A new Bayesian method to identify the environmental factors that influence recent migration.
We present a new multilocus genotype method that makes inferences about recent immigration rates and identifies the environmental factors that are more likely to explain observed gene flow patterns. It also estimates population-specific inbreeding coefficients, allele frequencies, and local population F(ST)'s and performs individual assignments. We generate synthetic data sets to determine the region of the parameter space where our method is and is not able to provide accurate estimates. Our simulation study indicates that reliable results can be obtained when the global level of genetic differentiation (F(ST)) is >1%, the number of loci is only 10, and sample sizes are of the order of 50 individuals per population. We illustrate our method by applying it to Pakistani human data, considering altitude and geographic distance as explanatory factors. Our results suggest that altitude explains better the genetic data than geographic distance. Additionally, they show that southern low-altitude populations have higher migration rates than northern high-altitude ones.
Faubet
Pierre
P
Génomique des Populations et Biodiversité Laboratoire d'Ecologie Alpine, CNRS UMR 5553, Université Joseph Fourier, 38041 Grenoble, France.
Gaggiotti
Oscar E
OE
D001499:Bayes Theorem; D003241:Consanguinity; D019992:Databases as Topic; D004641:Emigration and Immigration; D004777:Environment; D051456:Gene Flow; D006801:Humans; D008957:Models, Genetic; D010154:Pakistan