Inference of Locus-Specific Population Mixtures From Linked Genome-Wide Allele Frequencies

Abstract

SummaryAdmixture between between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rates for the entire genome and do not explicitly account for linkage. Here, we introduceTreeSwirl, a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred.TreeSwirlbuilds uponTreeMixthat uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast toTreeMix, our model infers locus-specific mixture proportions employing a Hidden Markov Model that accounts for linkage. Through simulated data, we demonstrate thatTreeSwirlcan accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D- and F-statistics in terms of accuracy and sensitivity to detect introgressed loci.