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.

中文翻译：

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使用小麦（Triticum aestivum）重组自交系仁的代谢组学分析和代谢产物-农艺性状关联。

植物产生大量的代谢产物，这些代谢产物对它们的生长发育至关重要。但是，小麦代谢组的遗传结构尚未得到很好的研究。在这里，我们利用高密度遗传图谱，通过针对性强的LC-MS / MS进行了全面的代谢组学研究，以分析小麦籽粒的代谢。我们进一步结合了农艺性状，并分析了代谢产物与农艺性状之间的遗传关系。总共检测到1260个代谢特征。使用连锁分析，发现1005个代谢定量性状基因座（mQTL）在整个基因组中分布不均。发现二十四个候选基因可调节不同代谢产物的水平，其中两个在体外通过功能注释分析涉及类黄酮的合成和修饰。将代谢物-农艺性状的相关性分析与甲基化数量性状位点（mQTL）和表型QTL（pQTL）的共定位相结合，发现了代谢物与农艺性状之间的遗传关系。例如，使用相关性和共定位分析来识别候选物，该分析可以管理植物生长素的积累，从而影响每个穗的籽粒数（NGPS）。此外，代谢组学数据被用于预测小麦农艺性状的表现，发现代谢产物为NGPS和植物高度提供了强大的预测能力。这项研究使用代谢组学和关联分析来更好地了解小麦代谢的遗传基础，这最终将有助于小麦育种。