  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 Fst 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 favourable
haplotype is only at intermediate frequency in the population(s) under
selection.
