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