A Genome-Wide Association Study Reveals a Rich Genetic Architecture of Flour Color-Related Traits in Bread Wheat

文献类型: 外文期刊

第一作者: Shengnan Zhai

作者: Shengnan Zhai;Jindong Liu;Dengan Xu;Weie Wen;Jun Yan;Pingzhi Zhang;Yingxiu Wan;Shuanghe Cao;Yuanfeng Hao;Xianchun Xia;Wujun Ma;Zhonghu He

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关键词: brightness (L*); candidate gene; GWAS; redness (a*); yellowness (b*); yellow pigment content (YPC)

期刊名称:FRONTIERS IN PLANT SCIENCE ( 影响因子:5.753; 五年影响因子:6.612 )

ISSN: 1664-462X

年卷期: 2018 年 9 卷

页码:

收录情况: SCI

摘要: Flour color-related traits, including brightness (L*), redness (a*), yellowness (b*) and yellow pigment content (YPC), are very important for end-use quality of wheat. Uncovering the genetic architecture of these traits is necessary for improving wheat quality by marker-assisted selection (MAS). In the present study, a genome-wide association study (GWAS) was performed on a collection of 166 bread wheat cultivars to better understand the genetic architecture of flour color-related traits using the wheat 90 and 660 K SNP arrays, and 10 allele-specific markers for known genes influencing these traits. Fifteen, 28, 25, and 32 marker-trait associations (MTAs) for L*, a*, b*, and YPC, respectively, were detected, explaining 6.5-20.9% phenotypic variation. Seventy-eight loci were consistent across all four environments. Compared with previous studies, Psy-A1, Psy-B1, Pinb-D1, and the 1B.1R translocation controlling flour color-related traits were confirmed, and four loci were novel. Two and 11 loci explained much more phenotypic variation of a* and YPC than phytoene synthase 1 gene (Psy1), respectively. Sixteen candidate genes were predicted based on biochemical information and bioinformatics analyses, mainly related to carotenoid biosynthesis and degradation, terpenoid backbone biosynthesis and glycolysis/gluconeogenesis. The results largely enrich our knowledge of the genetic basis of flour color-related traits in bread wheat and provide valuable markers for wheat quality improvement. The study also indicated that GWAS was a powerful strategy for dissecting flour color-related traits and identifying candidate genes based on diverse genotypes and high-throughput SNP arrays.

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