Detecting Selection from Linked Sites Using an F-Model. Issue 4 (1st December 2020)

• Record Type: Journal Article
• Authors: Galimberti, Marco; Leuenberger, Christoph; Wolf, Beat; Szilágyi, Sándor Miklós; Foll, Matthieu; Wegmann, Daniel
• Is Part Of: Genetics. Volume 216:Issue 4(2020)
• Page Start: 1205
• Page End: 1215
• Publication Date: 2020-12-01
• Subjects: Bayesian statistics -- F-statistics -- hidden Markov model -- divergent selection -- balancing selection; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.120.303780
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Allele frequencies vary across populations and loci, even in the presence of migration. While most differences may be due to genetic drift, divergent selection will further increase differentiation at some loci. Identifying those is key in studying local adaptation, but remains statistically challenging. A particularly elegant way to describe allele frequency differences among populations connected by migration is the F -model, which measures differences in allele frequencies by population specific F ST coefficients. This model readily accounts for multiple evolutionary forces by partitioning F ST coefficients into locus- and population-specific components reflecting selection and drift, respectively. Here we present an extension of this model to linked loci by means of a hidden Markov model (HMM), which characterizes the effect of selection on linked markers through correlations in the locus specific component along the genome. Using extensive simulations, we show that the statistical power of our method is up to twofold higher than that of previous implementations that assume sites to be independent. We finally evidence selection in the human genome by applying our method to data from the Human Genome Diversity Project (HGDP).