Exploring the genic resources underlying metabolites through mGWAS and mQTL in wheat: From large-scale gene identification and pathway elucidation to crop improvement

Abstract

Common wheat ( Triticum aestivum L.) is a leading cereal crop, but has lagged behind with respect to the interpretation of the molecular mechanisms of phenotypes compared with other major cereal crops such as rice and maize. The recently available genome sequence of wheat affords the pre-requisite information for efficiently exploiting the potential molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets. Meanwhile, the successful application of metabolomics as an emergent large-scale profiling methodology in several species has demonstrated this approach to be accessible for reaching the above goals. One such productive avenue is combining metabolomics approaches with genetic designs. However, this trial is not as widespread as that for sequencing technologies, especially when the acquisition, understanding, and application of metabolic approaches in wheat populations remain more difficult and even arguably underutilized. In this review, we briefly introduce the techniques used in the acquisition of metabolomics data and their utility in large-scale identification of functional candidate genes. Considerable progress has been made in delivering improved varieties, suggesting that the inclusion of information concerning these metabolites and genes and metabolic pathways enables a more explicit understanding of phenotypic traits and, as such, this procedure could serve as an -omics-informed roadmap for executing similar improvement strategies in wheat and other species.

Pan-transcriptome assembly combined with multiple association analysis provides new insights into the regulatory network of specialized metabolites in the tea plant Camellia sinensis

“…mGWAS has been used to study the genetic variation of high-throughput specialized metabolites in maize [ 18 , 19 ], rice [ 20 , 21 ], foxtail millet [ 55 ], wheat [ 56 , 57 ], and other species [ 58 ]. However, apart from a few well-known specialized metabolites, namely catechins, caffeine, and theanine [ 9 , 10 ], other specialized metabolites in tea plants have rarely been studied.…”

“…Owing to the inevitable limitations of methods that involve a single data type, the integration of multi-dimensional datasets such as genotype, metabolite, and gene expression data can compensate for missing or unreliable information in any single data type [ 59 ]. Multi-dimensional analysis is regarded as an important means of exploring biological mechanisms and can provide systematic clues for understanding complex biological problems [ 57 , 60 , 61 ]. For instance, Zhu et al (2017) analyzed genotype, transcriptome, and metabolome datasets from 610 tomato accessions, highlighting the impact of global breeding on tomato metabolite content [ 22 ].…”

Dissection of Crop Metabolome Responses to Nitrogen, Phosphorus, Potassium, and Other Nutrient Deficiencies

“…Furthermore, as an important bridge between genome and phenome, metabolite-based genome-wide association study (mGWAS) has recently been used in interactive functional genomics and metabolomics to understand the genetic bases of plant metabolism [ 14 , 97 ]. The mGWAS approach is performed to identify key genes involved in specific metabolic pathways in crops.…”

Lipidomics-Assisted GWAS (lGWAS) Approach for Improving High-Temperature Stress Tolerance of Crops

“…However, the effect of genetic associations on such complex traits is often small, and information about the biological processes underlying such traits is lacking in most crops [ 173 , 174 ]. Therefore, targeting intermediate traits that are closely related to the physiological and biochemical status of the stressed plants could reduce the complexity of HT trait [ 175 , 176 ]. Physiological trait-based breeding has been proven to be an ideal strategy for transferring QTLs/gene(s) conferring high-temperature tolerance [ 169 , 177 , 178 ].…”

“…The metabolome is defined as the last receiver of genetic information flow to attain the desired phenotype; in other words, metabolites are considered to be the bridge between genotypes and phenotypes [ 175 , 185 , 186 , 187 ]. A metabolic trait can be either a functional intermediate or a correlated biomarker for the physiological status of a plant [ 174 , 176 , 188 ].…”