A Regression-Based Approach to Selection Mapping

Selection mapping applies the population genetics theory of hitchhiking to the localization of genomic regions containing genes under selection. This approach predicts that neutral loci linked to genes under positive selection will have reduced diversity due to their shared history with a selected locus, and thus, genome scans of diversity levels can be used to identify regions containing selected loci. Most previous approaches to this problem ignore the spatial genomic pattern of diversity expected under selection. The regression-based approach advocated in this paper takes into account the expected pattern of decreasing genetic diversity with increased proximity to a selected locus. Simulated data are used to examine the patterns of diversity under different scenarios, in order to assess the power of a regression-based approach to the identification of regions under selection. Application of this method to both simulated and empirical data demonstrates its potential to detect selection. In contrast to some other methods, the regression approach described in this paper can be applied to any marker type. Results also suggest that this approach may give more precise estimates of the location of the selected locus than alternative methods, although the power is slightly lower in some cases.