This project is concerned with developing new statistical methodology for population genetic data. Attention will be focused on dependencies among sets of alleles: the applications of Hardy-Weinberg testing in high-dimensional genetic data and the characterization of population structure with rare variants and related populations. Theory will be developed at least in part in response to the needs of current whole-genome SNP surveys and whole-genome sequence variant surveys for humans. This work will be extended to analyses of population structure and ancestry proportions to the intensely selected and medically important HLA region to provide an in-depth analysis of population structure and ancestry proportions. The work is proposed by investigators in the Department of Biostatistics at the University of Washington. They will be joined by Jerome Goudet at the University of Lausanne, Jan Graffelman at the Universitat Politecnica de Catalunya and Diogo Meyer at the University of Sao Paulo.
As population genetic datasets grow, there is both the need and the opportunity to quantify the dependencies among alleles within and between individuals, or within and between populations. Individual-level dependencies address inbreeding and relatedness and can lead to estimates of heritability of complex human traits, while population level dependencies can be used to infer the presence of natural selection in the history of the populations. Work is proposed to strengthen ways of estimating allelic dependencies, with attention being paid to the variation imposed by the evolutionary process as well as the variation from sampling individuals from current populations, with particular attention to HLA genes in the major histocompatility region.
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