ArticleTheoretical population biology2015
Sensitivity of quantitative traits to mutational effects and number of loci.
Article in Theoretical population biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Evolution of quantitative traits with background selection.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Genetic prediction with ARG-powered linear algebra.Genetics · 2026Article
- Representation in genetic studies affects inference about genetic architecture.bioRxiv : the preprint server for biology · 2026Article
- Alternative Mutational Architectures Producing Identical M-Matrices can Lead to Different Patterns of Evolutionary Divergence.Genome biology and evolution · 2025Article
- Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations.PLoS biology · 2024Article
- Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution.Genome biology and evolution · 2023Article
- Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution.bioRxiv : the preprint server for biology · 2023Article
- Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana.The Plant cell · 2022Article
- Hierarchical Modelling of Haplotype Effects on a Phylogeny.Frontiers in genetics · 2020Article
- Evolutionary perspectives on polygenic selection, missing heritability, and GWAS.Human genetics · 2020Review
- Empirical measures of mutational effects define neutral models of regulatory evolution inProceedings of the National Academy of Sciences of the United States of America · 2019Article
- The Effects of Demography and Genetics on the Neutral Distribution of Quantitative Traits.Genetics · 2019Article
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2 authors.
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Abstract
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.
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