Metabolomics and Analysis of Quantitative Traits: State of the Art, Challenges and Future Perspectives

Modern crop cultivars display limited genetic variability, and important traits in crop breeding are regulated by numerous genes. Regulation of complex traits can be studied via quantitative trait loci based analyses, whereby elite cultivars are crossed with exotic germplasm to generate collections of introgression, recombinant and backcrossed inbred lines. The availability of increasingly high-quality genetic maps facilitates the investigation of the underlying genetic basis for a given trait. The chemical composition of fruits and seeds is inherently associated to important complex quality traits in fruits, e.g. BRIX, sugar content in grapes, amino acids as precursors of volatiles in melon and grape. In seeds, the regulation of metabolism can infer on the seed physiology upon imbibition and in grains it has a significant  nutritional value, e.g. the content of trace amino acids like lysine. In the context of generating metabolically enhanced crops, high-throughput phenotyping approaches such as transcriptomics and metabolomics can be applied to mapping populations to delucidate the regulation of valuable metabolic traits. Nevertheless the vast dataset generated necessitates bioinformatics approaches for complexity reduction and data integration. For this purpose, graph-theory based analyses is being implemented on omics data from mapping populations. The application of these methods provide a new strategies to identify quantitative trait loci for crop compositional traits.