  - Genome-scan methods are used for screening genome-wide patterns of DNA
polymorphism to detect signatures of positive selection. There are two main
types of methods: (i) 'outlier' detection methods based on Fst that detect loci
with high differentiation compared to the rest of the genomes, and (ii)
environmental association methods that test the association between allele
frequencies and environmental variables.
  - We present a new Fst-based genome-scan method, BayeScEnv, which
incorporates environmental information in the form of 'environmental
differentiation'. It is based on the F-model, but, as opposed to existing
approaches, it considers two locus-specific effects; one due to divergent
selection, and another one due to various other processes different from local
adaptation (e.g. range expansions, differences in mutation rates across loci or
background selection). The method was developped in C++ and is avaible at
http://github.com/devillemereuil/bayescenv.
  - Simulation studies shows that our method has a much lower false positive
rate than an existing Fst-based method, BayeScan, under a wide range of
demographic scenarios. Although it has lower power, it leads to a better
compromise between power and false positive rate.
  - We apply our method to human and salmon datasets and show that it can be
used successfully to study local adaptation. We discuss its scope and compare
its mechanics to other existing methods.
