作物学报2016，Vol.42Issue(11)：1592-1600,9.DOI:10.3724/SP.J.1006.2016.01592
不同密度下玉米穗部性状的QTL分析
QTL Mapping for Ear Architectural Traits under Three Plant Densities in Maize
摘要
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 ex-periments 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 ex-plained phenotypic variation ranging from 4.20% to 14.07%. One QTL related to ear row number on chromosome 2 was repeat-edly 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.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)Key words
Maize/Ear architectural traits/Plant density/QTL/BLUP引用本文复制引用
王辉,梁前进,胡小娇,李坤,黄长玲,王琪,何文昭,王红武,刘志芳..不同密度下玉米穗部性状的QTL分析[J].作物学报,2016,42(11):1592-1600,9.基金项目
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。 This study was supported by the National Basic Research Program of China (2014CB138200), the Program of Beijing Municipal Science and Technology (D141100005014003), and the Agricultural Science and Technology Innovation Program (ASTIP) of CAAS （973计划）
