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 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 have 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 begun genome wide genotyping 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. We have extended these analyses to reveal age related DNA methylation changes that occur across various tissues, including brain. Currently there is ongoing work to perform a more dense genetic and epigenetic survey of these tissues, including exome sequencing, assay of 500,000 DNA methylation sites and mRNA sequencing. Out current work aims to expand this effort to include whole RNAseq in a set of 300 brains. Further we aim to perform mroe integrated analyses, including the effects of aging, DNA, methylation, RNA, and microRNA, on outcomes such as protein level.

National Institute of Health (NIH)
National Institute on Aging (NIA)
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National Institute on Aging
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Ferrari, Raffaele; Hernandez, Dena G; Nalls, Michael A et al. (2014) Frontotemporal dementia and its subtypes: a genome-wide association study. Lancet Neurol 13:686-99
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