Resolving Gene-Metabolism Links through Multi-Metabolite mQTL Commonality Analysis
High-throughput metabolomics detects so many compounds that most studies use only a fraction of them, focusing on the few metabolites with an annotated structure or substructure. Metabolite genome-wide association studies of specialized metabolism detect many metabolite quantitative trait loci (mQTLs), but most associated metabolite features remain unannotated, limiting gene discovery. In leaves of 362 tomato accessions under control and water-deficit conditions, only 13 of the 826 metabolite features associated with 77 reproducible mQTLs were annotated with good confidence. To study the mQTLs with the largest impact, we developed commonality analysis, an approach that does not depend on well-studied pathways. Commonality analysis uses biologically defined groups, such as the features associated with one mQTL, to infer relatedness between metabolites. By identifying the fragments, neutral losses or mass transformations that are common in a group, it links the group to a substructure, pathway or reaction, and can extend annotation within the group. Most high-impact mQTLs had a significantly distinct fragment commonality, whose outstanding fragments suggested the pathway involved and its candidate gene. We demonstrate the approach in three representative mQTLs: (i) common fragments pointed to a sinapoyl-hexose pathway and a serine carboxypeptidase-like candidate acyltransferase; (ii) common fragments marked the features associated with a purine permease as nitrogen-containing, which isotope labeling then supported; (iii) a common 2.02 Da transformation pointed to a double-bond reduction and a putative reductase. Overexpression of each gene reproduced the commonality patterns, supporting the candidate genes and showing that the approach also applies to experimentally defined metabolite groups.