 . To give credit to the many researchers who generated the data, I briefly mention relevant citations. Data sets 1 and 2 (whole blood samples from a Dutch population) were generated by Roel Ophoff and colleagues [ 14 ]. Data set 3 (whole blood) consists of whole blood samples from a recent large scale study of healthy individuals [ 24 ]. The authors used these and other data to estimate human aging rates and developed a highly accurate predictor of age based on blood data. Data set 4 consists of leukocyte samples from healthy male children from Children’s Hospital Boston [ 46 ]. Data set 5 consists of peripheral blood leukocyte samples [ 47 ]. Data set 6 consists of cord blood samples from newborns [ 30 ]. Data set 7 consists of cerebellum samples, which were provided by C Liu and C Chen (Gene Expression Omnibus (GEO) identifier GSE38873 ). Data sets 8, 9, 10, and 13 consist of cerebellum, frontal cortex, pons, and temporal cortex samples, respectively, obtained from the same subjects [ 48 ]. Data set 11 consists of prefrontal cortex samples from healthy controls [ 22 ]. Data set 12 consists of neuron and glial cell samples from [ 49 ]. Data set 14 consists of normal breast tissue samples [ 50 ]. Data set 15 consists of buccal cells from 109 15-year-old adolescents from a longitudinal study of child development [ 51 ]. Data set 16 consists of buccal cells from eight different subjects [ 15 ]. Data set 17 consists of buccal cells from monozygotic (MZ) and dizygotic (DZ) twin pairs from the Peri/postnatal Epigenetic Twins Study (PETS) cohort [ 52 ]. Data set 18 consists of cartilage (chondrocyte) samples from [ 53 ]. Data set 19 normal consists of adjacent colon tissue from TCGA. Data set 20 consists of colon mucosa samples from [ 54 ]. Data set 21 consists of dermal fibroblast samples from [ 21 ]. Data set 22 consists of epidermis samples from [ 55 ]. Data set 23 consists of gastric tissue samples from [ 56 ]. Data set 24 consists of head/neck normal adjacent tissue samples from TCGA (HNSC data). Data set 25 consists of heart tissue samples from [ 57 ]. Data set 26 consists of normal adjacent renal papillary tissue from TCGA (KIRP data). Data sets 27 consists of normal adjacent tissue from TCGA (KIRC data). Data set 28 consists of normal adjacent liver samples from [ 58 ]. Data set 29 consists of normal adjacent lung tissue from TCGA (LUSC data). Data set 30 consists of normal adjacent lung tissue samples from TCGA (LUAD data). Data set 31 is from TCGA (LUSC). Data set 32 consists of mesenchymal stromal cells isolated from bone marrow [ 59 ]. Data set 33 consists of placenta samples from mothers of monozygotic and dizygotic twins [ 60 ]. Data set 34 consists of prostate samples from [ 61 ]. Data set 35 consists of normal adjacent prostate tissue from TCGA (PRAD data). Data set 36 consists of male saliva samples from [ 62 ]. Data set 37 consists of male saliva samples from [ 23 ]. Data set 38 consists of stomach from TCGA (STAD data). Data set 39 consists of thyroid TCGA (THCA data). Data set 40 consists of whole blood from type 1 diabetics [ 10 , 63 ]. Data set 41 consists of whole blood from [ 15 ]. Data sets 42 and 43 consist of involve whole blood samples from women with ovarian cancer and healthy controls, respectively; these are the samples from the United Kingdom Ovarian Cancer Population Study [ 10 , 63 ]. Data set 44 consists of whole blood from [ 64 ]. Data set 45 consists of leukocytes from healthy children of the Simons Simple Collection [ 46 ]. Data set 46 consists of peripheral blood mononuclear cells from [ 65 ]. Data set 47 consists of peripheral blood mononuclear cells from [ 66 ]. Data set 48 consists of cord blood samples from newborns provided by N Turan and C Sapienza (GEO GSE36812 ). Data set 49 consists of cord blood mononuclear cells from [ 67 ]. Data set 50 consists of cord blood mononuclear cells from [ 60 ]. Data set 51 consists of CD4 T cells from infants [ 68 ]. Data set 52 consists of CD4+ T cells and CD14+ monocytes from [ 15 ]. Data set 53 consists of immortalized B cells and other cells from progeria, Werner syndrome patients, and controls [ 69 ]. Data sets 54 and 55 are brain samples from [ 70 ]. Data sets 56 and 57 consist of breast tissue from TCGA (27K and 450K platforms, respectively). Data set 58 consists of buccal cells from [ 71 ]. Data set 59 consists of colon from TCGA (COAD data). Data set 60 consists of fat (adipose) tissue from [ 72 ]. Data set 61 consists of human heart tissue from [ 27 ]. Data set 62 consists of kidney (normal adjacent) tissue from TCGA (KIRC). Data set 63 consists of liver (normal adjacent tissue) from TCGA (LIHC data). Data set 64 consists of lung from TCGA. Data set 65 consists of muscle tissue from [ 72 ]. Data set 66 consists of muscle tissue from [ 73 ]. Data set 67 consists of placenta samples from [ 74 ]. Data set 68 consists of female saliva samples [ 62 ]. Data set 69 consists of uterine cervix samples from [ 50 , 75 ]. Data set 70 consists of uterine endometrium (normal adjacent) tissue from TCGA (UCEC data). Data set 71 consists of various human tissues from the ENCODE/HAIB Project (GEO GSE40700 ). Data set 72 consists of chimpanzeeterm and human tissues from [ 27 ]. Data set 73 consists of great ape blood samples from [ 28 ]. Data set 74 consists of sperm samples from [ 76 ]. Data set 75 consists of sperm samples from [ 77 ]. Data set 76 consists of vascular endothelial cells from human umbilical cords from [ 60 ]. Data sets 77 and 78 (special cell types) involve human embryonicterm stem cells, iPS cellsterm, and somatic cell samples measured on the Illumina 27Kterm array and Illumina 450Kterm array, respectively [ 78 ]. Data set 79 consists of reprogrammed mesenchymal stromal cells from human bone marrow (iP-MSC), initial mesenchymal stromal cells, and embryonicterm stem cells [ 79 ]. Data set 80 consists of human ES cellsterm and normal primary tissue from [ 80 ]. Data set 81 consists of human ES cellsterm from [ 81 ]. Data set 82 consists of blood cell type data from [ 82 ].
