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Human genomic methylation: signatures across populations and ages

Genomic DNA methylation is an epigenetic marker that reflects influences of the environment, aging, and diseases. Although causal mechanisms of these alterations are understudied, the first step to addressing changes in DNA methylation is to map alterations appearing in a particular context. For example, human populations have diverse situational exposures. As an extreme example, the isolated populations of nomadic hunter/gatherer individuals in the Kalahari Desert lack access to most of the conveniences of modern lifestyles. Due to climate and behavioral adaptations of the lifestyle of these isolated populations (acoustic sensitivity to predators, irregular water and food availability), I predicted and demonstrated altered methylation landscapes compared with a non-Khoesan group of southern Africans with more industrialized lifestyles. The sites of differential methylation were assessed for potential functional impact at several example loci.

A related project addressed nucleotide variants at interrogated methylation loci. For instance, C to T polymorphisms occurring in some individuals cannot initially be discriminated from loci that have differential cytosine methylation (following bisulfite treatment) in an array-based assay. My solution to this problem was the development of a computational approach to detect loci in methylation array data, which show tiered patterns created by SNP alleles rather than the usual continuum of differential methylation values. This approach was applied to the Kalahari populations and HapMap groups to show the utility of the approach.

In the Kalahari populations, post-infancy ages are not recorded. We used functions that utilize DNA methylation to calculate estimates of aging and compared these results with predictions reported by the sample collectors, which were based primarily on interactions with non-nomadic neighbors. I compared the same aging estimates to known ages in the non-Khoesan samples and found correspondence. Although DNA methylation is a good predictor of cellular age, another method is telomere length measurement. To assess a relationship between predictors, I assessed associations in 300 samples between age, DNA methylation, and telomere length. Initial results indicated multiple correlated loci when accounting for gender and ethnicity using a linear model approach. / 2020-07-31T00:00:00Z
Date01 August 2019
CreatorsLaBarre, Brenna
ContributorsTullius, Thomas
Source SetsBoston University
Detected LanguageEnglish
RightsAttribution-NonCommercial-ShareAlike 4.0 International,

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