Open AccessApplication of nontargeted metabolite profiling to discover novel markers of quality traits in an advanced population of malting barley
Author(s) -
Heuberger Adam L.,
Broeckling Corey D.,
Kirkpatrick Kaylyn R.,
Prenni Jessica E.
Publication year - 2014
Publication title -
plant biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.525
H-Index - 115
eISSN - 1467-7652
pISSN - 1467-7644
DOI - 10.1111/pbi.12122
Subject(s) - biology , profiling (computer programming) , metabolite , metabolite profiling , population , doubled haploidy , computational biology , quantitative trait locus , microbiology and biotechnology , genetics , gene , biochemistry , computer science , demography , sociology , operating system
Summary The process of breeding superior varieties for the agricultural industry is lengthy and expensive. Plant metabolites may act as markers of quality traits, potentially expediting the appraisal of experimental lines during breeding. Here, we evaluated the utility of metabolites as markers by assessing metabolic variation influenced by genetic and environmental factors in an advanced breeding setting and in relation to the phenotypic distribution of 20 quality traits. Nontargeted liquid chromatography–mass spectrometry metabolite profiling was performed on barley ( H ordeum vulgare L .) grain and malt from 72 advanced malting barley lines grown at two distinct but climatically similar locations, with 2‐row and 6‐row barley as the main genetic factors. 27 420 molecular features were detected, and the metabolite and quality trait profiles were similarly influenced by genotype and environment; however, malt was more influenced by genotype compared with barley. An O 2 PLS model characterized molecular features and quality traits that covaried, and 1319 features associated with at least one of 20 quality traits. An indiscriminant MS / MS acquisition and novel data analysis method facilitated the identification of metabolites. The analysis described 216 primary and secondary metabolites that correlated with multiple quality traits and included amines, amino acids, alkaloids, polyphenolics and lipids. The mechanisms governing quality trait–metabolite associations were interpreted based on colocalization to genetic markers and their gene annotations. The results of this study support the hypothesis that metabolism and quality traits are co‐influenced by relatively narrow genetic and environmental factors and illustrate the utility of grain metabolites as functional markers of quality traits.