Description of the cancer data sets An overview of the cancer tissueterm and cancer cell lineterm data sets is provided in Additional file 12 . More details can be found in Additional file 2 .
All data are publicly available as can be seen from the column that reports GSE identifiers from the GEO database and other online resources. Most cancer data sets came from TCGA. Data set 3, GBM from [ 44 ]; data set 4, breast cancer from [ 83 ]; data set 5, breast cancer from [ 84 ]; data set 6, breast cancer from [ 50 ]; data set 10, colorectal cancer from [ 39 ]; data set 23, prostate cancer from [ 61 ]; data set 30, urothelial carcinoma from [ 85 ].
DNA methylationterm profiling and normalization steps All of the public Illumina DNA data were generated by following the standard protocol of Illumina methylation assays, which quantifies DNA methylation levelsterm by the β value. A detailed description of the pre-processing and data normalization steps is provided in Additional file 2 .
Meta analysis for measuring pure age effects (irrespective of tissue typeterm·var) I used the metaAnalysis R function in the WGCNA R package [ 86 ] to measure pure age effects (Additional file 9 ) as detailed in Additional file 2 .
Analysis of variance for measuring tissue variation To measure tissue effects in the training dataterm (Additional file 8 ), I used analysis of variance (ANOVA) to calculate an F statistic as follows. First, a multivariate regression model was used to regress each CpG (dependent variable) on age and tissue typeterm·var. The analysis adjusted for age since the different data sets have very different mean ages (Additional file 1 ). Next, ANOVA based on the multivariate regression model was used to calculate an F statistic, F.tissueTraining , for measuring the tissue effect in the training dataterm. This F statistic measures the tissue effect after adjusting for age in the training dataterm sets. I did not translate the F statistic into a corresponding P -value since the latter turned out to be extremely significant for most CpGs. Additional file 8 D shows that F.tissueTraining is highly correlated with an independent measure of tissue variance (defined using adult somatic tissues from data set 77).
Characterizing the CpGs using sequence properties I studied occupancy counts for Polycomb-group target (PCGT) genes since they have an increased chance of becoming methylated with age compared to non-targets [ 10 ]. Toward this end, I used the occupancy counts of Suz12, Eed, and H3K27me3 published in [ 87 ]. To obtain the protein binding site occupancy throughout the entire nonrepeat portion of the human genome, Lee et al . [ 87 ] isolated DNA sequences bound to a particular protein of interest (for example, Polycomb-group protein SUZ12) by immunoprecipitating that protein (chromatin immunoprecipitation) and subsequently hybridizing the resulting fragments to a DNA microarray. More details on the chromatin state data from [ 29 ] can be found in Additional file 2 .
Abbreviations AML: Acute myeloid leukemia; BLCA: Bladder urothelial carcinoma; CBMC: Cord blood mononuclear cell; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; COAD: Colon adenocarcinoma; EMS: Epigenetic maintenance systemterm; ER: Estrogen receptorterm; ES: Embryonicterm stem; GBM: Glioblastoma multiforme; GEO: Gene Expression Omnibus; HNSC: Head/neck squamous cell carcinoma; HUVEC: Cell- human umbilical vascular endothelial cells; iPS: Induced pluripotent stem; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LIHC: Liver hepatocellular carcinoma; LOO: Leave one data set out; MSC: Mesenchymal stromal cell; PR: Progesterone receptorterm; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SCM: Skin cutaneous melanoma; TCGA: The Cancer Genome Atlas; THCA: Thyroid carcinoma; UCEC: Uterine corpus endometrioid carcinoma.
Competing interests The Regents of the University of California is the sole owner of a provisional patent application directed at this invention for which SH is a named inventor.
Supplementary Material DNA methylationterm data involving healthy (non-cancer) tissue. The rows correspond to 82 publicly available Illumina data sets. Column 1 reports the data set number and corresponding color code. Other columns report the source of the DNA (for example, tissue), Illumina platform, sample size n, proportion of females, median age, age range (minimum and maximum age), relevant citation (first author and publication year), public availability (for example, GEO identifier). The column 'Data Use’ reports whether the data set was used as a training set, test set, or served another purpose. The table also reports the age correlationterm, Cor(Age, DNAmAge), median errorterm·var, and median age accelerationterm for DNAm ageterm. The last two columns of the table report the age correlationterm (Cor LOOCVterm) and median errorterm·var (Error LOOCVterm) resulting from a leave-one-data-set-out cross-validationterm analysis.
