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Modern population genetics studies typically involve genome-wide genotypingof individuals from a diverse network of ancestries. An important, unsolvedproblem is how to formulate and estimate probabilistic models of observedgenotypes that allow for complex population structure. We formulate two generalprobabilistic models, and we propose computationally efficient algorithms toestimate them. First, we show how principal component analysis (PCA) can beutilized to estimate a general model that includes the well-knownPritchard-Stephens-Donnelly mixed-membership model as a special case. Notingsome drawbacks of this approach, we introduce a new "logistic factor analysis"(LFA) framework that seeks to directly model the logit transformation ofprobabilities underlying observed genotypes in terms of latent variables thatcapture population structure. We demonstrate these advances on data from thehuman genome diversity panel and 1000 genomes project, where we are able toidentify SNPs that are highly differentiated with respect to structure whilemaking minimal modeling assumptions.

Probabilistic models of genetic variation in structured populations applied to global human studies