This program, BayeScan aims at identifying candidate loci under natural selection from genetic data, using differences in allele frequencies between populations. BayeScan is based on the multinomial-Dirichlet model. One of the simplest possible scenarios covered consists of an island model in which subpopulation allele frequencies are correlated through a common migrant gene pool from which they differ in varying degrees. The difference in allele frequency between this common gene pool and each subpopulation is measured by a subpopulation specific FST coefficient. In BayeScan, three different types of data can be used: (i) codominant data (as SNPs or microsatellites), (ii) dominant binary data (as AFLPs) and (iii) AFLP amplification intensity, which are neither considered as dominant nor codominant. Selection is introduced by decomposing FST coefficients into a population-specific component shared by all loci, and a locus-specific component shared by all the populations using a logistic regression on FST coefficients.
The author is Matthieu Foll.
The web site of the newest version (2.0) can be found here.
The software can be cited as: Foll, M and OE Gaggiotti (2008) A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180: 977-993.