Click here for file Materials and methods supplement. This document has the following sections: Limitations; Description of the healthy tissue and cell line data sets; Criteria guiding the choice of the training sets; Description of the cancer data sets; DNAm profiling and pre-processing steps; Normalization methods for the DNA methylationterm data; Explicit details on the definition of DNAm ageterm; Chromatin state data used for Additional file 9 ; Comparing the multi-tissue predictor with other age predictors; Meta analysis for finding age-related CpGs; Variation of age related CpGs across somatic tissues; Studying age effects using gene expression data; Meta-analysis applied to gene expression data; Names of the genes whose mutations are associated with age accelerationterm; Is DNAm ageterm a biomarker of aging?
Click here for file Coefficient values for the DNAm ageterm predictor. This Excel file provides detailed information on the multi-tissue age predictorterm defined using the training set data. The multi-tissue age predictorterm uses 353 CpGs, of which 193 and 160 have positive and negative correlations with age, respectively. The table also represents the coefficient values for the shrunkenterm age predictor that is based on a subset of 110 CpGs (a subset of the 353 CpGs). Although this information is sufficient for predicting age, I recommend using the R software tutorial since it implements the normalization method. The table reports a host of additional information for each CpG, including its variance, minimum value, maximum value, and median value across all training and test dataterm. Further, it reports the median beta value in subjects aged younger than 35 years and in subjects older than 55 years.
Click here for file Age predictionsterm in blood data sets. (A) DNAm ageterm has a high correlation with chronological ageterm (y-axis) across all blood data sets. (B-S) Results for individual blood data sets. The negligible age correlationterm in panel 0) reflects very young subjects that were either zero or 0.75 years (9 months) old. (S) DNAm ageterm in different cord blood data sets (x-axis). Bars report the mean DNAm ageterm (±1 standard error). The mean DNAm ageterm in data sets 6 and 50 is close to its expected value (zero) and it is not significantly different from zero in data set 48. (T) Mean DNAm ageterm across whole blood, peripheral blood mononuclear cells, granulocytes as well as seven isolated cell populations (CD4+ T cells, CD8+ T cells, CD56+ natural killer cells, CD19+ B cells, CD14+ monocytes, neutrophils, and eosinophils) from healthy male subjects [ 82 ]. The red vertical line indicates the average age across subjects. No significant difference in DNAm ageterm could be detected between these groups, but note the relatively small group sizes (indicated by the grey numbers on the y-axis).
Click here for file Age predictionsterm in brain data sets. (A) Scatter plot showing that DNAm ageterm (defined using the training set CpGs) has a high correlation (cor = 0.96, error = 3.2 years) with chronological ageterm (y-axis) across all training and test dataterm sets. (B-J) Results in individual brain data sets. (G) The brain samples of data set 12 are composed of 58 glial cell (labeled G, blue color), 58 neuron cell (labeled N, red color), 20 bulk (labeled B, turquoise), and 9 mixed samples (labeled M, brown). (K) Comparison of mean DNAm agesterm (horizontal bars) across different brain regions from the same subjects [ 48 ] reveals no significant difference between temporal cortex, pons, frontal cortex, and cerebellum. Differing group sizes (grey numbers on the y-axis) reflect that some suspicious samples were removed in an unbiased fashion (Additional file 2 ). (L) Using data sets 54 and 55, I found no significant difference in DNAm ageterm (x-axis) between cerebellum and occipital cortex from the same subjects [ 70 ].
Click here for file Age predictionsterm in breast data sets. (A) DNAm ageterm is highly correlated with age across all breast data sets, but the high error of 12 years reflects accelerated aging in normal adjacent breast cancer tissueterm (data sets 56, 57). (B-D) Relationship between DNAm ageterm and chronological ageterm in individual data sets. As expected, the lowest error (8.9 years) is observed in normal breast tissue (training dataterm set 14, panel (B)).
Click here for file Ingenuity Pathway Analysis. The document describes the results from applying Ingenuity Pathway Analysis to the 353 genes that are located near the 353 clock CpGsterm. Top biological function analysis implicated cell death/survival (74 genes, P = 1.1E-7) and cellular growth/development (71 genes, P = 3.7E-5). Significant overlap can be observed for the following disease-related gene sets: cancer (109 genes, P = 9.2E-5), endocrine system disorder (28, P = 2.6E-4), hereditary disorders (50 genes, 2.6E-4), and reproductive system disease (37 genes, P = 2.6E-4). Significant Ingenuity networks include a) hematological system development, tissue morphology, cell death and survival ( P = E-37), b) cellular growth and proliferation, cell signaling, developmental disorder ( P = E-37).
Click here for file Marginal analysis of CpGs. The figure shows how individual CpGs (corresponding to points) relate to age and tissue variation. Red and blue points correspond the 193 positively and the 160 negatively related clock CpGsterm, respectively. (A) The variance across adult somatic tissues is highly correlated with variance across fetal somatic tissues, which illustrates that it is robustly defined. Note that data set 77 [ 78 ] was not used for defining DNAm ageterm. (B,C) Average variance of DNAm levels across adult and fetal somatic tissues, respectively. The blue and red bars correspond to groups of positively and negatively related clock CpGsterm, respectively. (D) Tissue variance across the training dataterm (F statistic from ANOVA) is highly correlated (cor = 0.73) with tissue variance across adult somatic tissues (data set 77), which illustrates that tissue variance is robustly defined. (E) Pure (unconfounded) age effects in the training dataterm (x-axis) relate to those in all data sets (y-axis). To estimate pure age effects, I used a meta-analysis method that implicitly conditions on data set (Materials and methods; Additional file 2 ). The logarithm (base 10) of the meta-analysis P -value was multiplied by -1 or 1 so that high positive (negative) values indicate that the CpG is positively (negatively) correlated with age. The high correlation illustrates that little information is lost by focusing on the training dataterm. Further, note that the most significantly positively (red dots) and negatively related CpGsterm (blue dots) are used in the epigenetic clock. (F) Tissue variance in the training dataterm (y-axis) versus the signed logarithm of the meta-analysis P -value in the training dataterm (x-axis).
