AUTHOR=Ellis Noel , Hattori Chie , Cheema Jitender , Donarski James , Charlton Adrian , Dickinson Michael , Venditti Giampaolo , Kaló Péter , Szabó Zoltán , Kiss György B. , Domoney Claire TITLE=NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites JOURNAL=Frontiers in Plant Science VOLUME=9 YEAR=2018 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01022 DOI=10.3389/fpls.2018.01022 ISSN=1664-462X ABSTRACT=

Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value.