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 cha...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
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.