Click here for file Characterizing the clock CpGsterm using DNA sequence properties. Figure titles are preceded by ' + ’ or '-’ if they report properties of positively related or negatively related clock CpGsterm, respectively. Panels in the first row (A-E) relate the clock CpGsterm to chromatin state annotation provided in [ 29 ]. The y-axis reports the mean number of cell lines (out of 9 cell lines) for which the CpGs were in the chromatin state mentioned in the title. (A) The bar plots shows that the 193 positively related CpGsterm were significantly ( P = 1.6E-6) less likely to be in chromatin state 1 (active promoters) than the other 21k CpGs, which is not the case for the 160 negatively related CpGsterm (C). (B) Positively related CpGsterm were more likely to be in chromatin state 3 regions (poised promotersterm). (D) Negatively related CpGsterm were more likely to be in chromatin states 2 (weak promotersterm). (E) Negatively related CpGterm are often located chromatin state 4 regions (strong enhancersterm). (F) No significant relationship with CpG island status can be observed for the positively related CpGsterm. (K) Negatively related CpGsterm are significantly over-represented in shores. (G) Positively related CpGsterm were outside of RNApol2 bound regions (annotation from [ 87 ]). This is not the case for negatively related CpGsterm (L). (H-J) Positively related CpGsterm are over-represented near Polycomb-group target genesterm, that is, in regions with high occupancy of Suz12 ( P = 7.1E-6, H), EED ( P = 0.0030, I), and H3K27m3 ( P = 0.0048, J). This is not the case for the negatively related CpGsterm (M-O).
Click here for file Estimating the heritability of age accelerationterm. Two twin data sets (data sets 41 and 50) are used to estimate the broad sense heritabilityterm of accelerated age (defined as difference between DNAm ageterm and chronological ageterm). (A,E) Age histograms for data set 41 (median age 63 years, all females) and data set 50 (composed of newborns), respectively. (B,F) All twins irrespective of zygosity. Each point corresponds to a twin pair and is colored red if the twins are monozygotic. Age accelerationterm of the first twin (randomly chosen) versus that in the second twin, respectively. (C,G) Monozygotic twins only. (D,H) Dizygotic twins only. The high correlations in monozygotic twins (cor = 0.4 for data set 41 and cor = 0.77 for data set 50) contrast sharply with those observed for dizygotic twins (cor = 0.20 and cor = -0.21).
Click here for file Aging effects in gene expression (mRNA) and DNAm data. Due to space limitations, I can only report results for the direct approach of matching each individual CpG to its corresponding gene symbol. Using publicly available gene expression data (Additional file 2 ), I do not find a significant relationship between age effects on messenger RNA levels and age effects on DNAm levels in (A) blood, (C) brain, (E) kidney, (G) muscle, and (I) CD8 T cells. For each data modality, I estimated 'pure’ age effect using a meta-analysis method that conditioned on data (as described in Additional file 2 ). The y-axis reports a signed logarithm (base 10) of the meta-analysis P -value, that is, a high positive (negative) value indicates that the gene expression levelterm increases (decreases) with age. Gene expression data and CpG data were matched according to gene symbol as described in [ 88 ]. Each point in the scatter plots corresponds to a CpG (x-axis) and the corresponding gene symbol (y-axis). Genes corresponding to the positively related and negatively related clock CpGsterm are colored in red and blue, respectively. (B,D,F,H,J,L) Mean age effect (y-axis) across gene groups defined by their corresponding CpG. (K,L) Aging effects on DNAm levels (x-axis) do not affect genes known to be differentially expressed between naive CD8 T cells and CD8 memory cells. The y-axis reports the signed logarithm of the Student t -test P -value of differential expression.
Click here for file Description of cancer data sets. The file describes 32 publicly available cancer tissueterm data sets and 7 cancer cell lineterm data sets. Column 1 reports the data number and corresponding color code. Other columns report the affected tissue, Illumina platform, sample size n, proportion of females, median age, age range (minimum and maximum age), relevant citation (TCGA or first author with publication year), and public availability. None of these data sets were used in the construction of estimator of DNAm ageterm. The table also reports the age correlationterm, cor(Age,DNAmage), median errorterm·var, and median age accelerationterm.
Click here for file DNAm ageterm versus chronological ageterm in cancer. Each point corresponds to a DNA methylationterm sample (cancer sampleterm from a human subject). Points are colored and labeled according to the underlying cancer data sets as described in Additional file 12 . (A) Across all cancer data sets, there is only a weak correlation (cor = 0.15, P = 1.9E-29) between DNAm ageterm (x-axis) and chronological patient age (y-axis). The high error (40 years) reflects high age accelerationsterm. (B) Each cancer/affected tissue shows evidence of significant age accelerationterm (y-axis) with an average age accelerationterm of 36.2 years. (C-W) Results for individual cancers/affected tissues. Several cancer tissuesterm maintain moderately large age correlationsterm (larger than 0.3), including brain (cor = 0.61) (E), thyroid (cor = 0.6) (U), kidney (cor = 0.45) (K,L), liver (cor = 0.42) (M), colorectal (cor = 0.37) (I), and breast (cor = 0.31) (F).
