Data from: What affects the predictability of evolutionary constraints using a G-matrix? the relative effects of modular pleiotropy and mutational correlation.

Phenotypic traits do not always respond to selection independently from each other and often show correlated responses to selection. The structure of a genotype-phenotype map (GP map) determines trait covariation, which involves variation in the degree and strength of the pleiotropic effects of the underlying genes. It is still unclear, and debated, how much of that structure can be deduced from variational properties of quantitative traits that are inferred from their genetic (co)variance matrix (G-matrix). Here we aim to clarify how the extent of pleiotropy and the correlation among the pleiotropic effects of mutations differentially affect the structure of a G-matrix and our ability to detect genetic constraints from its eigen decomposition. We show that the eigenvectors of a G-matrix can be predictive of evolutionary constraints when they map to underlying pleiotropic modules with correlated mutational effects. Without mutational correlation, evolutionary constraints caused by the fitness costs associated with increased pleiotropy are harder to infer from evolutionary metrics based on a G-matrix's geometric properties because uncorrelated pleiotropic effects do not affect traits' genetic correlations. Correlational selection induces much weaker modular partitioning of traits' genetic correlations in absence then in presence of underlying modular pleiotropy.表型性状并非总能彼此独立地响应选择，且往往会表现出相关的选择响应。基因型-表型映射（genotype-phenotype map，GP映射）的结构决定了性状协变模式，而这一过程涉及到基础基因多效性效应的程度与强度的变异。目前，我们仍不清楚且存在诸多争论：从由遗传（共）方差矩阵（genetic (co)variance matrix，G矩阵）推导得到的数量性状变异属性中，能够在多大程度上反推上述GP映射的结构。本研究旨在阐明：多效性程度以及突变多效性效应间的相关性，会如何差异化地影响G矩阵的结构，以及我们通过其特征分解来检测遗传约束的能力。我们的研究表明，当G矩阵的特征向量对应到携带相关突变效应的基础多效性模块时，该特征向量可用于预测进化约束。若不存在突变相关性，由多效性增强所带来的适合度代价所引发的进化约束，将更难通过基于G矩阵几何属性的进化指标进行推断——这是因为无关联的多效性效应不会改变性状间的遗传相关关系。相较于存在基础模块化多效性的情况，当不存在此类模块化多效性时，相关选择所诱导的性状遗传相关模块化划分程度要弱得多。