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. 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. We demonstrate the utility of the two methods through simulations and the analysis of real data. The simulation results showed power ranging from 60–95% in several of the scenarios whilst the false positive rate was controlled below the nominal level. The analysis of real samples consisted of phased data from the HapMap project and unphased data from intertidal marine snail ecotypes. The results illustrate that the proposed methods could be useful for detecting locally adapted polymorphisms. The software HacDivSel implements the methods explained in this manuscript.
Highlights
Current population genetics has an important focus on the detection of the signature of natural selection at the molecular level
The power of a test is measured as the percentage of runs in which selection was detected from simulated selective scenarios, and the false positive rate (FPR) is measured as the percentage of runs in which selection was detected from simulated neutral scenarios
To check the performance of the Extreme Outlier Set test (EOS) test with real data, we provide an example with the aim of checking if even under its conservativeness is able of detecting some outliers on an already published dataset from the marine gastropod L. saxatilis
Summary
Current population genetics has an important focus on the detection of the signature of natural selection at the molecular level. The detection of the selection effect in a given DNA region is important because it may connect that region with a key functionality, or past or ongoing selective events, to have a deeper understanding of the evolutionary processes. It may help to understand the evolutionary mechanisms allowing the species adaptation to local conditions. The study of local adaptation processes implies that some genetic variant is favored. HacDivSel: Detection of divergent selection and analysis, decision to publish, or preparation of the manuscript
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