中国农业科学 ›› 2005, Vol. 38 ›› Issue (09): 1717-1724 .

• 作物遗传育种.种质资源 •    下一篇

多个相关数量性状主基因的联合分析方法

肖静,徐辰武,胡治球,汤在祥,隋炯明,李欣   

  1. 扬州大学数量遗传研究室
  • 收稿日期:2004-10-15 修回日期:1900-01-01 出版日期:2005-09-10 发布日期:2005-09-10
  • 通讯作者: 徐辰武

Joint Analysis Method for Major Genes Controlling Multiple Correlated Quantitative Traits

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  1. 扬州大学数量遗传研究室
  • Received:2004-10-15 Revised:1900-01-01 Online:2005-09-10 Published:2005-09-10

摘要: 根据多个相关数量性状的主基因+多基因遗传模型,首次提出利用多个相关数量性状进行主基因检测、主基因效应与变异估计的联合分离分析方法。该方法以EM算法实现的极大似然估计方法进行主基因的效应估计,以似然比统计量进行主基因的各种遗传假设检验。大量的计算机模拟研究表明,多性状联合分析不仅可以提高主基因的被发现能力,而且可以增加主基因效应估计值的准确度和精确度。以水稻杂交组合多蘖矮×中花11的F2群体597个植株株高和分蘖数为例演示了分析程序。结果表明该组合的株高和分蘖数受同一主基因控制。该主基因对株高的加性和显性效应分别为-21.3 cm和40.6 cm,表现为超显性;对分蘖数的加性和显性效应则分别为22.7和-25.3,表现为接近完全显性。

关键词: 相关数量性状, 主基因, 联合分离分析, 极大似然估计, EM算法

Abstract: Based on the major gene and polygene mixed inheritance model for multiple correlated quantitative traits, the authors proposed a new joint segregation analysis method of major gene controlling multiple correlated quantitative traits, which include major gene detection and its effect and variation estimation. The effect and variation of major gene are estimated by the maximum likelihood method implemented via expectation-maximization (EM) algorithm. Major gene is tested with the likelihood ratio (LR) test statistic. Extensive simulation studies showed that joint analysis not only increases the statistical power of major gene detection but also improves the precision and accuracy of major gene effect estimates. An example of the plant height and the number of tiller of F2 population in rice cross Duonieai×Zhonghua 11 was used in the illustration. The results indicated that the genetic difference of these two traits in this cross refers to only one pleiotropic major gene. The additive effect and dominance effect of the major gene are estimated as -21.3cm and 40.6cm on plant height, and 22.7 and -25.3 on number of tiller, respectively. The major gene shows overdominance for plant height and close to complete dominance for number of tillers.

Key words: Multiple correlated quantitative traits, Major gene, Joint segregation analysis, Maximum likelihood estimation, EM algorithm