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diploS/HIC: An Updated Approach to Classifying Selective Sweeps
oleh: Andrew D. Kern, Daniel R. Schrider
Format: | Article |
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Diterbitkan: | Oxford University Press 2018-06-01 |
Deskripsi
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.