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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., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The Wright-Fisher process with selection is an important tool in population genetics theory. Traditional analysis of this process relies on the diffusion approximation. The diffusion approximation is usually studied in a partial differential equations framework. In this paper, I introduce a path integral formalism to study the Wright-Fisher process with selection and use that formalism to obtain a simple perturbation series to approximate the transition density. The perturbation series can be understood in terms of Feynman diagrams, which have a simple probabilistic interpretation in terms of selective events. The perturbation series proves to be an accurate approximation of the transition density for weak selection and is shown to be arbitrarily accurate for any selection coefficient., (Copyright © 2013 Elsevier Inc. All rights reserved.)

We investigate the properties of a Wright-Fisher diffusion process starting at frequency x at time 0 and conditioned to be at frequency y at time T. Such a process is called a bridge. Bridges arise naturally in the analysis of selection acting on standing variation and in the inference of selection from allele frequency time series. We establish a number of results about the distribution of neutral Wright-Fisher bridges and develop a novel rejection-sampling scheme for bridges under selection that we use to study their behavior., (Copyright © 2013 Elsevier Inc. All rights reserved.)