ArticleGenetics2013
A comparison of models to infer the distribution of fitness effects of new mutations.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
82 citing papers in PubMed.
- Gene-level complexity explains genome-wide variation in the distribution of fitness effects.Molecular biology and evolution · 2026Article
- High Rate of Mutation and Efficient Removal by Selection of Structural Variants From Natural Populations of Caenorhabditis Elegans.Genome biology and evolution · 2026Article
- Analysis of the impact of gene evolution on reproductive effects reveals prevalent sexual and germline-soma conflicts.Nature ecology & evolution · 2026Article
- The distribution of fitness effects of new mutations in regulatory regions of thebioRxiv : the preprint server for biology · 2026Article
- Estimating the distribution of fitness effects of loss of heterozygosity (LOH) events using an engineered library of Saccharomyces cerevisiae.PLoS genetics · 2026Article
- Scaling the fitness effects of mutations with respect to differentially adapted Arabidopsis thaliana accessions under natural conditions.Evolution; international journal of organic evolution · 2025Article
- A model of Hill-Robertson interference caused by purifying selection in a nonrecombining genome.Genetics · 2025Article
- A Guide for Developing Demo-Genetic Models to Simulate Genetic Rescue.Evolutionary applications · 2025Article
- Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data.G3 (Bethesda, Md.) · 2025Article
- Demographic History, Genetic Load, and the Efficacy of Selection in the Globally Invasive Mosquito Aedes aegypti.Genome biology and evolution · 2025Article
- Isolating selective from non-selective forces using site frequency ratios.PLoS genetics · 2025Article
- Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species.Evolution; international journal of organic evolution · 2025Article
- Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life.Nature communications · 2024Article
- Inferring demographic and selective histories from population genomic data using a two-step approach in species with coding-sparse genomes: an application to human data.bioRxiv : the preprint server for biology · 2024Article
- Evolutionary consequences of domestication on the selective effects of new amino acid changing mutations in canids.bioRxiv : the preprint server for biology · 2024Article
- Article
- Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species.bioRxiv : the preprint server for biology · 2024Article
- The distribution of fitness effects during adaptive walks using a simple genetic network.PLoS genetics · 2024Article
- The fitness consequences of genetic divergence between polymorphic gene arrangements.Genetics · 2024Article
- Between but Not Within-Species Variation in the Distribution of Fitness Effects.Molecular biology and evolution · 2023Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Knowing the distribution of fitness effects (DFE) of new mutations is important for several topics in evolutionary genetics. Existing computational methods with which to infer the DFE based on DNA polymorphism data have frequently assumed that the DFE can be approximated by a unimodal distribution, such as a lognormal or a gamma distribution. However, if the true DFE departs substantially from the assumed distribution (e.g., if the DFE is multimodal), this could lead to misleading inferences about its properties. We conducted simulations to test the performance of parametric and nonparametric discretized distribution models to infer the properties of the DFE for cases in which the true DFE is unimodal, bimodal, or multimodal. We found that lognormal and gamma distribution models can perform poorly in recovering the properties of the distribution if the true DFE is bimodal or multimodal, whereas discretized distribution models perform better. If there is a sufficient amount of data, the discretized models can detect a multimodal DFE and can accurately infer the mean effect and the average fixation probability of a new deleterious mutation. We fitted several models for the DFE of amino acid-changing mutations using whole-genome polymorphism data from Drosophila melanogaster and the house mouse subspecies Mus musculus castaneus. A lognormal DFE best explains the data for D. melanogaster, whereas we find evidence for a bimodal DFE in M. m. castaneus.
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