Integrated genomics-based mapping reveals the genetics underlying maize flavonoid biosynthesis. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Jin, Min; Zhang, Xuehai; Zhao, Mingchao; Deng, Min; Du, Yuanhao; Zhou, Yang; Wang, Shouchuang; Tohge, Takayuki; Fernie, Alisdair; Willmitzer, Lothar; Brotman, Yariv; Yan, Jianbing; Wen, Weiwei
• Is Part Of: BMC plant biology. Volume 17:Issue 1(2017)
• Page Start: 1
• Page End: 17
• Publication Date: 2017-12
• Subjects: Maize -- Flavonoid -- Natural variation -- Linkage mapping -- Association analysis -- Co-expression network; Plant molecular biology -- Periodicals; Botany -- Periodicals; 580.5
• DOI: 10.1186/s12870-017-0972-z
• Languages: English
• ISSNs: 1471-2229

Abstract

Abstract Background Flavonoids constitute a diverse class of secondary metabolites which exhibit potent bioactivities for human health and have been indicated to play an important role in plant development and defense. However, accumulation and variation of flavonoid content in diverse maize lines and the genes responsible for their biosynthesis in this important crop remain largely unknown. In this study, we combine genetic mapping, metabolite profiling and gene regulatory network analysis to further enhance understanding of the maize flavonoid pathway. Results We repeatedly detected 25 QTL corresponding to 23 distinct flavonoids across different environments or populations. In addition, a total of 39 genes were revealed both by an expression based network analysis and genetic mapping. Finally, the function of three candidate genes, including two UDP-glycosyltransferases (UGT ) and an oxygenase which belongs to the flavone synthase super family, was revealed via preliminary molecular functional characterization. Conclusion We explored the genetic influences on the flavonoid biosynthesis based on integrating the genomic, transcriptomic and metabolomic information which provided a rich source of potential candidate genes. The integrated genomics based genetic mapping strategy is highly efficient for defining the complexity of functional genetic variants and their respective regulatory networks as well as in helping to select candidate genes and allelic variance before embarking on laborious transgenic validations.