Region‐based epigenetic clock design improves RRBS‐based age prediction

Abstract

Recent studies suggest that epigenetic rejuvenation can be achieved using drugs that mimic calorie restriction and techniques such as reprogramming‐induced rejuvenation. To effectively test rejuvenation in vivo, mouse models are the safest alternative. However, we have found that the recent epigenetic clocks developed for mouse reduced‐representation bisulphite sequencing (RRBS) data have significantly poor performance when applied to external datasets. We show that the sites captured and the coverage of key CpGs required for age prediction vary greatly between datasets, which likely contributes to the lack of transferability in RRBS clocks. To mitigate these coverage issues in RRBS‐based age prediction, we present two novel design strategies that use average methylation over large regions rather than individual CpGs, whereby regions are defined by sliding windows (e.g. 5 kb), or density‐based clustering of CpGs. We observe improved correlation and error in our regional blood clocks (RegBCs) compared to published individual‐CpG‐based techniques when applied to external datasets. The RegBCs are also more robust when applied to low coverage data and detect a negative age acceleration in mice undergoing calorie restriction. Our RegBCs offer a proof of principle that age prediction of RRBS datasets can be improved by accounting for multiple CpGs over a region, which negates the lack of read depth currently hindering individual‐CpG‐based approaches.

Age Prediction Using DNA Methylation Heterogeneity Metrics

“…To date, several approaches have been described for constructing epigenetic clock algorithms based on RRBS data, the most productive being the analysis of average methylation over genomic regions of different sizes [13,19]. To compare the efficacy of this regional approach with the heterogeneity-based one, we built an epigenetic clock model that is similar to a previously described method for mice blood RRBS eAge clocks [13].…”

“…To date, several approaches have been described for constructing epigenetic clock algorithms based on RRBS data, the most productive being the analysis of average methylation over genomic regions of different sizes [13,19]. To compare the efficacy of this regional approach with the heterogeneity-based one, we built an epigenetic clock model that is similar to a previously described method for mice blood RRBS eAge clocks [13]. Briefly, the average methylation frequency over genomic windows of different sizes was calculated, windows containing methylation data were deduplicated, and age-correlated windows were used for further analysis (Table S9).…”

“…The main disadvantage of RRBS and WGBS is the non-uniform CpG-site coverage across samples due to the difference in sequencing depth and library preparation. The resulting inter-sample variation can be a source of uncertainty when a model built on one dataset is applied to separate samples [13]. Microarray DNA methylation data are considered standardized in this regard.…”

“…Given these advantages, these methods are now widely used to discriminate cell types, and they have proven to be beneficial for cancer research and "liquid biopsy" diagnostics using circulating plasma DNA samples [5,10,14,15]. However, WGBS and RRBS are rarely used for eAge clock development and analysis [13].…”

“…Nevertheless, the first step when building a model using sequencing data is to bring continuous methylation patterns from every individual DNA molecule to the array of scores. This straightforward solution, which relies on the aggregation of the average methylation levels of individual CpGs within overlapping reads in the manner of microarray data, has been used previously for constructing RRBS-based eAge models [13,19]. However, this approach completely loses information about the samplespecific diversity of methylation patterns.…”