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作物学报 ›› 2010, Vol. 36 ›› Issue (10): 1624-1633.doi: 10.3724/SP.J.1006.2010.01624

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

玉米籽粒产量与产量构成因子的关系及条件QTL分析

彭勃1,王阳1,**,李永祥1,刘成2,张岩1,刘志斋1,3,谭巍巍1,王迪1,孙宝成2,石云素1,宋燕春1,王天宇1,*,黎裕1,*   

  1. 1中国农业科学院作物科学研究所，北京100081；2新疆农业科学院粮食作物研究所，新疆乌鲁木齐830000；3西南大学农学院，重庆400716
  • 收稿日期:2010-02-12 修回日期:2010-05-25 出版日期:2010-10-12 网络出版日期:2010-08-04
  • 通讯作者: 王天宇,E-mail:wangtianyu@263.net,Tel: 010-62186632;黎裕,E-mail:yuli@mail.caas.net.cn,Tel:010-62131196
  • 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（2006CB101700，1008CB118401），国家科技支撑计划项目（2006BAD13B03）和国家自然科学基金重点项目（30730063）资助。

Correlation Analysis and Conditional QTL Analysis of Grain Yield and Yield Components in Maize

PENG Bo1,WANG Yang1,**,LI Yong-Xiang1,LIU Cheng2,ZHANG Yan1,LIU Zhi-Zhai1,TAN Wei-Wei1,WANG Di1,SUN Bao-Cheng2,SHI Yun-Su1,SONG Yan-Chun1,WANG Tian-Yu1,*,LI Yu1,*   

  1. 1Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 2Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China；3Southwest University, Chongqing 400716, China
  • Received:2010-02-12 Revised:2010-05-25 Published:2010-10-12 Published online:2010-08-04
  • Contact: WANG Tian,E-mail:wangtianyu@263.net,Tel: 010-62186632;LI Yu,E-mail:yuli@mail.caas.net.cn,Tel: 010-62131196

PDF

2010

被引次数

47

摘要： 以我国玉米育种的骨干亲本齐319和黄早四构建的230个F2:3家系群体为材料，通过条件分析结合QTL定位方法探讨了单株产量(GYPP)与单株粒数(KNPP)和百粒重(KWEI)的遗传关系。结果表明，百粒重比单株粒数和单株产量遗传力高，受环境影响小。相关分析表明，单株粒数和百粒重与单株产量均呈现显著正相关，单株粒数与单株产量的相关系数更高。单株产量非条件QTL分析共定位到5个遗传主效应QTL和5对上位性位点，其中4个是控制3个性状(单株产量、单株粒数和百粒重)的一因多效性位点，1个是控制2个性状(单株产量和单株粒数)的一因多效性位点，全部5对上位性位点都与单株粒数和百粒重有关。条件QTL分析还检测到14个QTL位点及10对上位性位点，这些位点在非条件QTL分析中未被检测到，其效应较小。因此，单株粒数和百粒重与单株产量密切相关，通过改良单株粒数和百粒重可有效提高产量；条件QTL分析方法在单个QTL水平上证实了单株产量与单株粒数、百粒重较强的遗传相关性，并且能够检测到更多效应较小的QTL；发掘的两个效应较大的一因多效位点可为玉米高产分子育种和进一步精细定位提供理论参考。

关键词: 玉米, 产量, 产量构成因子, 条件QTL分析

Abstract: Grain yield per plant (GYPP) is one of the most complicated quantitative traits and determined by kernel number per plant (KNPP) and 100-kernel weight (KWEI) in maize. It is of great importance in genetic improvement to reveal genetic relationship between GYPP and two yield components (KNPP and KWEI). In the present study, an F2:3 population including 230 families derived from two foundation lines in maize breeding of China (Qi 319 and Huangzaosi) was used to conduct conditional analysis and QTL mapping. The genetic relationships between GYPP and yield components (KNPP and KWEI) were investigated. The results showed that the heritability of KWEI was higher than that of GYPP and KNPP, and KWEI was less influenced by environment. The phenotypic correlation analysis showed that both KNPP and KWEI were positively significantly correlated with GYPP, and the correlation coefficient between KNPP and GYPP was higher than that between KWEI and GYPP. A total of five QTLs and five pairs of epistatic loci were detected for GYPP by the unconditional QTL mapping. Among the five QTLs, four were found with pleiotropic effects on three traits (GYPP, KNPP, and KWEI), while another one controlled two traits (GYPP and KNPP). All of the five pairs of epistatic loci were related to both KNPP and KWEI. Furthermore, fourteen QTLs and ten pairs of epistatic loci with small effects were detected only in the conditional QTL mapping. Consequently, either KNPP or KWEI was closely related with GYPP at phenotypic level, suggesting that the improvement of KNPP and KWEI can efficiently contribute to high grain yield. The conditional QTL mapping demonstrated the strong genetic relationship between GYPP and two yield components (KNPP and KWEI) at the individual QTL level. The conditional QTL mapping could detect additional QTLs with small effects that were not detected in the unconditional QTL mapping. Two important QTLs with pleiotropic and larger effects could be used in maize breeding for high yield and fine mapping.

Key words: Maize, Yield, Yield components, Conditional QTL analysis

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