TY - JOUR

T1 - Detection of correlation between genotypes and environmental variables. A fast computational approach for genomewide studies

A1 - Guillot,Gilles

AU - Guillot,Gilles

PB - Cornell-University

PY - 2012

Y1 - 2012

N2 - 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}. The methods allows one to quantify correlation between genotypes and environmental variables and to rank loci accordingly. It works for SNP and AFLP data obtained either at the individual or at the population level. We provide R scripts with detailed comments that can be used readily for the analysis of real data without specific prior knowledge of the R language.

AB - 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}. The methods allows one to quantify correlation between genotypes and environmental variables and to rank loci accordingly. It works for SNP and AFLP data obtained either at the individual or at the population level. We provide R scripts with detailed comments that can be used readily for the analysis of real data without specific prior knowledge of the R language.

UR - http://arxiv.org/abs/1206.0889

JO - arXiv

JF - arXiv

ER -