Multilocus approaches for the measurement of selection on correlated genetic loci

The study of ecological speciation is inherently linked to the study of selection. Methods for estimating phenotypic selection within a generation based on associations between trait values and fitness (e.g. survival) of individuals are established. These methods attempt to disentangle selection acting directly on a trait from indirect selection caused by correlations with other traits via multivariate statistical approaches (i.e. inference of selection gradients). The estimation of selection on genotypic or genomic variation could also benefit from disentangling direct and indirect selection on genetic loci. However, achieving this goal is difficult with genomic data because the number of potentially correlated genetic loci (p) is very large relative to the number of individuals sampled (n). In other words, the number of model parameters exceeds the number of observations (p ≫ n). We present simulations examining the utility of whole-genome regression approaches (i.e. Bayesian sparse l...生态物种形成的研究本质上与自然选择的研究密不可分。基于个体性状值与适合度（fitness，例如存活率）之间的关联，估算单代表型选择的方法已趋于完善。此类方法借助多元统计手段（即选择梯度（selection gradients）的推断），试图将直接作用于某一性状的选择，与由与其他性状的相关性所引发的间接选择区分开来。针对基因型或基因组变异的选择估算，同样可通过厘清遗传位点上的直接与间接选择来提升效能。然而，利用基因组数据实现这一目标却困难重重：相较于抽样个体数（n），潜在关联的遗传位点数量（p）极为庞大，换言之，模型参数的数量远超观测样本量（p ≫ n）。本研究通过模拟实验，探究全基因组回归方法（即贝叶斯稀疏线性……）的应用效用。