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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.

SUBMITTER: Kern AD 

PROVIDER: S-EPMC5982824 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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diploS/HIC: An Updated Approach to Classifying Selective Sweeps.

Kern Andrew D AD   Schrider Daniel R DR  

G3 (Bethesda, Md.) 20180531 6


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 d  ...[more]

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