Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases1,2, advances in genotyping and sequencing technology have made genome-wide association (GWA) studies an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available because once these lines have been genotyped, they can be phenotyped multiple times, making it possible (as well as extremely cost-effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly selfing model plant, known to harbor considerable genetic variation for many adaptively important traits3. Our results are dramatically different from those of human GWA studies in that we identify many common alleles with major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true from false associations. However, a priori candidates are significantly overrepresented among these associations as well, making many of them excellent candidates for follow-up experiments by the Arabidopsis community. Our study clearly demonstrates the feasibility of GWA studies in A. thaliana, and suggests that the approach will be appropriate for many other organisms.

Understanding the genetic bases and modes of adaptation to current climatic conditions is essential to accurately predict responses to future environmental change. We conducted a genome-wide scan to identify climate-adaptive genetic loci and pathways in the plant Arabidopsis thaliana. Amino acid-changing variants were significantly enriched among the loci strongly correlated with climate, suggesting that our scan effectively detects adaptive alleles. Moreover, from our results, we successfully predicted relative fitness among a set of geographically diverse A. thaliana accessions when grown together in a common environment. Our results provide a set of candidates for dissecting the molecular bases of climate adaptations, as well as insights about the prevalence of selective sweeps, which has implications for predicting the rate of adaptation.