ArticleGenetics2014
A bayesian MCMC approach to assess the complete distribution of fitness effects of new mutations: uncovering the potential for adaptive walks in challenging environments.
Article in Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed, 135 citations in OpenAlex.
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- Estimating the Distribution of Fitness Effects in Aye-Ayes (Daubentonia madagascariensis), Accounting for Population History as Well as Mutation and Recombination Rate Heterogeneity.American journal of primatology · 2025Article
- Epitope mapping via in vitro deep mutational scanning methods and its applications.The Journal of biological chemistry · 2025Review
- Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage.Genome biology · 2024Article
- Article
- The distribution of fitness effects during adaptive walks using a simple genetic network.PLoS genetics · 2024Article
- The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects.Genome biology and evolution · 2024Article
- ACIDES: on-line monitoring of forward genetic screens for protein engineering.Nature communications · 2023Article
- Unpredictable repeatability in molecular evolution.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Co-evolution of interacting proteins through non-contacting and non-specific mutations.Nature ecology & evolution · 2022Article
- Recommendations for improving statistical inference in population genomics.PLoS biology · 2022Article
- Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.PLoS biology · 2022Article
- Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies.Heredity · 2022Article
- Revisiting the notion of deleterious sweeps.Genetics · 2021Article
- Quantifying the Evolutionary Constraints and Potential of Hepatitis C Virus NS5A Protein.mSystems · 2021Article
- The Adaptive Potential of the Middle Domain of Yeast Hsp90.Molecular biology and evolution · 2021Article
- Imposed mutational meltdown as an antiviral strategy.Evolution; international journal of organic evolution · 2020Review
- Inferring Parameters of the Distribution of Fitness Effects of New Mutations When Beneficial Mutations Are Strongly Advantageous and Rare.G3 (Bethesda, Md.) · 2020Article
- Evaluating the contributions of purifying selection and progeny-skew in dictating within-host Mycobacterium tuberculosis evolution.Evolution; international journal of organic evolution · 2020Article
- Toward an Evolutionarily Appropriate Null Model: Jointly Inferring Demography and Purifying Selection.Genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)--that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type--showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher's geometric model.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.