diploS/HIC: An Updated Approach to Classifying Selective Sweeps

G3: Genes, Genomes, Genetics

Andrew D. Kern^1¹ 

[1] Department of Genetics, Rutgers University, Piscataway, NJ 08854^1

关键词: Machine Learning;    Deep learning;    Selective Sweeps;    Adaptation;    and Population genetics;   

DOI  :  10.1534/g3.118.200262

学科分类：生物科学（综合）

来源: Genetics Society of America

【 摘 要 】

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.

【 授权许可】

CC BY