HacDivSel: Two new methods (haplotype-based and outlier-based) for the detection of divergent selection in pairs of populations

Abstract

The detection of genomic regions involved in local adaptation is an important topic in current population genetics. There are several detection strategies available depending on the kind of genetic and demographic information at hand. A common drawback is the high risk of false positives. In this study, we introduce two complementary methods for the detection of divergent selection from populations connected by migration. Both methods have been developed with the aim of being robust to false positives. The first method combines haplotype information with inter-population differentiation (FST). Evidence of divergent selection is concluded only when both the haplotype pattern and the FST value support it. The second method is developed for independently segregating markers i.e. there is no haplotype information at hand. In this case, the power to detect selection is attained by developing a new outlier test based on detecting a bimodal distribution. The test computes the FST outliers and then assumes that those of interest would have a different mode which is detected by a clustering algorithm. The utility of the two methods is demonstrated through simulations and the analysis of real data. The simulation results show power ranging from 60-94% in several of the scenarios whilst the false positive rate is controlled below the nominal level in every scenario. The analysis of real samples consisted of phased data from the HapMap project and unphased data from intertidal marine snail ecotypes. The software HacDivSel implements the methods explained in this manuscript.