Factor-analytic models for genotype x environment type problems and structured covariance matrices.
Factor-analytic models for genotype x environment type problems and structured covariance matrices.
复制标题
DOI：
10.1186/1297-9686-41-21
复制
发表时间：
2009-01-30
期刊：
影响因子：
--
通讯作者：
Meyer K
中科院分区：
文献类型：
--
作者：
Meyer K
分析不同环境下不同表达基因型的数据是数量遗传学中的经典问题。综述了基因型×环境交互作用数据模型及相关问题，将早期基于方差分析的公式与现代混合模型相联系。结果表明，为植物育种中多环境试验的分析而开发的模型可直接应用于动物育种。特别是，“加性主效应，乘性相互作用”模型适应方差的异质性，其特征在于因子分析协方差结构。虽然这可以在混合模型中通过在标准的多性状模型中的遗传协方差矩阵上施加这样的结构来实现，但是通过分别拟合遗传的共同和特定因素来获得等效模型。属性的混合模型方程的因子分析模型的替代实现进行了讨论，并扩展到结构化建模的协方差矩阵多性状，多环境的情况下进行了说明。因子分析模型为基因型×环境交互作用类型问题的建模提供了一个自然的框架。由于对可用协方差结构的简洁描述、对因子的直接解释范围以及计算优势，拟合此类模型的混合模型分析可能会得到越来越多的使用。
Analysis of data on genotypes with different expression in different environments is a classic problem in quantitative genetics. A review of models for data with genotype × environment interactions and related problems is given, linking early, analysis of variance based formulations to their modern, mixed model counterparts. It is shown that models developed for the analysis of multi-environment trials in plant breeding are directly applicable in animal breeding. In particular, the 'additive main effect, multiplicative interaction' models accommodate heterogeneity of variance and are characterised by a factor-analytic covariance structure. While this can be implemented in mixed models by imposing such structure on the genetic covariance matrix in a standard, multi-trait model, an equivalent model is obtained by fitting the common and specific factors genetic separately. Properties of the mixed model equations for alternative implementations of factor-analytic models are discussed, and extensions to structured modelling of covariance matrices for multi-trait, multi-environment scenarios are described. Factor analytic models provide a natural framework for modelling genotype × environment interaction type problems. Mixed model analyses fitting such models are likely to see increasing use due to the parsimonious description of covariance structures available, the scope for direct interpretation of factors as well as computational advantages.
登录
查看更多内容
影响因子：
3
作者：
GOLLOB, HF
通讯作者：
GOLLOB, HF
影响因子：
3.7
作者：
FLURY, BN
通讯作者：
FLURY, BN
DOI：
10.1016/0301-6226(93)90095-y
发表时间：
1993-06-01
期刊：
LIVESTOCK PRODUCTION SCIENCE
影响因子：
--
作者：
CAMERON, ND
通讯作者：
CAMERON, ND
影响因子：
4.1
作者：
de Los Campos, Gustavo;Gianola, Daniel
通讯作者：
Gianola, Daniel
影响因子：
3.3
作者：
Hine, E;Blows, MW
通讯作者：
Blows, MW