sstar: A Python Package for Detecting Archaic Introgression from Population Genetic Data with S*

Author:

Huang Xin12ORCID,Kruisz Patricia3,Kuhlwilm Martin12ORCID

Affiliation:

1. Department of Evolutionary Anthropology, University of Vienna , Djerassiplatz 1, 1030 Vienna , Austria

2. Human Evolution and Archaeological Sciences (HEAS), University of Vienna , Djerassiplatz 1, 1030 Vienna , Austria

3. Department of Bio Data Science, Faculty of Engineering, University of Applied Sciences Wiener Neustadt , Biotech Campus Tulln, Konrad Lorenz-Straße 10, 3430 Tulln , Austria

Abstract

Abstract S* is a widely used statistic for detecting archaic admixture from population genetic data. Previous studies used freezing-archer to apply S*, which is only directly applicable to the specific case of Neanderthal and Denisovan introgression in Papuans. Here, we implemented sstar for a more general purpose. Compared with several tools, including SPrime, SkovHMM, and ArchaicSeeker2.0, for detecting introgressed fragments with simulations, our results suggest that sstar is robust to differences in demographic models, including ghost introgression and two-source introgression. We believe sstar will be a useful tool for detecting introgressed fragments in various scenarios and in non-human species.

Funder

Vienna Science and Technology Fund

Publisher

Oxford University Press (OUP)

Subject

Genetics,Molecular Biology,Ecology, Evolution, Behavior and Systematics

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3. Harnessing deep learning for population genetic inference;Nature Reviews Genetics;2023-09-04

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5. Ghost admixture in eastern gorillas;2022-12-19

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