Characterization of Normal Genomic Variability

National Institute On Aging

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Abstract

Using more than 500 samples from 31 distinct worldwide human populations we performed very dense genome wide single nucleotide polymorphism (SNP) genotyping at 550,000 loci. We have previously analyzed these data and the distribution of genotypes, haplotypes and copy number variants across populations. We showed that these data were able to assign individuals to populations and that the resulting predictions supported fine-scale inferences about population structure. Increasing linkage disequilibrium was observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. We extended upon this work to use the data from these populations to determine whether imputation of unknown genotypes is feasible and the best approach to this prediction. This particular aspect of work has now been expanded to include numerous sub-populations in sub-saharan Africa, particularly from the people of the San. To understand the effects of genetic variability on DNA methylation we have performed genome wide genotyping, exome sequencing and epigenome wide DNA methylation typing in 500 brain samples. These data show a striking effect of genetic variation on DNA methylation levels and show clearly that such variation is likely to be physically close the the DNA methylation site under influence. Further we show that these effects are generally consistent across tissues, although there are some notable exceptions to this noted as tissue specific methylation Quantitative Trait Loci. These data are now being used to understand genetic aging in different tissues and as a reference data set for epigenome wide association studies. We have generated whole genome sequencing across our reference data set, the North American Brain Expression Consortium, and have in parallel performed whole transcriptome sequencing. Current analyses are focussing on expression quantitative trait locus identification, expression outlier analysis (to establish the role of rare variability on expression and biological traits in general). We are generating pilot data in these samples that aim to identify epigenetic and transcriptional changes at the single-cell level.