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Biology subjects

Michael S Kobor

Publications and source records attributed to Michael S Kobor.

3 recordsLinked to original sources

Trends in DNA methylation with age replicate across diverse human populations

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.

Genomics

Worldwide patterns of human epigenetic variation

DNA methylation is an epigenetic modification, influenced by both genetic and environmental variation, that can affect transcription and many organismal phenotypes. Although patterns of DNA methylation have been shown to differ between human populations, it remains to be determined whether epigenetic diversity mirrors the patterns observed for DNA polymorphisms or gene expression levels. We measured DNA methylation at 480,000 sites in 34 individuals from five diverse human populations in the Human Genome Diversity Panel, and analyzed these together with single nucleotide polymorphisms (SNPs) and gene expression data. We found greater population-specificity of DNA methylation than of mRNA levels, which may be driven by the greater genetic control of methylation. This study provides insights into gene expression and its epigenetic regulation across populations and offers a deeper understanding of worldwide patterns of epigenetic diversity in humans.

Genomics

A pooling-based approach to mapping genetic variants associated with DNA methylation

DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover less than 2% of the genome and cannot account for allele-specific methylation (ASM). Other studies have performed whole-genome bisulfite sequencing on a few individuals, but these lack statistical power to identify variants associated with DNA methylation. We present a novel approach in which bisulfite-treated DNA from many individuals is sequenced together in a single pool, resulting in a truly genome-wide map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified over 2,000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data.

Genomics