Aging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting “epigenetic clock” closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons—the best predictor of reproductive success—imposes costs consistent with a “live fast, die young” life history strategy.

DNA methylation data were generated from blood-extracted DNA collected from known individuals in the Amboseli study population.  RRBS libraries were constructed via Msp1 digestion of~200 ng baboon DNA plus 0.2 ng unmethylated lambda phage DNA per sample as input . Samples were sequenced to a mean depth of 17.8 million reads on either the Illumina HiSeq 2000 or HiSeq 4000 platform. Sequence reads were trimmed with Trim Galore to remove adapters and low quality sequence (Phred score < 20). Trimmed reads were mapped with BSMAP to the baboon genome (Panu2.0) allowing a 10% mismatch rate to account for the degenerate composition of bisulfite-converted DNA. We used the mapped reads to count the number of methylated and total reads per CpG site, per sample. CpG sites were filtered to retain sites with a mean methylation level between 0.1 and 0.9 (i.e., to exclude constitutively hyper- or hypo-methylated sites) and mean coverage ≥5x. We also excluded any CpG sites with missing data for ≥ 5% of individuals in the sample. After filtering, we retained N = 458,504 CpG sites for downstream analysis. Included here are the effective total CT counts and methylated counts for all 277 samples (from 245 unique individuals) across each of these 458,504 CpG sites. 

total_read_counts

Number of reads mapped to a given location in the genome for a given sample. Sites included in this dataset represent the set of filtered sites we analyzed to produce our main results. Note that these values are the "effective CT count" values output from BSMAP, which attempts to correct for potential SNPs at measured CpG sites. In a small number of cases (<.1%), this measure results in an "effective CT count" value that is less than the methylated counts. Depending on the downstream handling of methylation values, and desired used of these data, these cases can be excluded, ignored, or one can simply set the "effective CT count"=methylated count, for those small subsets of sites. 

methylated_read_counts

Number of methylated reads mapped to a given location in the genome for a given sample. Sites included in this dataset represent the set of filtered sites we analyzed to produce our main results. The row and column dimensions of the methylated_read_count table matches the total_read_count table, such that corresponding values for the same CpG site and sample can be found in the same table cell.

counts_tables_columns

A file noting which sample appears in which column of the methylated and total read counts tables. The individual identifiers in this file are consistent with the identifiers in Table S1. Note that multiple samples from the same individuals can be identified via the Anonymous ID # (i.e. AMB_140 and AMB_140B came from the same individual sampled at different dates).