Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana - Wu, Si; Alseekh, Saleh; Cuadros-Inostroza, Álvaro; Fusari, Corina M.; Mutwil, Marek; Kooke, Rik; Keurentjes, Joost B.; Fernie, Alisdair R.; Willmitzer, Lothar; Brotman, Yariv (PLOS Genetics, 2016)
Author Summary Primary metabolites are key elements in plant growth and development. Our partial understanding of their biosynthesis and regulation derives mostly from biochemical and genetic modification experiments. The recent generation of large-scale genome-wide data, along with the advances in mass-spectrometry techniques, allows us to treat metabolite levels as quantitative traits and to link them to genomic information, resulting in the identification of so-called metabolic quantitative trait loci (mQTL). These mQTL contribute to the discovery of new biosynthetic and regulatory elements that control the plant's metabolic landscape. Low mapping resolution, however, normally limits discovery to one causal gene per locus. Here, we utilize a complementary strategy to support the identification of casual genes by genetic mapping. We measured metabolite levels in 314 A. thaliana accessions, then used genome-wide association mapping to identify mQTL. We next used previously published results from a time-course stress study to construct metabolite‒transcript correlation networks. Integrating data from both approaches enabled us to select candidate genes linked to specific metabolites. We finally validated two of the novel gene‒metabolite associations using knockout lines. We demonstrated that by using an integrative strategy, we can validate previously characterized gene–metabolite associations, and most importantly, identify novel associations between metabolites and genes. The combined quantitative genetics and metabolite‒transcript networks that we present here can be applied to other organisms and fields of research.