Schraiber Joshua G, Landis Michael J
Department of Genome Sciences, University of Washington, Seattle, WA 98195, United States.
Department of Integrative Biology, University of California, Berkeley, CA 94720, United States.
Theor Popul Biol. 2015 Jun;102:85-93. doi: 10.1016/j.tpb.2015.03.005. Epub 2015 Mar 31.
When models of quantitative genetic variation are built from population genetic first principles, several assumptions are often made. One of the most important assumptions is that traits are controlled by many genes of small effect. This leads to a prediction of a Gaussian trait distribution in the population, via the Central Limit Theorem. Since these biological assumptions are often unknown or untrue, we characterized how finite numbers of loci or large mutational effects can impact the sampling distribution of a quantitative trait. To do so, we developed a neutral coalescent-based framework, allowing us to gain a detailed understanding of how number of loci and the underlying mutational model impacts the distribution of a quantitative trait. Through both analytical theory and simulation we found the normality assumption was highly sensitive to the details of the mutational process, with the greatest discrepancies arising when the number of loci was small or the mutational kernel was heavy-tailed. In particular, skewed mutational effects will produce skewed trait distributions and fat-tailed mutational kernels result in multimodal sampling distributions, even for traits controlled by a large number of loci. Since selection models and robust neutral models may produce qualitatively similar sampling distributions, we advise extra caution should be taken when interpreting model-based results for poorly understood systems of quantitative traits.
当基于群体遗传学的第一原理构建数量遗传变异模型时，通常会做出几个假设。其中最重要的假设之一是性状由许多效应较小的基因控制。通过中心极限定理，这导致了群体中高斯性状分布的预测。由于这些生物学假设往往未知或不真实，我们描述了有限数量的基因座或大的突变效应如何影响数量性状的抽样分布。为此，我们开发了一个基于中性合并的框架，使我们能够详细了解基因座数量和潜在的突变模型如何影响数量性状的分布。通过分析理论和模拟，我们发现正态性假设对突变过程的细节高度敏感，当基因座数量较少或突变核为重尾时，差异最大。特别是，即使对于由大量基因座控制的性状，偏态的突变效应也会产生偏态的性状分布，重尾的突变核会导致多峰抽样分布。由于选择模型和稳健的中性模型可能产生定性相似的抽样分布，我们建议在解释基于模型的结果时，对于了解甚少的数量性状系统应格外谨慎。
