Data from: Genetic variation, simplicity and evolutionary constraints for function-valued traits

Understanding the patterns of genetic variation and constraint for continuous reaction norms, growth trajectories, and other function-valued traits is challenging. We describe and illustrate a recent analytical method, simple basis analysis (SBA), that uses the genetic variance-covariance (G) matrix to identify “simple” directions of genetic variation and genetic constraints that have straightforward biological interpretations. We discuss the parallels between the eigenvectors (principal components) identified by principal components analysis (PCA) and the simple basis (SB) vectors identified by SBA. We apply these methods to estimated G matrices obtained from 10 studies of thermal performance curves and growth curves. Our results suggest that variation in overall size across all ages represented most of the genetic variance in growth curves. In contrast, variation in overall performance across all temperatures represented less than one-third of the genetic variance in thermal performance curves in all cases, and genetic trade-offs between performance at higher versus lower temperatures were often important. The analyses also identify potential genetic constraints on patterns of early and later growth in growth curves. We suggest that SBA can be a useful complement or alternative to PCA for identifying biologically interpretable directions of genetic variation and constraint in function-valued traits.

解析连续反应规范（reaction norm）、生长轨迹及其他函数值性状（function-valued trait）的遗传变异与约束模式，向来是颇具挑战性的研究课题。本文介绍并展示了一种新近提出的分析方法——简单基分析（simple basis analysis, SBA），该方法借助遗传方差-协方差矩阵（genetic variance-covariance matrix, G），可识别具备明确生物学解释性的遗传变异与遗传约束的“简单”方向。本文还探讨了主成分分析（principal components analysis, PCA）所识别的特征向量（eigenvector，即主成分principal components）与SBA所识别的简单基（SB）向量之间的相似性。我们将上述方法应用于从10项关于热性能曲线（thermal performance curve）与生长曲线的研究中得到的估计G矩阵。研究结果显示，生长曲线中各年龄段整体体型的变异占遗传方差的绝大部分。与之形成对比的是，在所有热性能曲线分析中，各温度下整体性能的变异仅占遗传方差的不到三分之一，且高温与低温下性能之间的遗传权衡（genetic trade-off）往往具有重要意义。本次分析还识别出了生长曲线中早期与后期生长模式所受的潜在遗传约束。本文认为，在函数值性状中识别具备生物学解释性的遗传变异与约束方向时，SBA可作为PCA的有效补充或替代方法。