Localization of adaptive variants in human genomes using averaged one-dependence estimation.

ABSTRACT: Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptiv

SUBMITTER: Sugden LA 

PROVIDER: S-EPMC5818606 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature