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首页> 外文期刊>Genetical Research >Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops.
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Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops.

机译：通过考虑自花授粉作物的基因型-环境相互作用，对繁殖值进行贝叶斯预测。

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摘要

In self-pollinating populations, individuals are characterized by a high degree of inbreeding. Additionally, phenotypic observations are highly influenced by genotype-by-environment interaction effects. Usually, Bayesian approaches to predict breeding values (in self-pollinating crops) omit genotype-by-environment interactions in the statistical model, which may result in biased estimates. In our study, a Bayesian Gibbs sampling algorithm was developed that is adapted to the high degree of inbreeding in self-pollinated crops and accounts for interaction effects between genotype and environment. As related lines are supposed to show similar genotype-by-environment interaction effects, an extended genetic relationship matrix is included in the Bayesian model. Additionally, since the coefficient matrix C in the mixed model equations can be characterized by rank deficiencies, the pseudoinverse of C was calculated by using the nullspace, which resulted in a faster computation time. In this study, field data of spring barley lines and data of a 'virtual' parental population of self-pollinating crops, generated by computer simulation, were used. For comparison, additional breeding values were predicted by a frequentist approach. In general, standard Bayesian Gibbs sampling and a frequentist approach resulted in similar estimates if heritability of the regarded trait was high. For low heritable traits, the modified Bayesian model, accounting for relatedness between lines in genotype-by-environment interaction, was superior to the standard model.
机译：在自花授粉种群中，个体的特征是高度近交。此外，表型观察受基因型-环境相互作用的影响很大。通常，贝叶斯方法（用于在自花授粉作物中）预测育种价值在统计模型中会忽略基因型-环境之间的相互作用，这可能会导致估计偏差。在我们的研究中，开发了一种贝叶斯吉布斯采样算法，该算法适用于自花授粉作物的高度近交，并解释了基因型与环境之间的相互作用。由于相关谱系应该显示出相似的基因型-环境相互作用效应，因此贝叶斯模型中包含了扩展的遗传关系矩阵。另外，由于混合模型方程中的系数矩阵 C 可以用秩亏来表征，因此使用空空间计算 C 的伪逆，从而可以更快地进行计算。时间。在这项研究中，使用了春季大麦品系的田间数据和通过计算机模拟生成的“虚拟”父母自花授粉作物的数据。为了进行比较，通过常识性方法预测了其他育种值。通常，如果所考虑的性状的遗传力很高，则标准的贝叶斯吉布斯抽样法和一种经常性方法得出的估计值相似。对于低遗传性状，改进的贝叶斯模型优于标准模型，该模型考虑了基因型-环境相互作用中品系之间的相关性。

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