↓ Skip to main content

Epistasis between Beneficial Mutations and the Phenotype-to-Fitness Map for a ssDNA Virus

Overview of attention for article published in PLoS Genetics, June 2011
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

patent
1 patent
f1000
1 research highlight platform

Readers on

mendeley
108 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Epistasis between Beneficial Mutations and the Phenotype-to-Fitness Map for a ssDNA Virus
Published in
PLoS Genetics, June 2011
DOI 10.1371/journal.pgen.1002075
Pubmed ID
Authors
Abstract

Epistatic interactions between genes and individual mutations are major determinants of the evolutionary properties of genetic systems and have therefore been well documented, but few quantitative data exist on epistatic interactions between beneficial mutations, presumably because such mutations are so much rarer than deleterious ones. We explored epistasis for beneficial mutations by constructing genotypes with pairs of mutations that had been previously identified as beneficial to the ssDNA bacteriophage ID11 and by measuring the effects of these mutations alone and in combination. We constructed 18 of the 36 possible double mutants for the nine available beneficial mutations. We found that epistatic interactions between beneficial mutations were all antagonistic-the effects of the double mutations were less than the sums of the effects of their component single mutations. We found a number of cases of decompensatory interactions, an extreme form of antagonistic epistasis in which the second mutation is actually deleterious in the presence of the first. In the vast majority of cases, recombination uniting two beneficial mutations into the same genome would not be favored by selection, as the recombinant could not outcompete its constituent single mutations. In an attempt to understand these results, we developed a simple model in which the phenotypic effects of mutations are completely additive and epistatic interactions arise as a result of the form of the phenotype-to-fitness mapping. We found that a model with an intermediate phenotypic optimum and additive phenotypic effects provided a good explanation for our data and the observed patterns of epistatic interactions.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 3 3%
Belgium 2 2%
United Kingdom 1 <1%
Spain 1 <1%
Canada 1 <1%
Australia 1 <1%
Unknown 99 92%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 40 37%
Researcher 25 23%
Student > Master 10 9%
Professor 5 5%
Lecturer 4 4%
Other 9 8%
Unknown 15 14%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 59 55%
Biochemistry, Genetics and Molecular Biology 16 15%
Immunology and Microbiology 2 2%
Physics and Astronomy 2 2%
Medicine and Dentistry 2 2%
Other 9 8%
Unknown 18 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 May 2018.
All research outputs
#9,642,007
of 31,634,113 outputs
Outputs from PLoS Genetics
#5,063
of 9,542 outputs
Outputs of similar age
#51,419
of 148,508 outputs
Outputs of similar age from PLoS Genetics
#50
of 110 outputs
Altmetric has tracked 31,634,113 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 9,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.9. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 148,508 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.