Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.

ABSTRACT: Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is crucial to develop the statistical machinery required to uncover which genomic regions have recently acquired haplotypes via introgression from a sister population. We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. FILET works by combining information from a number of population genetic summary statistics, including several new statistics that we introduce, that capture patterns of variation across two p

SUBMITTER: Schrider DR 

PROVIDER: S-EPMC5933812 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature