Blows M W
School of Integrative Biology, University of Queensland, Brisbane, Australia.
J Evol Biol. 2007 Jan;20(1):1-8. doi: 10.1111/j.1420-9101.2006.01164.x.
Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (gamma) which describes the individual fitness surface. The second is the genetic variance-covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element-by-element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of gamma and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.
我们对微观进化变化的理解基于两个对称矩阵。第一个是描述个体适应度曲面的非线性选择梯度矩阵（γ）。第二个是影响对选择的多变量响应的遗传方差协方差矩阵（G）。对这些矩阵进行实证分析的一种常见方法是逐个元素进行显著性检验，然后基于这些单变量和双变量参数对模式进行生物学解释。在这里，我展示了为什么这种方法在许多情况下可能会错误地呈现数量性状的遗传基础以及作用于它们的选择。方阵的对角化是生物学家使用的许多多变量统计技术的一个基本方面。将此方法及其他相关方法应用于γ和G矩阵结构的分析，能更深入地了解非线性选择的形式和强度，以及多个性状的遗传方差可用性。
