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利用Bayesian-MCMC方法进行畜禽复杂离散性状QTL定位 被引量：2

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摘要 复杂离散性状由于表型数据呈离散分布并且提供信息量过小,因此很难用常规的统计方法对此类性状的QTL进行定位研究．Bayesian-MCMC方法是复杂离散性状QTL定位的重要手段,该方法通过所有先验信息来推导QTL参数的后验分布并利用Markov Chain随机过程进行抽样的方法对目标参数进行统计推断．利用Monte Carlo方法,针对畜禽远交群体模拟产生多个全同胞家系的2级分类复杂离散性状,然后基于IBD方差组分的随机模型的定位策略,同时利用MCMC的3种不同抽样技术(Gibbs抽样、Metropolis抽样和Reversible Jump MCMC抽样)产生相应QTL参数的后验样本,并进行了目标参数的Bayesian统计推断．结果表明:Bayesian-MCMC方法能够对不同家系结构和QTL效应水平下复杂离散性状QTL进行有效检测;当家系含量增加时, QTL定位的精确性和准确性提高,并可适用于效应更小QTL的检测．
作者 刘剑锋 张沅 张勤 王立贤 张纪刚
机构地区 中国农业大学动物科技学院 中国农业科学院畜牧研究所
出处 《中国科学（C辑）》 CSCD 北大核心 2006年第3期240-246,共7页 Science in China(Series C)
基金 国家自然科学基金重点资助项目(批准号:30430500)
关键词 复杂离散性状 QTL定位 Bayesian-MCMC方法 远交群体 IBD方差组分随机模型
分类号 S813.1 [农业科学—畜牧学]
  • 相关文献

参考文献19

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共引文献39

同被引文献22

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引证文献2

二级引证文献5

中国科学（C辑）
2006年 第3期

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