Metabolomics analysis and metabolite‐agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines

Abstract

Summary Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high‐density genetic map, we conducted a comprehensive metabolome study via widely targeted LC‐MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty‐four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite‐agronomic traits with the co‐localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co‐localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.

Metabolomics Analyses of Cotyledon and Plumule Showing the Potential Domestic Selection in Lotus Breeding

“…Phenotypic selection is the classical approach for breeding cultivars that adapt to the agricultural system, and molecular marker-assisted strategy has become increasingly popular in modern breeding because of the development of biological techniques. Recent studies have shown that metabolite data could be used to predict the performance of agronomic traits, ultimately assisting crop breeding [ 46 , 47 ]. Breeding for seed lotus has focused on improving the yield, such as increasing seed size, but consideration of the functional components has been neglected.…”

Breeding and biotechnological interventions for trait improvement: status and prospects

“…The targeted metabolome profiling of wheat kernel through LC–MS/MS followed by linkage analysis has resulted in the identification of 1005 mQTLs distributed unevenly in the genome. Twenty-two candidate genes underlying these mQTLs regulating the level of different metabolites were functionally annotated (Shi et al 2020 ). Comprehensive information about these QTLs is essential for facilitating the effective use of genes and genomic regions that regulate key traits.…”

Promoting Human Nutrition and Health through Plant Metabolomics: Current Status and Challenges

“…Understanding genetic and biochemical mechanisms underlying trait formation in crops under both normal and stressed conditions form the basis of the application of metabolomics in crop improvement for human nutrition and health [ 3 , 5 ]. Metabolomics, combined with other omics, facilitates crop improvement by linking a specific metabolite or a metabolic pathway more rationally with a trait of nutritive and healthy importance, through interspecies metabolomics comparison [ 31 , 43 , 46 , 47 ], metabolite quantitative trait loci (mQTLs) [ 48 , 49 ], and metabolome-based genome-wide association study (mGWAS) [ 50 , 51 , 52 , 53 ]. mGWAS alone or combined with phenotype GWAS (pGWAS) significantly increases the power of metabolomics in the successful identification of candidate causing genes of important traits in crops, such as rice [ 48 , 54 ], maize [ 55 , 56 ], wheat [ 49 , 52 , 57 ], tomato [ 58 , 59 ], and other crops [ 18 ].…”

“…Using combined metabolomics (LC-MS/MS) and mQTL analysis, the genetic architecture of the wheat kernel metabolome has been established, in which 1005 mQTLs are discovered and among them, 24 genes are responsible for the observed metabolites variations. There are clear genetic relationships between metabolites and agronomic traits [ 49 ], as found in rice [ 60 ], and two genes of them are found to be involved in the biosynthesis and modification of flavonoids that are associated with human health [ 49 ]. To understand better complex metabolite-trait association in wheat, mGWAS using 805 identified metabolites (LC-MS-MS) and 14,646 SNP markers among 182 wheat accessions identifies 1098 mGWAS associations, among them, 26 candidate genes are potential causative genes for 42 loci, and further validation studies on two candidate genes uncover the flavonoid decoration pathway in wheat kernel [ 52 ].…”