Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement

Abstract

It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC 2 F 7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling-stage leaf, grouting-stage leaf, young kernel and mature kernel) with clear tissue-specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi-tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs.

PANOMICS meets germplasm

“…Recently, Li and co‐workers identified 65 primary metabolites that were quantified in four different tissues that showed clear tissue‐specific patterns. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were examined, which were distributed unevenly across the genome and included two QTL hotspots (Li et al , 2019).…”

Combining metabolomic and transcriptomic approaches to assess and improve crop quality traits

“…In this sense, recent advances in metabolite profiling technologies, which now allow the simultaneous detection and quantification of thousands of metabolites, combined with genomic and transcriptomic platforms, are fundamental in dissecting crop composition and in identifying the genetic variants underlying metabolic content (de Abreu e Lima et al 2018; Ballester et al 2016;Garbowicz et al 2018;Labadie et al 2020;Li et al 2019;Osorio et al 2011Osorio et al , 2019Rambla et al 2016;Vallarino et al 2019). While the integration of genetic and metabolic information is a powerful strategy to dissect the bases of plant metabolism and to associate complex traits with genotype, it is also a key strategy for the breeding of high-yielding and nutritionally rich crops (Luo 2015;Wen et al 2018).…”

Understanding Omics Driven Plant Improvement and de novo Crop Domestication: Some Examples

“…Now, the targeted and un-targeted metabolomics approach have been coupled with genomics data for carrying out metabolomics-based quantitative trait locus (mQTL) and metabolic genome-wide association studies (mGWAS) studies (Wen et al, 2015;Chen et al, 2016); which simultaneously identifies the genomic region, causal genes and key metabolites and associated metabolic pathways that govern particular trait in plants. Recently, Li K. et al (2019) identified 65 primary metabolites viz 22 amino acids, 21 organic acids, 12 sugars, four amines and six miscellaneous metabolites in the leaf of teosinte (an ancestor of maize) and identifies advantageous genes present in the wild relative associated with grain yield and shape trait in maize. In tomato, for one of the important trait accumulation of secondary metabolite in fruit was analyzed, and reported several subset of mQTLs-including mQTLs associated with acylsugar, hydroxycinnamates, naringenin chalcone, and a range of glycoalkaloids (Alseekh et al, 2015).…”