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.

Arabidopsis thaliana is native to Eurasia and naturalized across the world due to human disturbance. Its easy propagation and immense phenotypic variability make it an ideal model system for functional, ecological and evolutionary genetics. To date, analyses of its natural variation have involved small numbers of individuals or genetic markers. Here we genotype 1,307 world-wide accessions, including several regional samples, at 250K SNPs, enabling us to describe the global pattern of genetic variation with high resolution. Three complementary tests applied to these data reveal novel targets of selection. Furthermore, we characterize the pattern of historical recombination and observe an enrichment of hotspots in intergenic regions and repetitive DNA, consistent with the pattern observed for humans but strikingly different from other plant species. We are making seeds for this Regional Mapping (RegMap) panel publicly available; they comprise the largest genomic mapping resource available for a naturally occurring, non-human, species.

Transcriptional reprogramming forms a major part of a plant's response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea.

To respond to pathogen attack, selection and associated evolution has led to the creation of plant immune system that are a highly effective and inducible defense system. Central to this system are the plant defense hormones jasmonic acid (JA) and salicylic acid (SA) and crosstalk between the two, which may play an important role in defense responses to specific pathogens or even genotypes. Here, we used the Arabidopsis thaliana-Botrytis cinerea pathosystem to test how the host's defense system functions against genetic variation in a pathogen. We measured defense-related phenotypes and transcriptomic responses in Arabidopsis wild-type Col-0 and JA-and SA-signaling mutants, coi1-1 and npr1-1, individually challenged with 96 diverse B. cinerea isolates. Those data showed genetic variation in the pathogen influences on all components within the plant defense system at the transcriptional level. We identified four gene coexpression networks and two vectors of defense variation triggered by genetic variation in B. cinerea. This showed that the JA and SA signaling pathways functioned to constrain/canalize the range of virulence in the pathogen population, but the underlying transcriptomic response was highly plastic. These data showed that plants utilize major defense hormone pathways to buffer disease resistance, but not the metabolic or transcriptional responses to genetic variation within a pathogen.

Understanding how genetic variation interacts with the environment is essential for understanding adaptation. In particular, the life cycle of plants is tightly coordinated with local environmental signals through complex interactions with the genetic variation (G x E). The mechanistic basis for G x E is almost completely unknown. We collected flowering time data for 173 natural inbred lines of Arabidopsis thaliana from Sweden under two growth temperatures (10°C and 16°C), and observed massive G x E variation. To identify the genetic polymorphisms underlying this variation, we conducted genome-wide scans using both SNPs and local variance components. The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both. Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%). Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.