RT Journal Article SR Electronic T1 HacDivSel: Two new methods (haplotype-based and outlier-based) for the detection of divergent selection in pairs of populations of non-model species JF bioRxiv FD Cold Spring Harbor Laboratory SP 026369 DO 10.1101/026369 A1 A. Carvajal-Rodríguez YR 2016 UL http://biorxiv.org/content/early/2016/02/25/026369.abstract AB In this work two new methods for detection of divergent selection in populations connected by migration are introduced. The new statistics are robust to false positives and do not need knowledge on the ancestral or derived allelic state. There is no requirement for performing neutral simulations to obtain critical cut-off values for the identification of candidates. The first method, called nvdFST, combines information from the haplotype patterns with inter-population differences in allelic frequency. Remarkably, this is not a FST outlier test because it does not look at the highest FSTs to identify loci. On the contrary, candidate loci are chosen based on a haplotypic allelic class metric and then the FST for these loci are estimated and compared to the overall FST. Evidence of divergent selection is concluded only when both the haplotype pattern and the FST value support it. It is shown that power ranging from 79-94% are achieved in many of the scenarios assayed while the false positive rate is controlled below the desired nominal level (γ = 0.05). Additionally, the method is also robust to demographic scenarios including population bottleneck and expansion. The second method, called EOS, is developed for data with independently segregating markers. In this case, the power to detect selection is attained by developing a new GST extreme-outlier set test (EOS) based on heuristic problem solving via a k-means clustering algorithm. The utility of the methods is demonstrated through simulations and the analysis of real data. Both algorithms have been implemented in the program HacDivSel.