A fully integrated machine learning scan of selection in the chimpanzee genome.

ABSTRACT: After diverging, each chimpanzee subspecies has been the target of unique selective pressures. Here, we employ a machine learning approach to classify regions as under positive selection or neutrality genome-wide. The regions determined to be under selection reflect the unique demographic and adaptive history of each subspecies. The results indicate that effective population size is important for determining the proportion of the genome under positive selection. The chimpanzee subspecies share signals of selection in genes associated with immunity and gene regulation. With these results, we have created a selection map for each population that can be displayed in a genome browser (www.hsb.upf.edu/chimp_browser). This study is the first to use a detailed demographic history and machine lear

SUBMITTER: Nye J 

PROVIDER: S-EPMC7671310 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature