数字农科院2.0

Genetic Architecture Of Grain Yield I.n Bread Wheat Based O n Genome-Wide Association Studies

文献类型： 外文期刊

作者： Li, FJ; Wen, WE; Liu, JD; Zhang, Y; Cao, SH; He, ZH; Rasheed, A; Jin, H; Zhang, C; Yan, J; Zhang, PZ; Wan, YX; Xia, XC

作者机构：

关键词： GWAS; Marker-assisted selection; Single nucleotide polymorphism; Triticum aestivum

期刊名称： BMC PLANT BIOLOGY

ISSN： 1471-2229

年卷期： 2019 年 19 卷

页码：

收录情况： JCR（2021版）

摘要： BackgroundIdentification of loci for grain yield (GY) and related traits, and dissection of the genetic architecture are important for yield improvement through marker-assisted selection (MAS). Two genome-wide association study (GWAS) methods were used on a diverse panel of 166 elite wheat varieties from the Yellow and Huai River Valleys Wheat Zone (YHRVWD) of China to detect stable loci and analyze relationships among GY and related traits.ResultsA total of 326,570 single nucleotide polymorphism (SNP) markers from the wheat 90K and 660K SNP arrays were chosen for GWAS of GY and related traits, generating a physical distance of 14,064.8Mb. One hundred and twenty common loci were detected using SNP-GWAS and Haplotype-GWAS, among which two were potentially functional genes underpinning kernel weight and plant height (PH), eight were at similar locations to the quantitative trait loci (QTL) identified in recombinant inbred line (RIL) populations in a previous study, and 78 were potentially new. Twelve pleiotropic loci were detected on eight chromosomes; among these the interval 714.4-725.8Mb on chromosome 3A was significantly associated with GY, kernel number per spike (KNS), kernel width (KW), spike dry weight (SDW), PH, uppermost internode length (UIL), and flag leaf length (FLL). GY shared five loci with thousand kernel weight (TKW) and PH, indicating significantly affected by two traits. Compared with the total number of loci for each trait in the diverse panel, the average number of alleles for increasing phenotypic values of GY, TKW, kernel length (KL), KW, and flag leaf width (FLW) were higher, whereas the numbers for PH, UIL and FLL were lower. There were significant additive effects for each trait when favorable alleles were combined. UIL and FLL can be directly used for selecting high-yielding varieties, whereas FLW can be used to select spike number per unit area (SN) and KNS.ConclusionsThe loci and significant SNP markers identified in the present study can be used for pyramiding favorable alleles in developing high-yielding varieties. Our study proved that both GWAS methods and high-density genetic markers are reliable means of identifying loci for GY and related traits, and provided new insight to the genetic architecture of GY.

分类号：

  • 相关文献

[1]Genome-Wide Association Analysis of Fusarium Head Blight Resistance in Chinese Elite Wheat Lines. Xia, Xianchun,Tong, Hanwen,Rasheed, Awais,Yang, Lijun,Zhu, Weiwei,Zhang, Wei,Zhang, Wei,Fu, Luping,Gao, Chunbao,Gao, Chunbao,Zhu, Zhanwang,Zhu, Zhanwang,Chen, Ling,Hao, Yuanfeng,Liu, Jindong,Liu, Yike,Rasheed, Awais,He, Zhonghu,He, Zhonghu,Li, Junhui. 2020

[2]Genome-wide linkage mapping for canopy activity related traits using three RIL populations in bread wheat. Faji Li,Weie Wen,Jindong Liu,Shengnan Zhai,Xinyou Cao,Cheng Liu,Dungong Cheng,Jun Guo,Yan Zi,Ran Han,Xiaolu Wang,Aifeng Liu,Jianmin Song,Jianjun Liu,Haosheng Li,Xianchun Xia. 2021

[3]Qtl Mapping Of Adult-Plant Resistance T.o Leaf Rust In T he Wheat Cross Zhou 8425B/Chinese Spring Using High-Density Snp Markers. Zhang, PP, Yin, GH, Zhou, Y, Qi, AY, Gao, FM, Xia, XC, He, ZH, Li, ZF, Liu, DQ. 2017

[4]Wheat Functional Genomics In The E.ra Of Next Generation S equencing: An Update. Jia, ML, Guan, JT, Zhai, ZW, Geng, SF, Zhang, XY, Mao, L, Li, AL. 2018

[5]蛋鸡资源群体的盲肠长度全基因组关联分析. 王星果,曲亮,卢建,窦套存,马猛,胡玉萍,沈曼曼,郭军,李尚民,王克华. 2018

[6]中国籼稻群体的遗传结构解析及12个株型性状的全基因组关联分析. 鲁清,章孟臣,魏兴华. 2015

[7]北京油鸡血清IgG含量的GWAS初步分析. 张磊,刘冉冉,赵桂苹,文杰,郑麦青,吴丹,胡耀东,李鹏,刘丽. 2011

[8]黄瓜发芽期耐热性评价及全基因组关联分析. 张松,苗晗,宋子超,薄凯亮,王烨,顾兴芳,张圣平. 2019

[9]功能基因筛选方法的研究进展. 姚雅馨,喇永富,汤继顺,贺小云,张壮彪,储明星. 2020

[10]玉米叶片叶绿素含量的全基因组关联性分析. 滕守振1,,2 汪海2 梁海生1,,2 辛红佳2 李圣彦2 郎志宏2. 2017

[11]基于通路分析方法诠释肉鸡体重性状全基因组关联研究. 王杰,刘璐,刘杰,赵桂苹,刘冉冉,郑麦青,文杰. 2017

[12]黄瓜核心种质幼苗耐低温性评价及GWAS分析. 王伟平,宋子超,薄凯亮,董邵云,魏爽,苗晗,李锦斌,张圣平,顾兴芳. 2019

[13]黄瓜核心种质幼苗耐热性评价及GWAS分析. 魏爽,张松,薄凯亮,王伟平,苗晗,董邵云,顾兴芳,张圣平. 2019

[14]利用两种统计模型对中国肉用西门塔尔牛屠宰性状的全基因组关联分析. 常天鹏,夏江威,宝金山,金生云,朱波,徐凌洋,陈燕,张路培,高雪,李俊雅,高会江. 2018

[15]基于SNP标记的小麦籽粒性状全基因组关联分析. 张芳,任毅,曹俊梅,李法计,夏先春,耿洪伟. 2021

[16]小麦芒基因定位及其与农艺性状的相关性分析. 李玲,刘盼,张蕾,张浩,贾继增,高丽锋. 2021

[17]桃花花型（铃形/蔷薇形）基因型鉴定、分子标记开发与利用. 吉爽秋,王力荣,李勇,朱更瑞,曹珂,方伟超,陈昌文,王新卫,张琦,吴金龙. 2022

[18]油菜籽脂肪组成的遗传结构剖析. 杨雨婷,于文凯,徐颖,王会,梅德圣,蒋立希,胡琼,刘佳. 2022

[19]基于GWAS和BSA-seq挖掘辣椒果实中辣椒红色素含量的QTL区间及候选基因. 班国梁,曹亚从,张正海,于海龙,吴华茂,李戎轩,赵红,张伟丽,聂智星,宋红霞,王立浩. 2025

[20]基于MAGIC群体的烟草抗黑胫病全基因组关联分析. 潘怡,刘洪泰,吉白璐,王红,黄立钰,程立锐. 2024

作者其他论文 更多>>