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Trends in DNA methylation with age replicate across diverse human populations

By Shyamalika Gopalan, Oana Carja, Maud Fagny, Etienne Patin, Justin W Myrick, Lisa McEwen, Sarah M Mah, Michael S Kobor, Alain Froment, Marcus Feldman, Lluis Quintana-Murci, Brenna M. Henn

Posted 02 Sep 2016
bioRxiv DOI: 10.1101/073171 (published DOI: 10.1534/genetics.116.195594)

Aging is associated with widespread changes in genome-wide patterns of DNA methylation. Thousands of CpG sites whose tissue-specific methylation levels are strongly correlated with chronological age have been previously identified. However, the majority of these studies have focused primarily on cosmopolitan populations living in the developed world; it is not known if age-related patterns of DNA methylation at these loci are similar across a broad range of human genetic and ecological diversity. We investigated genome-wide methylation patterns using saliva and whole blood derived DNA from two traditionally hunting and gathering African populations: the Baka of the western Central African rainforest and the ≠Khomani San of the South African Kalahari Desert. We identify hundreds of CpG sites whose methylation levels are significantly associated with age, thousands that are significant in a meta-analysis, and replicate trends previously reported in populations of non-African descent. We confirm that an age-associated site in the gene ELOVL2 shows a remarkably congruent relationship with aging in humans, despite extensive genetic and environmental variation across populations. We also demonstrate that genotype state at methylation quantitative trait loci (meQTLs) can affect methylation trends at some known age-associated CpG sites. Our study explores the relationship between CpG methylation and chronological age in populations of African hunter-gatherers, who rely on different diets across diverse ecologies. While many age-related CpG sites replicate across populations, we show that considering common genetic variation at meQTLs further improves our ability to detect previously identified age associations.

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