Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana

Plant primary metabolism is a highly coordinated, central, and complex network of biochemical processes regulated at both the genetic and post-translational levels. The genetic basis of this network can be explored by analyzing the metabolic composition of genetically diverse genotypes in a given plant species. Here, we report an integrative strategy combining quantitative genetic mapping and metabolite‒transcript correlation networks to identify functional associations between genes and primary metabolites in Arabidopsis thaliana. Genome-wide association study (GWAS) was used to identify metabolic quantitative trait loci (mQTL). Correlation networks built using metabolite and transcript data derived from a previously published time-course stress study yielded metabolite‒transcript correlations identified by covariation. Finally, results obtained in this study were compared with mQTL previously described. We applied a statistical framework to test and compare the performance of different single methods (network approach and quantitative genetics methods, representing the two orthogonal approaches combined in our strategy) with that of the combined strategy. We show that the combined strategy has improved performance manifested by increased sensitivity and accuracy. This combined strategy allowed the identification of 92 candidate associations between structural genes and primary metabolites, which not only included previously well-characterized gene‒metabolite associations, but also revealed novel associations. Using loss-of-function mutants, we validated two of the novel associations with genes involved in tyrosine degradation and in β-alanine metabolism. In conclusion, we demonstrate that applying our integrative strategy to the largely untapped resource of metabolite–transcript associations can facilitate the discovery of novel metabolite-related genes. This integrative strategy is not limited to A. thaliana, but generally applicable to other plant species.

植物初生代谢（Plant primary metabolism）是一套高度协同、兼具核心地位且复杂度极高的生化过程网络，其调控同时涉及遗传与翻译后两个层面。解析该网络的遗传基础，可通过分析特定植物物种内遗传多样性基因型的代谢组组成来实现。本研究报道了一种整合性研究策略，该策略结合定量遗传定位与代谢物-转录本关联网络分析，用以鉴定拟南芥（Arabidopsis thaliana）中基因与初生代谢物之间的功能关联。本研究采用全基因组关联分析（Genome-wide association study, GWAS）来鉴定代谢数量性状位点（metabolic quantitative trait loci, mQTL）。利用已发表的时序胁迫研究中获取的代谢物与转录本数据构建关联网络，通过共变异分析得到代谢物与转录本之间的关联关系。最后，将本研究所得结果与已报道的mQTL进行比对验证。我们搭建了一套标准化统计框架，用以测试并对比单一方法（关联网络分析方法与定量遗传学方法，即本整合策略所结合的两类正交研究路径）与整合策略的整体性能。研究结果表明，整合策略的性能得到显著提升，具体体现为灵敏度与准确性的双重优化。该整合策略成功鉴定出92个结构基因与初生代谢物之间的候选关联，其中既包含已有充分研究的基因-代谢物关联，也发掘出全新的功能关联。我们利用功能丧失型突变体，验证了其中两个全新关联，分别对应参与酪氨酸降解与β-丙氨酸代谢的相关基因。综上，本研究证明，将该整合策略应用于尚未充分开发的代谢物-转录本关联资源，可有效助力新型代谢物相关基因的发掘。该整合策略并非仅适用于拟南芥，而是可推广应用至其他植物物种。