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The detection of molecular signatures of selection is one of the majorconcerns of modern population genetics. A widely used strategy in this contextis to compare samples from several populations, and to look for genomic regionswith outstanding genetic differentiation between these populations. Geneticdifferentiation is generally based on allele frequency differences betweenpopulations, which are measured by Fst or related statistics. Here we introducea new statistic, denoted hapFLK, which focuses instead on the differences ofhaplotype frequencies between populations. In contrast to most existingstatistics, hapFLK accounts for the hierarchical structure of the sampledpopulations. Using computer simulations, we show that each of these twofeatures - the use of haplotype information and of the hierarchical structureof populations - significantly improves the detection power of selected loci,and that combining them in the hapFLK statistic provides even greater power. Wealso show that hapFLK is robust with respect to bottlenecks and migration andimproves over existing approaches in many situations. Finally, we apply hapFLKto a set of six sheep breeds from Northern Europe, and identify seven regionsunder selection, which include already reported regions but also several newones. We propose a method to help identifying the population(s) under selectionin a detected region, which reveals that in many of these regions selectionmost likely occurred in more than one population. Furthermore, several of thedetected regions correspond to incomplete sweeps, where the favourablehaplotype is only at intermediate frequency in the population(s) underselection.

Using haplotype differentiation among hierarchically structured populations for the detection of selection signatures