A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.

This dataset contains measurements of defensive metabolites in genotyped accessions of Arabodopsis thaliana, the genotypes used for genome-wide association (GWA) mapping of these metabolites, and the output of GWA analyses. For the TOU-A population, metabolites were extracted and quantified from full rosettes using high pressure liquid chromatography and mass spectrometry (HPLC-MS/MS). The raw HPLC-MS/MS output files have been deposited here, along with the output of linear mixed models of metabolite variation across accessions. For other populations, metabolite abundances were obtained from prior publications and associated repositories, as described here in the README file and in the methods section of the manuscript associated with this dataset (doi:10.1098/rstb.2020.0512). GWA mapping of each metabolite in each population was conducted using the GEMMA package (doi:10.1038/ng.2310), and all output files have been deposited here. Scripts used for the analysis of the current dataset are available at https://github.com/peterlaurin/TOUA_Glucosinolate_GWAS.