Combining Quantitative Genetics Approaches with Regulatory Network Analysis to Dissect the Complex Metabolism of the Maize Kernel  . Issue 1 (10th November 2015)

• Record Type: Journal Article
• Authors: Wen, Weiwei; Liu, Haijun; Zhou, Yang; Jin, Min; Yang, Ning; Li, Dong; Luo, Jie; Xiao, Yingjie; Pan, Qingchun; Tohge, Takayuki; Fernie, Alisdair R.; Yan, Jianbing
• Is Part Of: Plant physiology. Volume 170:Issue 1(2016)
• Page Start: 136
• Page End: 146
• Publication Date: 2015-11-10
• Subjects: Plant physiology -- Periodicals; Botany -- Periodicals; Periodicals; Electronic journals; 571.2
• DOI: 10.1104/pp.15.01444
• Languages: English
• ISSNs: 0032-0889

Abstract

Abstract : A metabolic quantitative trait loci study combined with a regulatory network unraveled the genetic architecture of the natural variation of 155 metabolites in mature maize kernels. Abstract: Metabolic quantitative trait locus (QTL ) studies have allowed us to better understand the genetic architecture underlying naturally occurring plant metabolic variance. Here, we use two recombinant inbred line (RIL ) populations to dissect the genetic architecture of natural variation of 155 metabolites measured in the mature maize ( Zea mays ) kernel. Overall, linkage mapping identified 882 metabolic QTL s in both RIL populations across two environments, with an average of 2.1 QTL s per metabolite. A large number of metabolic QTL s (more than 65%) were identified with moderate effects ( r 2 = 2.1%–10%), while a small portion (less than 35%) showed major effects ( r 2 > 10%). Epistatic interactions between these identified loci were detected for more than 30% of metabolites (with the proportion of phenotypic variance ranging from 1.6% to 37.8%), implying that genetic epistasis is not negligible in determining metabolic variation. In total, 57 QTL s were validated by our previous genome-wide association study on the same metabolites that provided clues for exploring the underlying genes. A gene regulatory network associated with the flavonoid metabolic pathway was constructed based on the transcriptional variations of 28, 769 genes in kernels (15 d after pollination) of 368 maize inbred lines. A large number of genes (34 of 58) in this network overlapped with previously defined genes controlled by maize PERICARP COLOR1, while three of them were identified here within QTL intervals for multiple flavonoids. The deeply characterized RIL populations, elucidation of metabolic phenotypes, and identification of candidate genes lay the foundation for maize quality improvement.