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草业学报 ›› 2015, Vol. 24 ›› Issue (8): 118-129.DOI: 10.11686/cyxb2015071

• 论文 • 上一篇    下一篇

不同水分环境下小麦粒重QTL定位及遗传分析

胡亮亮1, 叶亚琼1, 吕婷婷1, 栗孟飞1, 刘媛1, 常磊2, 柴守玺2, 杨德龙1*, *   

  1. 1.甘肃省干旱生境作物学重点实验室,甘肃农业大学生命科学技术学院,甘肃 兰州 730070;
    2.甘肃农业大学农学院,甘肃 兰州730070
  • 出版日期:2015-08-20 发布日期:2015-08-20
  • 通讯作者: E-mail: yangdl@gsau.edu.cn
  • 作者简介:胡亮亮(1990-),男,河南安阳人,在读硕士。E-mail:hu931629850@163.com
  • 基金资助:
    国家自然科学基金项目(31460348,30960195),陇原青年创新人才扶持计划,甘肃农业大学“伏羲人才”计划(FXRC20130102),公益性行业(农业)科研专项(201303104)和甘肃省干旱生境作物学重点实验室开放基金项目(GSCS-2010-04)资助

QTL mapping and genetic analysis for grain weight in wheat (Triticum aestivum) under different water environments

HU Liang-Liang1, YE Ya-Qiong1, LV Ting-Ting1, LI Meng-Fei1, LIU Yuan1, CHANG Lei2, CHAI Shou-Xi2, YANG De-Long1, *   

  1. 1.Gansu Provincial Key Lab of Aridland Crop Science, College of Life Science and Technology, Gansu Agricultural University, Lanzhou 730070, China;
    2.College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
  • Online:2015-08-20 Published:2015-08-20

摘要: 为探讨小麦千粒重(TGW)分子数量性状遗传,及QTL与水分环境互作关系,本文以抗旱性强的冬小麦品种陇鉴19与水地高产品种Q9086杂交创建的重组近交系(recombinant inbred lines,RIL)群体120个株系为供试材料,采用条件复合区间作图法对3个环境不同水分条件下TGW进行QTL定位和遗传分析。结果表明,小麦RIL群体TGW对水分环境反应敏感,群体中各株系呈现广泛变异和超亲分离,属于微效多基因控制的复杂数量性状,易受水分环境影响。共检测到19个和38对控制TGW的加性QTL(A-QTL)和上位性QTL(AA-QTL),分布在除1A、3B、4D和6A以外的其他17条染色体上。这些A-QTL和AA-QTL表达通过正向或负向调控影响TGW表型变异,贡献率分别在1.24%~10.94%和0.38%~2.89%。发现了3个多环境均能稳定表达的A-QTL(Qtgw.acs-1B.1,Qtgw.acs-2A.1和Qtgw.acs-4A.1),以及4个A-QTL热点区域 [Xmag2064-Xbarc181(1B),Xwmc522-Xgwn122(2A),Xwmc446-Xgwm610(4A)和Xwmc603-Xbarc195(7A)]。所检测到的A-QTL和AA-QTL与干旱胁迫环境互作普遍负向调控TGW表型。加性效应和加性与环境的互作效应是决定小麦TGW的主要遗传因子。在干旱胁迫条件下,这种遗传主效应均不同程度降低TGW表型。本研究结果可为小麦抗旱遗传改良和分子标记辅助选择育种奠定理论基础。

Abstract: To better understand the molecular quantitative genetic and QTL patterns affecting thousand-grain weight (TGW) in wheat (Triticum aestivum) in different water environments, QTL mapping and genetic analysis were performed for TGW using a mixed linear model approach. TGW was evaluated for recombinant inbred lines (RIL) with 120 progenies from a cross between Longjian 19 (drought tolerant) and Q9086 (drought sensitive) under different water regimes in three environments. Phenotypic expression of TGW in the RILs was highly sensitive to water status and showed wide variation and transgressive segregation. TGW was found to be subject to complex quantitative genetic regulation by minor-effect polygenes, which were easily affected by water environments. A total of 19 additive QTL (A-QTL) and 38 pairs of epistatic QTL (AA-QTLs) were detected for TGW in wheat, distributed on all chromosomes including 1A, 3B, 4D and 6A. Expression of these QTLs influenced the phenotypic variation of TGW resulting in both up- and down-regulation. The magnitude of these effects on TGW ranged from 1.24%-10.94% and 0.38%-2.89%, respectively. Three A-QTLs, Qtgw.acs-1B.1, Qtgw.acs-2A.1 and Qtgw.acs-4A.1, were detected in multiple environments. In addition, four A-QTL hot-spot regions for TGW were also found at some specific locations, e.g., Xmag2064-Xbarc181 on chromosome 1B, Xwmc522-Xgwn122 on chromosome 2A, Xwmc446-Xgwm610 on chromosome 4A and Xwmc603-Xbarc195 on chromosome 7A. Most of the interaction effects of A-QTLs and AA-QTLs associated with drought-stressed environments were linked to down-regulation of the TGW variations. The additive and the additive×environment interaction effects may be the main genetic factors in TGW inheritance, and if so their expression would decrease TGW. The findings of this study should be useful for the genetic improvement of drought tolerance using molecular marker-assisted selection in wheat.