The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by F ST or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features-the use of haplotype information and of the hierarchical structure of populations-significantly improves the detection power of selected loci and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favorable haplotype is only at intermediate frequency in the population(s) under selection.T HE detection of molecular signatures of selection is one of the major concerns of modern population genetics. It provides insight on the mechanisms leading to population divergence and differentiation. It has become crucial in biomedical sciences, where it can help to identify genes related to disease resistance (Tishkoff et al. 2001;Barreiro et al. 2008;Albrechtsen et al. 2010;Fumagalli et al. 2010;Cagliani et al. 2011), adaptation to climate (Lao et al. 2007;Sturm 2009;Rees and Harding 2012), or altitude (Bigham et al. 2010;Simonson et al. 2010). In livestock species, where artificial selection has been carried out by humans since domestication, it contributes to map traits of agronomical interest, for instance, related to milk (Hayes et al. 2009) or meat (Kijas et al. 2012) production.Efficiency of methods for detecting selection varies with the considered selection timescale (Sabeti et al. 2006). For the detection of selection within species (the ecological scale of time), methods can be classified into three groups: methods based on (i) the high frequency of derived alleles and other consequences of hitchhiking within population (Kim and Stephan 2002;Kim and Nielsen 2004;Nielsen et al. 2005;Boitard et al. 2009), (ii) the length and structure of haplotypes, measured by extended haplotype homozygosity (EHH) or EHH-derived statistics (Sabeti et al. ...

The diversity of populations in domestic species offers great opportunities to study genome response to selection. The recently published Sheep HapMap dataset is a great example of characterization of the world wide genetic diversity in sheep. In this study, we re-analyzed the Sheep HapMap dataset to identify selection signatures in worldwide sheep populations. Compared to previous analyses, we made use of statistical methods that (i) take account of the hierarchical structure of sheep populations, (ii) make use of linkage disequilibrium information and (iii) focus specifically on either recent or older selection signatures. We show that this allows pinpointing several new selection signatures in the sheep genome and distinguishing those related to modern breeding objectives and to earlier post-domestication constraints. The newly identified regions, together with the ones previously identified, reveal the extensive genome response to selection on morphology, color and adaptation to new environments.

Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans increases detection power, but haplotype-based methods require individual genotypes and are not applicable on pool-sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium in genome scans for selection, cumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal. Using computer simulations, we demonstrate that this approach detects selection with higher power than several state-of-the-art single-marker, windowing or haplotype-based approaches. We illustrate this on two benchmark data sets including individual genotypes, for which we obtain similar results with the local score and one haplotype-based approach. Finally, we apply the local score approach to Pool-Seq data obtained from a divergent selection experiment on behaviour in quail and obtain precise and biologically coherent selection signals: while competing methods fail to highlight any clear selection signature, our method detects several regions involving genes known to act on social responsiveness or autistic traits. Although we focus here on the detection of positive selection from multiple population data, the local score approach is general and can be applied to other genome scans for selection or other genomewide analyses such as GWAS.