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（phenotypic trait）并非总能独立于彼此响应选择，且常表现出对选择的相关响应。基因型-表型映射（genotype-phenotype map，GP映射）的结构决定了性状的协变模式，该模式涉及基础基因多效性（pleiotropy）效应的程度与强度的变异。目前尚不清楚且存在诸多争议的是，从由遗传（共）方差矩阵（genetic (co)variance matrix，G矩阵）所推断出的数量性状的变异属性中，能够在多大程度上推导出该协变结构。本研究旨在阐明，多效性程度以及突变的多效性效应间的相关性，会以何种差异化方式影响G矩阵的结构，以及我们通过其特征分解检测遗传约束的能力。本研究表明，当G矩阵的特征向量对应于具有突变效应相关性的基础多效性模块（pleiotropic modules）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——这是因为无相关性的多效性效应并不会影响性状间的遗传相关。相较于存在基础模块化多效性的情形，在缺乏基础模块化多效性时，相关选择所诱导的性状遗传相关的模块化划分强度要弱得多。