Click here for file Age accelerationterm versus tumor grade and stage. Panels correspond to the cancer data sets described in Additional file 12 . Nominally significant negative correlations between grade and age accelerationterm can be observed in ovarian serous cystadenocarcinoma (panel G; P = 0.032) and uterine corpus endometroids (panel J; P = 0.019). A nominally significant positive correlation between stage and age accelerationterm can be observed for colon adenocarcinoma (panel O; P = 0.021). Only the highly significant negative correlation between stage and age accelerationterm in thyroid cancer (panel Z; P = 8.7E-9) remains significant after adjusting for multiple comparisons. Since grade and stage are often considered as ordinal variables, correlation test P -values are reported in all panels except the last. (H) For prostate cancer, the x-axis reports the Gleason sum score. The last panel shows that mean age accelerationterm in acute myeloid leukemia is not significantly related to French American British (FAB) morphology but some groups (notably M6 and M7) are very small (rotated grey numbers).
Click here for file Age accelerationterm versus mutation count status in breast cancer. Mutation count status (x-axis) was defined by assigning tumor samples to the high mutation count group if their number of somatic mutationsterm was larger than 50. Other thresholds lead to similar results. (A-L) Findings for Illumina 27Kterm (A-F) and 450K data (G-L). (A,G) The barplots show that mean age accelerationterm (y-axis) is lower in breast cancer samplesterm with high mutation count (compared to those samples whose somatic mutationterm count is less than 50). This result can also be found in ER+ (B,H), ER- (C,I), PR + (D,J), PR- (E,K), and triple negative (F,L) breast cancer samplesterm.
Click here for file Selected significant gene mutations versus age accelerationterm. The TCGA data sets were stratified by cancer type and Illumina platform. Mean age accelerationterm (y-axis) versus mutation status (x-axis) for up to two of the most significant genes per data set. Note that age accelerationterm in bone marrow (AML) was most highly related to mutation in the following two genes: U2AF1 and TP53term . Age accelerationterm in the two breast cancer data sets was most highly related to mutations in GATA3 , TP53term , and TTN . For kidney renal cell carcinoma (KIRC): only AKAP9 was significant. Strikingly, TP53term was among the top two most significant mutated genes in 4 out of 13 cancer data sets. More information on these genes is presented in Additional file 2 .
Click here for file Effect of TP53 mutationterm·var on age accelerationterm. Mutations in TP53term are associated with significantly lower age accelerationterm in five cancers: including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian serous cystadenocarcinoma ( P = 0.03) (I), and uterine corpus endometrioid ( P = 0.00093). Marginally significant results could be observed in lung squamous cell carcinoma ( P = 0.047 for the 27K data but insignificant results for the 450K data).
Click here for file DNAm ageterm of cancer cell linesterm. (A) A high variation of DNAm ageterm (x-axis) can be observed across various cancer lines lines (y-axis). The DNAm ageterm is reported in Additional file 19 . (B) Across all cell lines, DNAm ageterm (x-axis) does not have a significant correlation with the chronological ageterm of the patient from whom the cancer cell lineterm was derived. (C) Results for osteosarcoma cell lines.
Click here for file Cancer lines and DNAm ageterm. This Excel file reports the DNAm ageterm and age accelerationterm for 59 cancer cell linesterm.
Click here for file R software tutorial. This file contains an R software tutorial that describes how to estimate DNAm ageterm for data set 55. Further, it shows how to relate two measures of age accelerationterm to autism disease status. The R tutorial requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input.
Click here for file Probe annotation file for the Illumina 27Kterm array. This comma-delimited text file (.csv file) is needed for the R software tutorial.
Click here for file Additional probe annotation file for the R tutorial. This comma-delimited text file (.csv file) is needed for the R software tutorial.
Click here for file Coefficient values of the age predictor. This comma-delimited text file (.csv file) is needed for the R software tutorial. This file is very similar to Additional file 3 but rows appear in a different order.
Click here for file R code for normalizing the DNA methylationterm data. This text file is needed for the R software tutorial. It contains R code for normalizing the DNA methylationterm data and adapts R functions described in [ 89 ].
Click here for file This text file is needed for the R software tutorial. It contains R code implementing analysis steps.
Click here for file Methylation data from data set 55. This comma-delimited text file (.csv file) contains the DNA methylationterm data needed for the R software tutorial.
Click here for file This comma-delimited text file (.csv file) contains the sample annotation data needed for the R software tutorial.
Click here for file Acknowledgements I gratefully acknowledge the many researchers who made their DNA-methylation datasets publicly available and responded to my email requests. This study would not have been possible without the valuable data from The Cancer Genome Atlas. I gratefully acknowledge discussions with Prof. Darryl Shibata from the University of Southern California about mitotic ageterm. No specific funding was received for this work.
