Recalibrating the Epigenetic Clock: Implications for Assessing Biological Age in the Human Cortex

Abstract

Human DNA-methylation data have been used to develop biomarkers of ageingreferred to 'epigenetic clocks' -that have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks are highly accurate in blood but are less precise when used in older samples or on brain tissue. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life-course (n = 1,397, ages = 1 to 104 years). This dataset was split into 'training' and 'testing' samples (training: n = 1,047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel human cortex dataset (n = 1,221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1,175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically out-performed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts. mortem 6 samples were split into a "training" dataset (used to determine the DNAm sites included in the model and their weighted coefficients) and a "testing" dataset (used to profile the performance of the proposed model). To reduce the effects of experimental batch in our model, we maximised the number of different datasets included in the training data by combining the ten cohorts and randomly assigning individuals within them to either the training or testing dataset in a 3:1 ratio ( Table 1). In total, our training dataset (age range = 1-108 years, median = 57 years; see Supplementary Fig. S1) comprised DNAm data from 1,047 cortex samples (derived from 832 donors) and our testing dataset (age range = 1-108 years, median = 56 years; see Supplementary Fig. S1) comprised DNAm data from 350 cortex samples (derived from 323 donors). Individuals with a diagnosis of Alzheimer's disease and other major neurological phenotypes were excluded from our analysis given the ...

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“…Finally, we had access to DNA methylation data generated in an eighth independent ("Brains for Dementia Research [BDR]") cohort. This consisted of Illumina Infinium HumanMethylation EPIC BeadChip (EPIC array) data in the prefrontal cortex in 590 individuals 15 . As this is the successor to the 450K array (which had been used for the other seven cohorts), there are some differences in genome coverage, and for the 220 Bonferroni significant cross-cortex DMPs we had identified in the discovery cohorts, only 208 probes are also present on the EPIC array.…”

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“…All statistical analyses were performed using R version 3.5.2 (https://www.r-project.org/). All datasets of which raw data was available were pre-processed by our group following a standard quality control (QC) and normalization pipeline as described before [14] using either the R package wateRmelon [19] or bigmelon [20]. Briefly, samples with low signal intensities or incomplete bisulphite conversion were excluded prior to applying the pfilter() function from the wateRmelon package excluding samples with >1 % of probes with a detection P value >0.05 and probes with >1 % of samples with detection P value >0.05.…”

“…Briefly, these data consist of 1,221 samples from 632 donors (age range 41-104 years, median = 84 years), with DNA extracted from the prefrontal cortex (n = 610) and occipital cortex (n = 611). DNA methylation was quantified using the Illumina EPIC DNA methylation array, and were pre-processed using our group's standard QC pipeline as described in [14].…”

“…While in general the MTC generates reliable predictions of chronological age for most samples, there is increasing recognition that its performance is dependent on the characteristics of the training dataset (e.g. tissue/cell type and age range) and greater accuracy can be achieved with clocks trained on more refined sample sets, such as those from a single tissue [14,15]. To this end, a number of new DNAm clocks have been established based on specific tissue types, like whole blood [16] or brain tissue [14], which demonstrate more accurate predictions within the specified tissue.…”

“…tissue/cell type and age range) and greater accuracy can be achieved with clocks trained on more refined sample sets, such as those from a single tissue [14,15]. To this end, a number of new DNAm clocks have been established based on specific tissue types, like whole blood [16] or brain tissue [14], which demonstrate more accurate predictions within the specified tissue. A less established refinement of epigenetics clocks is the application to specific developmental stages, with embryonic or fetal samples, in particular, either omitted from or underrepresented in most existing clocks.…”