diplos/hic: an updated approach to classifying selective sweeps

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ID: 224192
2018
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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.
Reference Key
kern2018g3:diplos/hic: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Andrew D. Kern;Daniel R. Schrider
Journal separation and purification technology
Year 2018
DOI
10.1534/g3.118.200262
URL
http://g3journal.org/lookup/doi/10.1534/g3.118.200262
Keywords
Deep learning Machine learning adaptation

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