References Associated Data Supplementary Materials DNA methylationterm data involving healthy (non-cancer) tissue. The rows correspond to 82 publicly available Illumina data sets. Column 1 reports the data set number and corresponding color code. Other columns report the source of the DNA (for example, tissue), Illumina platform, sample size n, proportion of females, median age, age range (minimum and maximum age), relevant citation (first author and publication year), public availability (for example, GEO identifier). The column 'Data Use’ reports whether the data set was used as a training set, test set, or served another purpose. The table also reports the age correlationterm, Cor(Age, DNAmAge), median errorterm·var, and median age accelerationterm for DNAm ageterm. The last two columns of the table report the age correlationterm (Cor LOOCVterm) and median errorterm·var (Error LOOCVterm) resulting from a leave-one-data-set-out cross-validationterm analysis.
Click here for file Materials and methods supplement. This document has the following sections: Limitations; Description of the healthy tissue and cell line data sets; Criteria guiding the choice of the training sets; Description of the cancer data sets; DNAm profiling and pre-processing steps; Normalization methods for the DNA methylationterm data; Explicit details on the definition of DNAm ageterm; Chromatin state data used for Additional file 9 ; Comparing the multi-tissue predictor with other age predictors; Meta analysis for finding age-related CpGs; Variation of age related CpGs across somatic tissues; Studying age effects using gene expression data; Meta-analysis applied to gene expression data; Names of the genes whose mutations are associated with age accelerationterm; Is DNAm ageterm a biomarker of aging?
Click here for file Coefficient values for the DNAm ageterm predictor. This Excel file provides detailed information on the multi-tissue age predictorterm defined using the training set data. The multi-tissue age predictorterm uses 353 CpGs, of which 193 and 160 have positive and negative correlations with age, respectively. The table also represents the coefficient values for the shrunkenterm age predictor that is based on a subset of 110 CpGs (a subset of the 353 CpGs). Although this information is sufficient for predicting age, I recommend using the R software tutorial since it implements the normalization method. The table reports a host of additional information for each CpG, including its variance, minimum value, maximum value, and median value across all training and test dataterm. Further, it reports the median beta value in subjects aged younger than 35 years and in subjects older than 55 years.
Click here for file Age predictionsterm in blood data sets. (A) DNAm ageterm has a high correlation with chronological ageterm (y-axis) across all blood data sets. (B-S) Results for individual blood data sets. The negligible age correlationterm in panel 0) reflects very young subjects that were either zero or 0.75 years (9 months) old. (S) DNAm ageterm in different cord blood data sets (x-axis). Bars report the mean DNAm ageterm (±1 standard error). The mean DNAm ageterm in data sets 6 and 50 is close to its expected value (zero) and it is not significantly different from zero in data set 48. (T) Mean DNAm ageterm across whole blood, peripheral blood mononuclear cells, granulocytes as well as seven isolated cell populations (CD4+ T cells, CD8+ T cells, CD56+ natural killer cells, CD19+ B cells, CD14+ monocytes, neutrophils, and eosinophils) from healthy male subjects [ 82 ]. The red vertical line indicates the average age across subjects. No significant difference in DNAm ageterm could be detected between these groups, but note the relatively small group sizes (indicated by the grey numbers on the y-axis).
Click here for file Age predictionsterm in brain data sets. (A) Scatter plot showing that DNAm ageterm (defined using the training set CpGs) has a high correlation (cor = 0.96, error = 3.2 years) with chronological ageterm (y-axis) across all training and test dataterm sets. (B-J) Results in individual brain data sets. (G) The brain samples of data set 12 are composed of 58 glial cell (labeled G, blue color), 58 neuron cell (labeled N, red color), 20 bulk (labeled B, turquoise), and 9 mixed samples (labeled M, brown). (K) Comparison of mean DNAm agesterm (horizontal bars) across different brain regions from the same subjects [ 48 ] reveals no significant difference between temporal cortex, pons, frontal cortex, and cerebellum. Differing group sizes (grey numbers on the y-axis) reflect that some suspicious samples were removed in an unbiased fashion (Additional file 2 ). (L) Using data sets 54 and 55, I found no significant difference in DNAm ageterm (x-axis) between cerebellum and occipital cortex from the same subjects [ 70 ].
Click here for file Age predictionsterm in breast data sets. (A) DNAm ageterm is highly correlated with age across all breast data sets, but the high error of 12 years reflects accelerated aging in normal adjacent breast cancer tissueterm (data sets 56, 57). (B-D) Relationship between DNAm ageterm and chronological ageterm in individual data sets. As expected, the lowest error (8.9 years) is observed in normal breast tissue (training dataterm set 14, panel (B)).
Click here for file Ingenuity Pathway Analysis. The document describes the results from applying Ingenuity Pathway Analysis to the 353 genes that are located near the 353 clock CpGsterm. Top biological function analysis implicated cell death/survival (74 genes, P = 1.1E-7) and cellular growth/development (71 genes, P = 3.7E-5). Significant overlap can be observed for the following disease-related gene sets: cancer (109 genes, P = 9.2E-5), endocrine system disorder (28, P = 2.6E-4), hereditary disorders (50 genes, 2.6E-4), and reproductive system disease (37 genes, P = 2.6E-4). Significant Ingenuity networks include a) hematological system development, tissue morphology, cell death and survival ( P = E-37), b) cellular growth and proliferation, cell signaling, developmental disorder ( P = E-37).
