Population epigenetics: deciphering DNA methylation diversity and its implications for health, disease, and evolution

Population epigenetics investigates the distribution, determinants, and evolutionary significance of epigenetic variation, particularly DNA methylation (DNAm), within and among human populations. In this review, we synthesize recent advances in population epigenetics with an explicit evolutionary perspective, focusing on 3 interconnected dimensions. First, we outline patterns of DNAm variation, including tissue-, cell-, and developmental-stage specificity. Second, we examine the sources of DNAm variation, highlighting population-specific methylation quantitative trait loci, ancestry-related genetic architecture, and environmental modulators such as diet, climate, pollution, and lifestyle. Third, we explore evolutionary and clinical implications, linking DNAm variation to phenotypic plasticity, disease susceptibility, and adaptive processes shaped by natural selection. Comparative population studies demonstrate that genetic ancestry accounts for a substantial proportion of DNAm differentiation, reflecting the joint effects of allele-frequency divergence, linkage disequilibrium structure, and demographic history. At the same time, environmentally induced methylation changes introduce a dynamic and rapidly responsive layer of molecular diversity that can mediate short-term adaptation to local environments. Despite rapid progress, key challenges remain, including disentangling genetic, environmental, and technical confounders, assessing the stability and heritability of population-specific DNAm patterns, and establishing causal links between methylation, fitness-related traits, and selection. Looking ahead, advances in large-scale multi-ancestry cohorts, single-cell and long-read epigenomics, and integrative multi-omics will enable high-resolution mapping of epigenetic variation across populations. By bridging evolutionary theory, epigenetic epidemiology, and functional genomics, population epigenetics offers a powerful framework for understanding human adaptation, health disparities, and precision medicine in an evolutionary context.