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

## TL;DR

This paper introduces TreeSwirl, a new method to infer population mixtures using genome-wide data, which improves accuracy by considering genetic linkage.

## Contribution

TreeSwirl introduces a hidden Markov model to infer locus-specific mixture proportions while accounting for linkage, improving upon existing methods.

## Key findings

- TreeSwirl accurately estimates locus-specific mixture proportions in simulated data.
- The method outperforms traditional D- and f-statistics in detecting introgressed loci.
- It handles complex demographic scenarios effectively.

## Abstract

Admixture 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 rate for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl, 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. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix, our model infers locus-specific mixture proportions employing a hidden Markov model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can 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.