Factor-Analytic Variance–Covariance Structures for Prediction Into a Target Population of Environments

IF 1.3 3区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Hans-Peter Piepho, Emlyn Williams
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引用次数: 0

Abstract

Finlay–Wilkinson regression is a popular method for modeling genotype–environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance–covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

Abstract Image

用于预测目标环境人群的因子分析方差-协方差结构。
芬莱-威尔金森回归法是植物育种和作物品种测试中模拟基因型-环境交互作用的常用方法。当环境是一个随机因素时,该模型可被视为一个因素分析方差-协方差结构,意味着对随机潜在环境变量的回归。本文回顾了此类模型,重点介绍了它们在多环境试验分析中的应用,目的是对目标环境群体进行预测。我们从基本的方差分析模型入手,研究了随机效应假设与固定效应假设的影响,然后转向因子分析模型,并考虑向涉及可观测环境协变量的模型过渡,这些模型有望提供比潜在环境变量模型更准确、更有针对性的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrical Journal
Biometrical Journal 生物-数学与计算生物学
CiteScore
3.20
自引率
5.90%
发文量
119
审稿时长
6-12 weeks
期刊介绍: Biometrical Journal publishes papers on statistical methods and their applications in life sciences including medicine, environmental sciences and agriculture. Methodological developments should be motivated by an interesting and relevant problem from these areas. Ideally the manuscript should include a description of the problem and a section detailing the application of the new methodology to the problem. Case studies, review articles and letters to the editors are also welcome. Papers containing only extensive mathematical theory are not suitable for publication in Biometrical Journal.
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