Data from: There is more than one way to skin a G matrix

Because of its importance in directing evolutionary trajectories there has been considerable interest in comparing variation among genetic variance-covariance ( G) matrices. Numerous statistical approaches have been suggested but no general analysis of the relationship among these methods has previously been published. In this paper we used data from a half-sib experiment and simulations to explore the results of applying eight tests (T method, modified Mantel test, Bartlett’s test, Flury hierarchy, Jackknife-MANOVA, Jackknife-eigenvalue test, random skewers, selection skewers). Whereas a randomization approach produced acceptable estimates those from a bootstrap were typically unacceptable and we recommend randomization as the preferred method. All methods except the Jackknife-eigenvalue test gave similar results although a fine scale analysis suggested that the former group can be subdivided into two or possibly three groups, hierarchical tests, skewers, and the rest (Jackknife-MANOVA, Modified Mantel, T method, probably Bartlett’s). An advantage of the jackknife methods is that they permit tests of association with other factors, such as in this case, temperature and sex. We recommend applying all the tests described in this paper, with the exception of the T method, and provide R functions for this purpose.

鉴于其在调控演化轨迹方面的关键作用，学界长期以来对比较遗传方差-协方差（G）矩阵（genetic variance-covariance (G) matrices）间的变异充满了浓厚的研究兴趣。尽管已有诸多统计方法被提出，但此前尚未有针对这些方法间关联的系统性分析公开发表。本文中，我们利用半同胞实验（half-sib experiment）数据与模拟数据，探究了应用八种检验方法——T方法（T method）、修正Mantel检验（modified Mantel test）、巴特利特检验（Bartlett’s test）、弗吕里层级检验（Flury hierarchy）、刀切法多元方差分析（Jackknife-MANOVA）、刀切法特征值检验（Jackknife-eigenvalue test）、随机穿刺法（random skewers）与选择穿刺法（selection skewers）——的应用结果。研究结果显示，随机化方法（randomization approach）可生成可接受的估计结果，而自助法（bootstrap）得到的估计结果通常不尽如人意，因此我们推荐将随机化方法作为首选方法。除刀切法特征值检验之外的所有方法均得到了相似的结果，不过精细尺度分析表明，该组方法可进一步划分为两类或三类：层级检验类、穿刺法类，以及其余方法（刀切法多元方差分析、修正Mantel检验、T方法，大概率还包括巴特利特检验）。刀切法类方法的一大优势在于，其允许开展与其他因素的关联分析，例如本研究中的温度与性别因素。我们推荐应用本文所述的全部检验方法（T方法除外），并为此提供了对应的R语言函数。