SynthesisScientific reports2024
Loss-of-function mutations are main drivers of adaptations during short-term evolution.
Synthesis in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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
5 citing papers in PubMed.
- Functional insights into dispensable genes using genome-wide loss-of-function burden tests in Arabidopsis.The Plant cell · 2026Article
- Using TraDIS as a complementary approach to long term evolution for mapping adaptive mutations in Escherichia coli.PLoS genetics · 2026Article
- Historical contingency limits adaptive diversification in a spatially structured environment.Evolution letters · 2026Article
- Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.Communications biology · 2025Article
- A Short-Term View of Protein Sequence Evolution from Salmonella.Genome biology and evolution · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We noticed that during short-term experimental evolution and carcinogenesis, mutations causing gene inactivation (i.e., nonsense mutations or frameshifts) are frequent. Our meta-analysis of 65 experiments using modified dN/dS statistics indicated that nonsense mutations are adaptive in different experimental conditions and we empirically confirmed this prediction. Using yeast S. cerevisiae as a model we show that fixed or highly frequent gene loss-of-function mutations are almost exclusively adaptive in the majority of experiments.
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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.