The 88th Annual Meeting of the American Association of Physical Anthropologists (2019)


An epigenetic measure of biological aging in rhesus macaques

ELISABETH A. GOLDMAN1, KENNETH L. CHIOU2, LAUREN J.N. BRENT3, MICHAEL J. MONTAGUE4, MICHAEL L. PLATT4,5, JULIE E. HORVATH6,7,8, SIERRA SAMS2, KIRSTIN N. STERNER1 and NOAH SNYDER-MACKLER2.

1Anthropology, University of Oregon, 2Psychology, University of Washington, 3Centre for Research in Animal Behaviour, University of Exeter, 4Neuroscience, University of Pennsylvania, 5Psychology, University of Pennsylvania, 6Biological and Biomedical Sciences, North Carolina Central University, 7Genomics & Microbiology Research Lab, NC Museum of Natural Sciences, 8Evolutionary Anthropology, Duke University

March 30, 2019 33, CC Ballroom BC Add to calendar

DNA methylation is an epigenetic modification to the genome that primarily affects CG dinucleotides (“CpG sites”) and can influence gene expression. The epigenetic clock is a robust quantitative model that uses age-dependent changes in DNA methylation to produce a highly accurate age estimate referred to as epigenetic age. Epigenetic age is a measure of biological aging that can be used to detect if an individual is aging at an accelerated or decelerated rate. While chronological age increases at a constant rate, measures of biological age reflect genetic and environmentally-driven heterogeneity in the pace of aging. Accelerated epigenetic aging, where biological age exceeds chronological age, is associated with increased disease and mortality risk. While epigenetic clock models have been developed in mice, humans, and dogs, no robust age prediction model currently exists for a nonhuman primate. Here, we developed an epigenetic clock for rhesus macaques (Macaca mulatta). We measured genome-wide CpG methylation in blood samples from 105 rhesus macaques aged 5 to 28 years (roughly equivalent to 15 to 84 years in humans) living on the island of Cayo Santiago off the coast of Puerto Rico. We measured methylation at 2,846,027 CpG sites across the genome and employed a machine learning approach (elastic net regression) to identify sets of sites that together robustly predict chronological age. In addition to describing the clock, we will present preliminary data that use this age predictor model to investigate the effect of the environment and genetics on aging and age-related diseases in the Cayo macaques.

Funding for this study was provided by NIH R00-AG051764.