It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution; less information, using conventional statistical methods. Bayesian-Mark...It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution; less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL; polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm; reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well; robust under different family structures; QTL effects. As family size increases; the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.展开更多
鉴于我国人口死亡率统计数据质量不高的实际和传统Lee-Carter死亡率预测模型两阶段方法存在的误差累积问题,本文采用贝叶斯Markov Chain Monte Carlo方法来预测我国人口死亡率。通过Win BUGS编程,文章在一体化框架下一次性给出模型的参...鉴于我国人口死亡率统计数据质量不高的实际和传统Lee-Carter死亡率预测模型两阶段方法存在的误差累积问题,本文采用贝叶斯Markov Chain Monte Carlo方法来预测我国人口死亡率。通过Win BUGS编程,文章在一体化框架下一次性给出模型的参数估计和未来死亡率的预测值。对研究结果的比较分析表明,贝叶斯方法不仅有效减少了数据质量问题的不利影响,提高了参数估计的稳健性,而且有效克服了参数估计和预测分开进行的弊端,在BIC值和残差项方差等模型选择标准上明显优于传统方法。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.30430500).
文摘It is a challenging issue to map Quantitative Trait Loci (QTL) underlying complex discrete traits, which usually show discontinuous distribution; less information, using conventional statistical methods. Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach is the key procedure in mapping QTL for complex binary traits, which provides a complete posterior distribution for QTL parameters using all prior information. As a consequence, Bayesian estimates of all interested variables can be obtained straightforwardly basing on their posterior samples simulated by the MCMC algorithm. In our study, utilities of Bayesian-MCMC are demonstrated using simulated several animal outbred full-sib families with different family structures for a complex binary trait underlied by both a QTL; polygene. Under the Identity-by-Descent-Based variance component random model, three samplers basing on MCMC, including Gibbs sampling, Metropolis algorithm; reversible jump MCMC, were implemented to generate the joint posterior distribution of all unknowns so that the QTL parameters were obtained by Bayesian statistical inferring. The results showed that Bayesian-MCMC approach could work well; robust under different family structures; QTL effects. As family size increases; the number of family decreases, the accuracy of the parameter estimates will be improved. When the true QTL has a small effect, using outbred population experiment design with large family size is the optimal mapping strategy.
文摘鉴于我国人口死亡率统计数据质量不高的实际和传统Lee-Carter死亡率预测模型两阶段方法存在的误差累积问题,本文采用贝叶斯Markov Chain Monte Carlo方法来预测我国人口死亡率。通过Win BUGS编程,文章在一体化框架下一次性给出模型的参数估计和未来死亡率的预测值。对研究结果的比较分析表明,贝叶斯方法不仅有效减少了数据质量问题的不利影响,提高了参数估计的稳健性,而且有效克服了参数估计和预测分开进行的弊端,在BIC值和残差项方差等模型选择标准上明显优于传统方法。
