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Versatile simulations of admixture and accurate local ancestry inference with mixnmatch and ancestryinfer

Citation

Schumer, Molly; Powell, Daniel; Corbett-detig, Russell (2020), Versatile simulations of admixture and accurate local ancestry inference with mixnmatch and ancestryinfer, Dryad, Dataset, https://doi.org/10.5061/dryad.s4mw6m93f

Abstract

It is now clear that hybridization between species is much more common than previously recognized. As a result, we now know that the genomes of many modern species, including our own, are a patchwork of regions derived from past hybridization events. Increasingly researchers are interested in disentangling which regions of the genome originated from each parental species using local ancestry inference methods. Due to the diverse effects of admixture, this interest is shared across disparate fields, from human genetics to research in ecology and evolutionary biology. However, local ancestry inference methods are sensitive to a range of biological and technical parameters which can impact accuracy. Here we present paired simulation and ancestry inference pipelines, mixnmatch and ancestryinfer, to help researchers plan and execute local ancestry inference studies. mixnmatch can simulate arbitrarily complex demographic histories in the parental and hybrid populations, selection on hybrids, and technical variables such as coverage and contamination. ancestryinfer takes as input sequencing reads from simulated or real individuals, and implements an efficient local ancestry inference pipeline. We perform a series of simulations with mixnmatch to pinpoint factors that influence accuracy in local ancestry inference and highlight useful features of the two pipelines. Together, mixnmatch and ancestryinfer are powerful tools for predicting the performance of local ancestry inference methods on real data.

Funding

National Institute of General Medical Sciences, Award: 1R35GM128932,1R35GM133774

Howard Hughes Medical Institute, Award: Hanna H Gray

Alfred P. Sloan Foundation, Award: Alfred P. Sloan Fellowship

Stanford University