Departamento de Estatística, Universidade Federal de Viçosa, 36570-000 Viçosa, MG, Brazil.
Theor Appl Genet. 2013 Jul;126(7):1749-61. doi: 10.1007/s00122-013-2089-6. Epub 2013 Apr 20.
The objectives of this study were to implement a Bayesian framework for mixed models analysis in crop species breeding and to exploit alternatives for informative prior elicitation. Bayesian inference for genetic evaluation in annual crop breeding was illustrated with the first two half-sib selection cycles in a popcorn population. The Bayesian framework was based on the Just Another Gibbs Sampler software and the R2jags package. For the first cycle, a non-informative prior for the inverse of the variance components and an informative prior based on meta-analysis were used. For the second cycle, a non-informative prior and an informative prior defined as the posterior from the non-informative and informative analyses of the first cycle were used. Regarding the first cycle, the use of an informative prior from the meta-analysis provided clearly distinct results relative to the analysis with a non-informative prior only for the grain yield. Regarding the second cycle, the results for the expansion volume and grain yield showed differences among the three analyses. The differences between the non-informative and informative prior analyses were restricted to variance components and heritability. The correlations between the predicted breeding values from these analyses were almost perfect.
本研究的目的是在作物品种选育中实施混合模型分析的贝叶斯框架，并利用替代方法进行信息先验推断。通过对一个爆米花群体的前两个半同胞选择周期进行遗传评估的贝叶斯推断来说明年度作物选育中的贝叶斯框架。该贝叶斯框架基于 Just Another Gibbs Sampler 软件和 R2jags 包。对于第一个周期，使用方差分量倒数的非信息先验和基于荟萃分析的信息先验。对于第二个周期，使用非信息先验和定义为第一个周期非信息和信息分析的后验的信息先验。关于第一个周期，与仅使用非信息先验的分析相比，荟萃分析的信息先验的使用为谷物产量提供了明显不同的结果。关于第二个周期，膨化体积和谷物产量的结果在三种分析之间存在差异。三个分析之间的非信息和信息先验分析的结果仅限于方差分量和遗传力。这些分析中预测的育种值之间的相关性几乎是完美的。
