Résumé
Genome-environment association methods aim to detect genetic markers
associated with environmental variables. The detected associations are
usually analysed separately to identify the genomic regions involved in
local adaptation. However, a recent study suggests that single-locus
associations can be combined and used in a predictive way to estimate
environmental variables for new individuals on the basis of their
genotypes. Here, we introduce an original approach to predict the
environmental range (values and upper and lower limits) of species
genotypes from the genetic markers significantly associated with those
environmental variables in an independent set of individuals. We
illustrate this approach to predict aridity in a database constituted of
950 individuals of wild beets and 299 individuals of cultivated beets
genotyped at 14,409 random Single Nucleotide Polymorphisms (SNPs). We
detected 66 alleles associated with aridity and used them to calculate the
fraction (I) of aridity-associated alleles in each individual. The
fraction I correctly predicted the values of aridity in an independent
validation set of wild individuals and was then used to predict aridity in
the 299 cultivated individuals. Wild individuals had higher median values
and a wider range of values of aridity than the cultivated individuals,
suggesting that wild individuals have higher ability to resist to
stress-aridity conditions and could be used to improve the resistance of
cultivated varieties to aridity.