Identifying loci under natural selection from genomic surveys is of great interest in different research areas. Commonly used methods to separate neutral effects front adaptive effects are based on locus-specific population differentiation coefficients to identify outliers. Here we extend Such an approach to estimate directly the probability that each locus is subject to selection using a Bayesian method. We also extend it to allow the use of dominant markets like AFLPs. It has been shown that this model is robust. to complex demographic scenarios for neutral genetic differentiation. Here we show that the inclusion of isolated Populations that underwent a strong bottleneck can lead to a high rate of false positives. Nevertheless, we demonstrate that it is possible to avoid them by carefully choosing the populations that should be included in the analysis. We analyze two previously published data sets: a human data set of codominant markers and a Littorina saxatilis data set of dominant markers. We also perform a detailed sensitivity study to compare the power of the method rising amplified fragment length polymorphism (AFLP), SNP, and microsatellite markers. The method has been implemented in a new software available at our website (http://www-leca.ujf-grenoble.fr/logiciels.htm).