Résumé
Arabidopsis thaliana is an important model organism for understanding the
genetics and molecular biology of plants. Its highly selfing nature, together
with other important features, such as small size, short generation time, small
genome size, and wide geographic distribution, make it an ideal model organism
for understanding natural variation. Genome-wide association studies (GWAS)
have proven a useful technique for identifying genetic loci responsible for
natural variation in A. thaliana. Previously genotyped accessions (natural
inbred lines) can be grown in replicate under different conditions, and
phenotyped for different traits. These important features greatly simplify
association mapping of traits and allow for systematic dissection of the
genetics of natural variation by the entire Arabidopsis community. To
facilitate this, we present GWAPP, an interactive web-based application for
conducting GWAS in A. thaliana. Using an efficient Python implementation of a
linear mixed model, traits measured for a subset of 1386 publicly available
ecotypes can be uploaded and mapped with an efficient mixed model and other
methods in just a couple of minutes. GWAPP features an extensive, interactive,
and a user-friendly interface that includes interactive manhattan plots and
interactive local and genome-wide LD plots. It facilitates exploratory data
analysis by implementing features such as the inclusion of candidate SNPs in
the model as cofactors.