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A Genome-Wide association study reveals a rich genetic architecture of flour color-related traits in bread wheat

Zhai, S., Liu, J., Xu, D., Wen, W., Yan, J., Zhang, P., Wan, Y., Cao, S., Hao, Y., Xia, X., Ma, W. and He, Z. (2018) A Genome-Wide association study reveals a rich genetic architecture of flour color-related traits in bread wheat. Frontiers in Plant Science, 9 .

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Abstract

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.

Publication Type: Journal Article
Murdoch Affiliation: School of Veterinary and Life Sciences
Publisher: Frontiers
Copyright: © 2018 Zhai, Liu, Xu, Wen, Yan, Zhang, Wan, Cao, Hao, Xia, Ma and He.
URI: http://researchrepository.murdoch.edu.au/id/eprint/41722
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