diploS/HIC: An Updated Approach to Classifying Selective Sweeps

• Main Authors: Andrew D. Kern, Daniel R. Schrider
• Format: Article
• Language: English
• Published: Oxford University Press 2018-06-01
• Series: G3: Genes, Genomes, Genetics
• Subjects: Machine Learning; Deep learning; Selective Sweeps; Adaptation; and Population genetics
• Online Access: http://g3journal.org/lookup/doi/10.1534/g3.118.200262

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.