| F2群体动态性状基因定位的极大似然分析 |
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| 引用本文： | 高会江,孙华,陈学辉,杨润清,徐士忠.F2群体动态性状基因定位的极大似然分析[J].东北林业大学学报,2006,34(1):72-77. |
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| 作者姓名： | 高会江 孙华 陈学辉 杨润清 徐士忠 |
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| 作者单位： | 东北农业大学,哈尔滨,150030;哈尔滨市产品质量监督检验所;上海交通大学;Dept.of Botany & Plant Science,University of California,Riverside,CA 92521 |
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| 摘 要： | 把表型值随着时间(生命时期、年龄、胎次等)或其他可以量化的因素(生理状态、生产水平、代谢率和环境条件等)变化的性状称为动态性状。受动物遗传育种中用来估计动态性状育种值的随机回归测定日模型思想的启发,将关于时间(测定日期)的Legendre多项式镶嵌在遗传模型的每个遗传效应中,以刻画QTL对动态性状变化过程的作用,从而建立起动态性状基因定位的数学模型。以F2遗传设计群体为例,论述了动态性状基因定位分析的基本原理,推导了QTL效应大小和位置的极大似然法估计过程。选择3次Legendre多项式为子模型,采用MonteCarlo方法模拟F2设计群体,研究抽样群体大小和测定日密度对不同遗传力QTL检测效力的影响。利用正交设计安排因素试验组合,分析模拟试验结果表明:高遗传力的要比低遗传力的QTL在检测时需要较少个体数和测定日抽样;但不论QTL的遗传力多大,300以上的群体大小和5%以上的测定日密度都可以保证足够高的检测效力。
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| 关 键 词： | 动态性状 基因定位 Legendre多项式 似然法 模拟 |
| 收稿时间： | 2005-07-11 |
| 修稿时间： | 2005-07-11 |
Maximum Likelihood Analysis for Mapping Dynamic Trait QTL in F2 Population |
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| Gao Huijiang,Sun Hua,Chen Xuehui,Yang Runqing,Xu Shizhong.Maximum Likelihood Analysis for Mapping Dynamic Trait QTL in F2 Population[J].Journal of Northeast Forestry University,2006,34(1):72-77. |
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| Authors: | Gao Huijiang Sun Hua Chen Xuehui Yang Runqing Xu Shizhong |
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| Institution: | School of Animal Science and Technology, Northeast Agricultural University, Harbin 150030, P. R. China ; Harbin Supervising and Inspecting Institute for Product Quality ; School of Agriculture and Biology, Shanghai Jiaotong University ; University of California, Riverside, CA92521 |
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| Abstract: | Dynamic traits are those phenotypic values change with time and other quantifiable factors such as age, parities, physiological status, performance level and environment etc. In estimation of breeding value of dynamic traits, the process with dynamic changes was described by a Legendre polynomial on time (test day) of certain order. Based on this opinion, the effect of dynamic trait loci could also be explained by inserting a Legendre polynomial in each genetic factor, and so the mathematic model for mapping dynamic trait loci was constructed. The objective of this study is to propose a general theoretical framework for embedding a dynamic process in the statistical analysis of QTL mapping for dynamic traits. A maximum likelihood based method, implemented with EM algorithm, was induced to estimate QTL location and effects on dynamic process. The new approach was demonstrated by Monte Carlo simulation and the effect of population size and test day density on detect power for the QTL with different heritability and the factor combination in simulation study was designed by orthogonal table. It showed that the individual number and test-day density have almost the same effect on QTL analysis and detection, and complement each other in same sample size. It needs fewer individuals and test-day records with higher heritability in the detection for dynamic trait loci. But the power of detection is sufficient with individuals above 300 and test-day density over 5% even if in lower heritability. |
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| Keywords: | Dynamic traits Mapping QTL Legendre polynomial Maximum likelihood Computer simulation |
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