Kelly Alison M, Cullis Brian R, Gilmour Arthur R, Eccleston John A, Thompson Robin
Queensland DPI&F, Biometry, Toowoomba, Queensland, Australia.
Genet Sel Evol. 2009 Apr 9;41(1):33. doi: 10.1186/1297-9686-41-33.
Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.
最近文献中提出了用于植物育种计划中在重复多环境试验（MET）中测试的群体的划分加性和非加性遗传效应的遗传模型。对于这些数据，方差模型涉及一个大的分子亲缘关系矩阵A的直接乘积，以及基因型与环境互作效应的复杂结构，通常为因子分析（FA）形式。对于MET数据，我们预计不同环境间基因型排名具有高度相关性，从而导致非正定协方差矩阵。对于具有独立基因型的FA公式，已经推导了降秩模型的估计方法，并且我们将这些估计方法应用于涉及分子亲缘关系矩阵的更复杂情况。我们研究了具有嵌入系谱结构的MET数据的不同遗传模型的性能，并考虑了非加性方差的大小。现有软件包拟合这些复杂模型的能力很大程度上归功于稀疏矩阵方法和平均信息算法的使用。在此，我们提出了对标准公式的扩展，这是在多个环境中使用因子分析结构进行估计所必需的。
