DiploS/HIC: An updated approach to classifying selective sweeps

Andrew D. Kern, Daniel R. Schrider

Research output: Contribution to journalArticle

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1959-1970
Number of pages12
JournalG3: Genes, Genomes, Genetics
Volume8
Issue number6
DOIs
StatePublished - Jun 1 2018

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

Keywords

  • Adaptation and Population
  • Deep learning Selective Sweeps
  • Genetics
  • Machine Learning

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