RT Journal Article SR Electronic T1 diploS/HIC: an updated approach to classifying selective sweeps JF bioRxiv FD Cold Spring Harbor Laboratory SP 267229 DO 10.1101/267229 A1 Kern, Andrew D. A1 Schrider, Daniel R. YR 2018 UL http://biorxiv.org/content/early/2018/02/18/267229.abstract AB 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