Click here for file Marginal analysis of CpGs. The figure shows how individual CpGs (corresponding to points) relate to age and tissue variation. Red and blue points correspond the 193 positively and the 160 negatively related clock CpGsterm, respectively. (A) The variance across adult somatic tissues is highly correlated with variance across fetal somatic tissues, which illustrates that it is robustly defined. Note that data set 77 [ 78 ] was not used for defining DNAm ageterm. (B,C) Average variance of DNAm levels across adult and fetal somatic tissues, respectively. The blue and red bars correspond to groups of positively and negatively related clock CpGsterm, respectively. (D) Tissue variance across the training dataterm (F statistic from ANOVA) is highly correlated (cor = 0.73) with tissue variance across adult somatic tissues (data set 77), which illustrates that tissue variance is robustly defined. (E) Pure (unconfounded) age effects in the training dataterm (x-axis) relate to those in all data sets (y-axis). To estimate pure age effects, I used a meta-analysis method that implicitly conditions on data set (Materials and methods; Additional file 2 ). The logarithm (base 10) of the meta-analysis P -value was multiplied by -1 or 1 so that high positive (negative) values indicate that the CpG is positively (negatively) correlated with age. The high correlation illustrates that little information is lost by focusing on the training dataterm. Further, note that the most significantly positively (red dots) and negatively related CpGsterm (blue dots) are used in the epigenetic clock. (F) Tissue variance in the training dataterm (y-axis) versus the signed logarithm of the meta-analysis P -value in the training dataterm (x-axis).
Click here for file Characterizing the clock CpGsterm using DNA sequence properties. Figure titles are preceded by ' + ’ or '-’ if they report properties of positively related or negatively related clock CpGsterm, respectively. Panels in the first row (A-E) relate the clock CpGsterm to chromatin state annotation provided in [ 29 ]. The y-axis reports the mean number of cell lines (out of 9 cell lines) for which the CpGs were in the chromatin state mentioned in the title. (A) The bar plots shows that the 193 positively related CpGsterm were significantly ( P = 1.6E-6) less likely to be in chromatin state 1 (active promoters) than the other 21k CpGs, which is not the case for the 160 negatively related CpGsterm (C). (B) Positively related CpGsterm were more likely to be in chromatin state 3 regions (poised promotersterm). (D) Negatively related CpGsterm were more likely to be in chromatin states 2 (weak promotersterm). (E) Negatively related CpGterm are often located chromatin state 4 regions (strong enhancersterm). (F) No significant relationship with CpG island status can be observed for the positively related CpGsterm. (K) Negatively related CpGsterm are significantly over-represented in shores. (G) Positively related CpGsterm were outside of RNApol2 bound regions (annotation from [ 87 ]). This is not the case for negatively related CpGsterm (L). (H-J) Positively related CpGsterm are over-represented near Polycomb-group target genesterm, that is, in regions with high occupancy of Suz12 ( P = 7.1E-6, H), EED ( P = 0.0030, I), and H3K27m3 ( P = 0.0048, J). This is not the case for the negatively related CpGsterm (M-O).
Click here for file Estimating the heritability of age accelerationterm. Two twin data sets (data sets 41 and 50) are used to estimate the broad sense heritabilityterm of accelerated age (defined as difference between DNAm ageterm and chronological ageterm). (A,E) Age histograms for data set 41 (median age 63 years, all females) and data set 50 (composed of newborns), respectively. (B,F) All twins irrespective of zygosity. Each point corresponds to a twin pair and is colored red if the twins are monozygotic. Age accelerationterm of the first twin (randomly chosen) versus that in the second twin, respectively. (C,G) Monozygotic twins only. (D,H) Dizygotic twins only. The high correlations in monozygotic twins (cor = 0.4 for data set 41 and cor = 0.77 for data set 50) contrast sharply with those observed for dizygotic twins (cor = 0.20 and cor = -0.21).
Click here for file Aging effects in gene expression (mRNA) and DNAm data. Due to space limitations, I can only report results for the direct approach of matching each individual CpG to its corresponding gene symbol. Using publicly available gene expression data (Additional file 2 ), I do not find a significant relationship between age effects on messenger RNA levels and age effects on DNAm levels in (A) blood, (C) brain, (E) kidney, (G) muscle, and (I) CD8 T cells. For each data modality, I estimated 'pure’ age effect using a meta-analysis method that conditioned on data (as described in Additional file 2 ). The y-axis reports a signed logarithm (base 10) of the meta-analysis P -value, that is, a high positive (negative) value indicates that the gene expression levelterm increases (decreases) with age. Gene expression data and CpG data were matched according to gene symbol as described in [ 88 ]. Each point in the scatter plots corresponds to a CpG (x-axis) and the corresponding gene symbol (y-axis). Genes corresponding to the positively related and negatively related clock CpGsterm are colored in red and blue, respectively. (B,D,F,H,J,L) Mean age effect (y-axis) across gene groups defined by their corresponding CpG. (K,L) Aging effects on DNAm levels (x-axis) do not affect genes known to be differentially expressed between naive CD8 T cells and CD8 memory cells. The y-axis reports the signed logarithm of the Student t -test P -value of differential expression.
Click here for file Description of cancer data sets. The file describes 32 publicly available cancer tissueterm data sets and 7 cancer cell lineterm data sets. Column 1 reports the data number and corresponding color code. Other columns report the affected tissue, Illumina platform, sample size n, proportion of females, median age, age range (minimum and maximum age), relevant citation (TCGA or first author with publication year), and public availability. None of these data sets were used in the construction of estimator of DNAm ageterm. The table also reports the age correlationterm, cor(Age,DNAmage), median errorterm·var, and median age accelerationterm.
