Localizing and Classifying Adaptive Targets with Trend Filtered Regression.

ABSTRACT: Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics regardless of the genomic location from which they are calculated. However, due to linkage disequilibrium, summary statistics calculated at nearby genomic positions are highly correlated. We introduce an approach termed Trendsetter that accounts for the similarity of statistics calculated from adjacent genomic regions through trend filtering, while reducing the effects of multicollinearity through regularization. Our penalized regression framework has high power to detect sweeps, is capable of classifying sweep regions as either hard or soft,

SUBMITTER: Mughal MR 

PROVIDER: S-EPMC6409434 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature