Abstract
Genomic regions displaying outstanding correlation with some environmental
variables are likely to be under selection and this is the rationale of recent
methods of identifying selected loci and retrieve functional information about
them. To be efficient, such methods need to be able to disentangle the
potential effect of environmental variables from the confounding effect of
population history. For the routine analysis of genomewide data-sets, one also
need fast inference and model selection algorithms. We describe a method based
on an explicit spatial model that builds on the theoretical and computational
framework developed by Rue et al. (2009) and Lindgren et al. (2011
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