Click here for file DNAm ageterm versus chronological ageterm in cancer. Each point corresponds to a DNA methylationterm sample (cancer sampleterm from a human subject). Points are colored and labeled according to the underlying cancer data sets as described in Additional file 12 . (A) Across all cancer data sets, there is only a weak correlation (cor = 0.15, P = 1.9E-29) between DNAm ageterm (x-axis) and chronological patient age (y-axis). The high error (40 years) reflects high age accelerationsterm. (B) Each cancer/affected tissue shows evidence of significant age accelerationterm (y-axis) with an average age accelerationterm of 36.2 years. (C-W) Results for individual cancers/affected tissues. Several cancer tissuesterm maintain moderately large age correlationsterm (larger than 0.3), including brain (cor = 0.61) (E), thyroid (cor = 0.6) (U), kidney (cor = 0.45) (K,L), liver (cor = 0.42) (M), colorectal (cor = 0.37) (I), and breast (cor = 0.31) (F).
Click here for file Age accelerationterm versus tumor grade and stage. Panels correspond to the cancer data sets described in Additional file 12 . Nominally significant negative correlations between grade and age accelerationterm can be observed in ovarian serous cystadenocarcinoma (panel G; P = 0.032) and uterine corpus endometroids (panel J; P = 0.019). A nominally significant positive correlation between stage and age accelerationterm can be observed for colon adenocarcinoma (panel O; P = 0.021). Only the highly significant negative correlation between stage and age accelerationterm in thyroid cancer (panel Z; P = 8.7E-9) remains significant after adjusting for multiple comparisons. Since grade and stage are often considered as ordinal variables, correlation test P -values are reported in all panels except the last. (H) For prostate cancer, the x-axis reports the Gleason sum score. The last panel shows that mean age accelerationterm in acute myeloid leukemia is not significantly related to French American British (FAB) morphology but some groups (notably M6 and M7) are very small (rotated grey numbers).
Click here for file Age accelerationterm versus mutation count status in breast cancer. Mutation count status (x-axis) was defined by assigning tumor samples to the high mutation count group if their number of somatic mutationsterm was larger than 50. Other thresholds lead to similar results. (A-L) Findings for Illumina 27Kterm (A-F) and 450K data (G-L). (A,G) The barplots show that mean age accelerationterm (y-axis) is lower in breast cancer samplesterm with high mutation count (compared to those samples whose somatic mutationterm count is less than 50). This result can also be found in ER+ (B,H), ER- (C,I), PR + (D,J), PR- (E,K), and triple negative (F,L) breast cancer samplesterm.
Click here for file Selected significant gene mutations versus age accelerationterm. The TCGA data sets were stratified by cancer type and Illumina platform. Mean age accelerationterm (y-axis) versus mutation status (x-axis) for up to two of the most significant genes per data set. Note that age accelerationterm in bone marrow (AML) was most highly related to mutation in the following two genes: U2AF1 and TP53term . Age accelerationterm in the two breast cancer data sets was most highly related to mutations in GATA3 , TP53term , and TTN . For kidney renal cell carcinoma (KIRC): only AKAP9 was significant. Strikingly, TP53term was among the top two most significant mutated genes in 4 out of 13 cancer data sets. More information on these genes is presented in Additional file 2 .
Click here for file Effect of TP53 mutationterm·var on age accelerationterm. Mutations in TP53term are associated with significantly lower age accelerationterm in five cancers: including AML ( P = 0.0023), breast cancer ( P = 1.4E-5 and P = 3.7E-8), ovarian serous cystadenocarcinoma ( P = 0.03) (I), and uterine corpus endometrioid ( P = 0.00093). Marginally significant results could be observed in lung squamous cell carcinoma ( P = 0.047 for the 27K data but insignificant results for the 450K data).
Click here for file DNAm ageterm of cancer cell linesterm. (A) A high variation of DNAm ageterm (x-axis) can be observed across various cancer lines lines (y-axis). The DNAm ageterm is reported in Additional file 19 . (B) Across all cell lines, DNAm ageterm (x-axis) does not have a significant correlation with the chronological ageterm of the patient from whom the cancer cell lineterm was derived. (C) Results for osteosarcoma cell lines.
Click here for file Cancer lines and DNAm ageterm. This Excel file reports the DNAm ageterm and age accelerationterm for 59 cancer cell linesterm.
Click here for file R software tutorial. This file contains an R software tutorial that describes how to estimate DNAm ageterm for data set 55. Further, it shows how to relate two measures of age accelerationterm to autism disease status. The R tutorial requires Additional files 21 , 22 , 23 , 24 , 25 , 26 and 27 as input.
Click here for file Probe annotation file for the Illumina 27Kterm array. This comma-delimited text file (.csv file) is needed for the R software tutorial.
Click here for file Additional probe annotation file for the R tutorial. This comma-delimited text file (.csv file) is needed for the R software tutorial.
Click here for file Coefficient values of the age predictor. This comma-delimited text file (.csv file) is needed for the R software tutorial. This file is very similar to Additional file 3 but rows appear in a different order.
Click here for file R code for normalizing the DNA methylationterm data. This text file is needed for the R software tutorial. It contains R code for normalizing the DNA methylationterm data and adapts R functions described in [ 89 ].
Click here for file This text file is needed for the R software tutorial. It contains R code implementing analysis steps.
Click here for file Methylation data from data set 55. This comma-delimited text file (.csv file) contains the DNA methylationterm data needed for the R software tutorial.
Click here for file This comma-delimited text file (.csv file) contains the sample annotation data needed for the R software tutorial.
Click here for file