000			03042nab a22004217a 4500
999			_c60019 _d60011
001			60019
003			MX-TxCIM
005			20211006082418.0
008			190124s2018 sz |||pf|p||| 00| 0 eng d
024	8		_a10.3389/fpls.2018.01136
040			_aMX-TxCIM
041			_aeng
100	0		_93029 _aShengnan Zhai
245	1		_aA genome-wide association study reveals a rich genetic architecture of flour color-related traits in bread wheat
260			_aSwitzerland : _bFrontiers, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aFlour 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.
526			_aWC
546			_aText in English
650		7	_91265 _aSoft wheat _2AGROVOC
650		7	_2AGROVOC _91130 _aGenetics
650		7	_2AGROVOC _91113 _aFlours
700	0		_93032 _aJindong Liu
700	0		_95904 _aDengan Xu
700	0		_93035 _aWeie Wen
700	1		_9381 _aYan Jun
700	0		_95892 _aPingzhi Zhang
700	0		_95893 _aYingxiu Wan
700	0		_95093 _aShuanghe Cao
700	1		_8INT3329 _9919 _aYuanfeng Hao _gGlobal Wheat Program
700	0		_9377 _aXianchun Xia
700	0		_91979 _aWujun Ma
700	1		_aHe Zhonghu _gGlobal Wheat Program _8INT2411 _9838
773	0		_gv. 9, art. 1136 _tFrontiers in Plant Science _wu56875 _x1664-462X
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19895
942			_2ddc _cJA _n0