Cullis Brian R, Jefferson Paul, Thompson Robin, Smith Alison B
National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia.
Theor Appl Genet. 2014 Oct;127(10):2193-210. doi: 10.1007/s00122-014-2373-0. Epub 2014 Aug 22.
Modelling additive genotype-by-environment interaction is best achieved with the use of factor analytic models. With numerous environments and for outcrossing plant species, computation is facilitated using reduced animal models. The development of efficient plant breeding strategies requires a knowledge of the magnitude and structure of genotype-by-environment interaction. This information can be obtained from appropriate linear mixed model analyses of phenotypic data from multi-environment trials. The use of factor analytic models for genotype-by-environment effects is known to provide a reliable, parsimonious and holistic approach for obtaining estimates of genetic correlations between all pairs of trials. When breeding for outcrossing species the focus is on estimating additive genetic correlations and effects which is achieved by including pedigree information in the analysis. The use of factor analytic models in this setting may be computationally prohibitive when the number of environments is moderate to large. In this paper, we present an approach that uses an approximate reduced animal model to overcome the computational issues associated with factor analytic models for additive genotype-by-environment effects. The approach is illustrated using a Pinus radiata breeding dataset involving 77 trials, located in environments across New Zealand and south eastern Australia, and with pedigree information on 315,581 trees. Using this approach we demonstrate the existence of substantial additive genotype-by-environment interaction for the trait of stem diameter measured at breast height. This finding has potentially significant implications for both breeding and deployment strategies. Although our approach has been developed for forest tree breeding programmes, it is directly applicable for other outcrossing plant species, including sugarcane, maize and numerous horticultural crops.
通过使用因子分析模型能够最好地对加性基因型与环境互作进行建模。对于众多环境以及异交植物物种而言，使用简化动物模型有助于计算。高效植物育种策略的制定需要了解基因型与环境互作的程度和结构。这些信息可从多环境试验的表型数据的适当线性混合模型分析中获得。已知使用因子分析模型来分析基因型与环境效应，能为获得所有试验对之间的遗传相关性估计提供一种可靠、简约且全面的方法。在对异交物种进行育种时，重点在于估计加性遗传相关性和效应，这可通过在分析中纳入系谱信息来实现。当环境数量为中等至大量时，在这种情况下使用因子分析模型可能在计算上令人望而却步。在本文中，我们提出了一种方法，该方法使用近似简化动物模型来克服与用于加性基因型与环境效应的因子分析模型相关的计算问题。使用一个辐射松育种数据集对该方法进行了说明，该数据集涉及77个试验，分布在新西兰和澳大利亚东南部的各个环境中，并且包含315,581棵树的系谱信息。使用这种方法，我们证明了对于胸径性状存在显著的加性基因型与环境互作。这一发现对育种和部署策略都可能具有重大意义。尽管我们的方法是针对林木育种计划开发的，但它可直接应用于其他异交植物物种，包括甘蔗、玉米和许多园艺作物。
