摘要
Metabolic genome-wide association studies （mGWAS）, whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309Arabidopsis accessions were grown under two independent environmental conditions （control and stress） and subjected to untargeted LC-MS- based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two- condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci （p ≤ 1.0E-08）, 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was fol- lowed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between struc- tural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different ablotic environments for identifying metabollte-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.
Metabolic genome-wide association studies （mGWAS）, whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309Arabidopsis accessions were grown under two independent environmental conditions （control and stress） and subjected to untargeted LC-MS- based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two- condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci （p ≤ 1.0E-08）, 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was fol- lowed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between struc- tural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different ablotic environments for identifying metabollte-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.
