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A note on measuring natural selection on principal component scores

机译：关于测量主成分得分自然选择的注意事项

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摘要

Measuring natural selection through the use of multiple regression has transformed our understanding of selection, although the methods used remain sensitive to the effects of multicollinearity due to highly correlated traits. While measuring selection on principal component (PC) scores is an apparent solution to this challenge, this approach has been heavily criticized due to difficulties in interpretation and relating PC axes back to the original traits. We describe and illustrate how to transform selection gradients for PC scores back into selection gradients for the original traits, addressing issues of multicollinearity and biological interpretation. In addition to reducing multicollinearity, we suggest that this method may have promise for measuring selection on high‐dimensional data such as volatiles or gene expression traits. We demonstrate this approach with empirical data and examples from the literature, highlighting how selection estimates for PC scores can be interpreted while reducing the consequences of multicollinearity.
机译：通过使用多元回归来衡量自然选择，已经改变了我们对选择的理解，尽管由于高度相关的性状，所使用的方法对多重共线性的影响仍然很敏感。尽管测量主成分（PC）分数的选择显然是应对这一挑战的方法，但由于难以解释以及将PC轴与原始特征相关联，这种方法已受到广泛批评。我们描述和说明了如何将PC分数的选择梯度转换回原始特征的选择梯度，解决了多重共线性和生物学解释的问题。除了减少多重共线性之外，我们还建议该方法可能对测量高维度数据（如挥发物或基因表达特征）的选择有希望。我们用文献中的经验数据和示例来证明这种方法，重点介绍如何在减少多重共线性的结果的同时解释PC分数的选择估计。

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