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:>
- codominant data (as SNPs or microsatellites)
- dominant binary data (as AFLPs)
- 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.