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作物学报 ›› 2016, Vol. 42 ›› Issue (11): 1592-1600.doi: 10.3724/SP.J.1006.2016.01592

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

不同密度下玉米穗部性状的QTL分析

王辉,梁前进,胡小娇,李坤,黄长玲,王琪,何文昭,王红武*,刘志芳*   

  1. 中国农业科学院作物科学研究所,北京 100081
  • 收稿日期:2016-03-03 修回日期:2016-06-20 出版日期:2016-11-12 网络出版日期:2016-07-04
  • 通讯作者: 刘志芳,E-mail: liuzhifang@caas.cn; 王红武,E-mail: wanghongwu@caas.cn
  • 基金资助:

    本研究由国家重点基础研究发展计划(973计划)项目(2014CB138200),北京市科技计划项目(D141100005014003)和中国农业科学院科技创新工程项目资助。

QTL Mapping for Ear Architectural Traits under Three Plant Densities in Maize

WANG Hui,LIANG Qian-Jin,HU Xiao-Jiao,LI Kun,HUANG Chang-Ling,WANG Qi,HE Wen-Zhao,WANG Hong-Wu*,LIU Zhi-Fang*   

  1. Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China?
  • Received:2016-03-03 Revised:2016-06-20 Published:2016-11-12 Published online:2016-07-04
  • Contact: Liu Zhifang,E-mail: liuzhifang@caas.cn; Wang hongwu,E-mail: wanghongwu@caas.cn
  • Supported by:

    This study was supported by the China National 973 Project (2014CB138200), Program of Beijing Municipal Science and Technology (D141100005014003), and Agricultural Science and Technology Innovation Program (ASTIP) of CAAS.

摘要:

为研究玉米穗部性状对不同种植密度的遗传响应,以郑58和HD568为亲本构建的220个重组自交系群体为材料,于2014年春、2014年冬及2015年春分别在北京和海南进行3个种植密度的田间试验,调查玉米穗长、穗粗、穗行数和行粒数等表型性状。利用SAS软件计算穗部性状的最优线性无偏估计值(BLUP),并采用完备区间作图法进行QTL定位。结果表明,在3个种植密度下共检测到42个QTL,单个QTL可解释4.20%~14.07%的表型变异。3个种植密度下同时检测到位于第2染色体上控制穗行数的QTL。2个种植密度下同时检测到4个与穗粗、穗行数和行粒数有关的QTL,其中第4染色体上1个与穗行数有关的主效QTL,在低、中种植密度下可分别解释表型变异的10.88%和14.07%。此外,在第2、4和9染色体上检测到3个同时调控不同穗部性状的QTL。研究结果表明玉米穗部性状在不同种植密度下的遗传调控发生变化,在不同密度下共同检测到的稳定QTL可应用于精细定位或开发玉米耐密性分子标记用于辅助育种。

关键词: 玉米, 穗部性状, 密度, 数量性状位点(QTL), 最优线性无偏估计(BLUP)

Abstract:

To identify genetic factors of ear architectural traits response to plant density, we developed a recombination inbred line (RIL) mapping population with 220 families from a cross between two maize inbred lines, Zheng 58 and HD568. The filed experiments were performed in 2014 and 2015 seasons of Beijing and Hainan. The ear architectural traits including ear length, ear diameter, ear row number and kernel number per row were evaluated under three plant densities in each environment. With the BLUP value estimated by SAS software, QTLs for ear architectural traits were detected by inclusive composite interval mapping (ICIM) using Windows QTL ICI-Mapping software. In total, 42 QTLs were detected under three plant densities, each QTL explained phenotypic variation ranging from 4.20% to 14.07%. One QTL related to ear row number on chromosome 2 was repeatedly detected under three plant densities. Four QTLs related to ear diameter, ear row number and kernel number per row were commonly detected under two plant densities, among them an ear row number QTL was located on chromosome 4 with explained 10.88% and 14.07% of phenotypic variance under plant density of 52 500 plants ha-1 and 67 500 plants ha-1. In addition, we found three QTLs for different ear architectural traits on chromosomes 2, 4 and 9 simultaneously. This study revealed the genetic mechanisms of ear architectural traits changed under different plant densities. The QTLs stably expressed under different plant densities can be applied in fine mapping and marker assisted selection in density tolerance breeding of maize.

Key words: Maize, Ear architectural traits, Plant density, QTL, BLUP

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