Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines

Abstract

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.

Integration of responses within and acrossArabidopsisnatural accessions uncovers loci controlling root systems architecture

“…After standardizing each PC COL root trait to a mean of zero and a SD of 1, we performed genome-wide association mapping using the SNP database from Atwell et al (14), which documents over 214,000 SNPs (an average of 1 SNP/500 bp) in 69 different inbred lines from the wild. We filtered the database to include only SNPs with a minor allele frequency greater than 0.10, which left 177,623 SNPs for mapping.…”

“…The projections of the PC values from the natural variants into the PC1 COL and PC2 COL phenotypic space were next used to map genes underlying both natural variation and phenotypic plasticity. To do this, we looked for associations between the PC1 COL or PC2 COL phenotypes exhibited in the natural accessions and the 214,000 SNP dataset of polymorphisms in Arabidopsis (14). No significant genome-wide association study (GWAS) associations were identified for the PC1 COL trait (overall root size), suggesting that size might be (11).…”

Genome-Wide Association Mapping of Fertility Reduction upon Heat Stress Reveals Developmental Stage-Specific QTLs in Arabidopsis thaliana

“…A collection consisting of 285 natural accessions of Arabidopsis thaliana (Supplemental Data Set 1) selected to capture most of the genetic variation present within the species (Platt et al, 2010) and genotyped with ;215k SNP markers (Affymetrix SNP-tilling array Atsnptile1; Atwell et al, 2010;Li et al, 2010) was obtained from the ABRC Stock Center (Baxter et al, 2010).…”

Synthetic biology of metabolism: using natural variation to reverse engineer systems

“…These observations are coming from the use of genome wide association (GWA) mapping which utilizes the standing variation present within a natural population. Measuring phenotypes in this same population then allows the researcher to find putative genes where genetic variation correlates with phenotypic variation [33][34][35][36].GWA studies have recently begun to be applied to investigating the genetic variation underlying phenotypic variation in both primary and secondary metabolism [37 ,38,39,40 ]. An GWA study of genetic variation in the glucosinolate secondary metabolite pathway present in Arabidopsis thaliana showed that it was possible to identify causal genes with >70% accuracy using a combination of GWA and transcriptomics [37 ,39].…”

“…These observations are coming from the use of genome wide association (GWA) mapping which utilizes the standing variation present within a natural population. Measuring phenotypes in this same population then allows the researcher to find putative genes where genetic variation correlates with phenotypic variation [33][34][35][36].…”