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评估基因型与环境(G×E)相互作用及疾病严重程度数据的非参数方法的考量

Consideration of Nonparametric Approaches for Assessing Genotype-by-Environment (G × E) Interaction with Disease Severity Data.

作者信息

Madden L V, Paul P A, Lipps P E

机构信息

Department of Plant Pathology, The Ohio State University, Ohio Agricultural and Research Development Center (OARDC), Wooster 44691.

出版信息

Plant Dis. 2007 Jul;91(7):891-900. doi: 10.1094/PDIS-91-7-0891.

Abstract

Determination of host genotype-by-environment (G × E) interaction is needed to assess the stability of cultivar traits such as plant disease resistance and to reveal differences in aggressiveness or virulence of pathogen strains among locations. Here we explored the use of rank-based methodology to quantify the concordance (or discordance) of disease responses of host genotypes across environments, based on the Kendall coefficient of concordance (W) and ancillary test statistics, in order to determine the extent to which environment affected rankings of genotypes. An analysis of four data sets for disease severity of gray leaf spot of maize (with genotypes planted in as many as 11 locations in a given year) revealed highly significant concordance (P ≤ 0.001) overall, indicating that genotypes varied little in within-environment rankings. This suggests that the G × E interaction was small or nonexistent (in terms of rankings). A novel rank-based method by Piepho was evaluated to further elucidate the interaction (if any) through a test for variance homogeneity. The Piepho test statistic was not significant (P > 0.25) for any of the gray leaf spot data sets, confirming the stability of genotypes across environments for this pathosystem; however, analysis of published data sets for other pathosystems indicated significant results. The relationship shown by Hühn, Lotito, and Piepho between the ratio of genotype and residual variances of the original data and the rank-based W statistic was evaluated using Monte Carlo simulations. A more general functional relationship was developed that is applicable over a wide range of number of genotypes and environments in the analyzed studies. This confirms previously shown linkages between rankings of genotypes within environments and variability in the original (unranked) data, thus permitting ease of interpretation of parametric and nonparametric results.

摘要

为了评估诸如植物抗病性等品种性状的稳定性,并揭示不同地点病原体菌株在侵袭性或毒力方面的差异,需要确定宿主基因型与环境(G×E)的相互作用。在此,我们探索使用基于秩的方法,基于肯德尔和谐系数(W)和辅助检验统计量,来量化宿主基因型在不同环境下疾病反应的一致性(或不一致性),以确定环境对基因型排名的影响程度。对四个玉米灰斑病严重程度数据集(给定年份中基因型种植于多达11个地点)的分析显示,总体上具有高度显著的一致性(P≤0.001),表明基因型在环境内排名变化很小。这表明G×E相互作用很小或不存在(就排名而言)。评估了Piepho提出的一种基于秩的新方法,通过方差齐性检验进一步阐明相互作用(如果有的话)。对于任何一个灰斑病数据集,Piepho检验统计量均不显著(P>0.25),证实了该病理系统中基因型在不同环境下的稳定性;然而,对其他病理系统已发表数据集的分析显示有显著结果。使用蒙特卡罗模拟评估了Hühn、Lotito和Piepho所展示的原始数据基因型方差与残差方差之比与基于秩的W统计量之间的关系。建立了一个更通用的函数关系,适用于分析研究中广泛的基因型和环境数量范围。这证实了先前所示的环境内基因型排名与原始(未排名)数据变异性之间的联系,从而便于解释参数和非参数结果。

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