Evidence map›Paper›PMID 32219589›Full record

ArticleGenetica2020

Low mutation rates promote the evolution of advantageous traits by preventing interference from deleterious mutations.

Yuta Aoyagi Blue, Satoki Sakai

Abstract read
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In one paragraph

Article in Genetica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yuta Aoyagi BlueGraduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8578, Japan. ao7yuta@gmail.com.
Satoki SakaiGraduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8578, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We studied how mutation rates promote the evolution of advantageous traits in an asexual population. First, to examine the effects of mutation rates on the evolution of an advantageous trait (high competitive ability), we carried out simulation analyses with competition between individuals for survival. Second, to examine the mechanism underlying the promotion of advantageous trait evolution, we calculated the probabilities that new favorable effects of mutations on the phenotype were acquired and that existing favorable effects were maintained. In the simulation analyses, advantageous traits evolved in the population with a low mutation rate; however, when the mutation rate was extremely low, advantageous traits evolved slowly because few beneficial mutations occurred. Then, the numerical calculations showed that the probability of acquiring new favorable effects of mutations on the phenotype and the probability of maintaining existing favorable effects are high if the mutation rate is low. The former occurs because, if the mutation rate is high, multiple mutations may occur in a genome, and even if beneficial mutations occur, their favorable effects may be masked by simultaneously occurring deleterious mutations. However, if the mutation rate is low, it is likely that only one beneficial mutation occurs, and its favorable effect on the phenotype is direct. In conclusion, low mutation rates are advantageous because they promote favorable phenotypic effects of mutations without interference from deleterious mutations; these low rates not only prevent the occurrence of deleterious mutations but also help maintain existing beneficial mutations and promote the evolution of advantageous traits.

Indexed as

Evolution, MolecularModels, GeneticComputer SimulationGenomeMutation RatePhenotypeSelection, GeneticEvolution of advantageous traitsInterference from deleterious mutationsMutation ratesSimulation analyses

Identifiers

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Registered trials

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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.