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S/HIC: Robust identification of soft and hard sweeps using machine learning

Daniel R. Schrider, Andrew D. Kern
doi: https://doi.org/10.1101/024547
Daniel R. Schrider
1Department of Genetics, Rutgers University, Piscataway, NJ 08854
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  • For correspondence: dan.schrider@rutgers.edu
Andrew D. Kern
1Department of Genetics, Rutgers University, Piscataway, NJ 08854
2Human Genetics Institute of New Jersey, Rutgers University, Piscataway, NJ 08854
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ABSTRACT

Detecting the targets of adaptive natural selection from whole genome sequencing data is a central problem for population genetics. However, to date most methods have shown sub-optimal performance under realistic demographic scenarios. Moreover, over the past decade there has been a renewed interest in determining the importance of selection from standing variation in adaptation of natural populations, yet very few methods for inferring this model of adaptation at the genome scale have been introduced. Here we introduce a new method, S/HIC, which uses supervised machine learning to precisely infer the location of both hard and soft selective sweeps. We show that S/HIC has unrivaled accuracy for detecting sweeps under demographic histories that are relevant to human populations, and distinguishing sweeps from linked as well as neutrally evolving regions. Moreover we show that S/HIC is uniquely robust among its competitors to model misspecification. Thus even if the true demographic model of a population differs catastrophically from that specified by the user, S/HIC still retains impressive discriminatory power. Finally we apply S/HIC to the case of resequencing data from human chromosome 18 in a European population sample and demonstrate that we can reliably recover selective sweeps that have been identified earlier using less specific and sensitive methods.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 04, 2016.
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S/HIC: Robust identification of soft and hard sweeps using machine learning
Daniel R. Schrider, Andrew D. Kern
bioRxiv 024547; doi: https://doi.org/10.1101/024547
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S/HIC: Robust identification of soft and hard sweeps using machine learning
Daniel R. Schrider, Andrew D. Kern
bioRxiv 024547; doi: https://doi.org/10.1101/024547

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