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环境间异方差性和遗传自相关性:对表型可塑性研究的启示。

Among-environment heteroscedasticity and genetic autocorrelation: implications for the study of phenotypic plasticity.

作者信息

Dutilleul P, Potvin C

机构信息

Department of Plant Science, McGill University, Ste-Anne-de-Bellevue, Québec, Canada.

出版信息

Genetics. 1995 Apr;139(4):1815-29. doi: 10.1093/genetics/139.4.1815.

Abstract

The impact of among-environment heteroscedasticity and genetic autocorrelation on the analysis of phenotypic plasticity is examined. Among-environment heteroscedasticity occurs when genotypic variances differ among environments. Genetic autocorrelation arises whenever the responses of a genotype to different environments are more or less similar than expected for observations randomly associated. In a multivariate analysis-of-variance model, three transformations of genotypic profiles (reaction norms), which apply to the residuals of the model while preserving the mean responses within environments, are derived. The transformations remove either among-environment heteroscedasticity, genetic autocorrelation or both. When both nuisances are not removed, statistical tests are corrected in a modified univariate approach using the sample covariance matrix of the genotypic profiles. Methods are illustrated on a Chlamydomonas reinhardtii data set. When heteroscedasticity was removed, the variance component associated with the genotype-by-environment interaction increased proportionally to the genotype variance component. As a result, the genetic correlation rg was altered. Genetic autocorrelation was responsible for statistical significance of the genotype-by-environment interaction and genotype main effects on raw data. When autocorrelation was removed, the ranking of genotypes according to their stability index dramatically changed. Evolutionary implications of our methods and results are discussed.

摘要

研究了环境间异方差性和遗传自相关性对表型可塑性分析的影响。当基因型方差在不同环境中存在差异时,就会出现环境间异方差性。只要一个基因型对不同环境的反应比随机关联观测值预期的更相似或更不相似,就会产生遗传自相关性。在一个多变量方差分析模型中,推导了基因型概况(反应规范)的三种变换,这些变换应用于模型的残差,同时保留环境内的平均反应。这些变换可以消除环境间异方差性、遗传自相关性或两者。当两种干扰都未消除时,使用基因型概况的样本协方差矩阵,通过一种改进的单变量方法对统计检验进行校正。在莱茵衣藻数据集上对方法进行了说明。当消除异方差性时,与基因型与环境互作相关的方差分量与基因型方差分量成比例增加。结果,遗传相关性rg发生了改变。遗传自相关性导致了基因型与环境互作以及基因型对原始数据的主效应的统计显著性。当消除自相关性时,根据稳定性指数对基因型的排名发生了显著变化。讨论了我们的方法和结果的进化意义。

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