diploS/HIC: An Updated Approach to Classifying Selective Sweeps

Abstract

Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes.

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Cited by papers we hold 2

• A community-maintained standard library of population genetic models · 2020
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• Contrasting signatures of genomic divergence during sympatric speciation · 2020
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