Evidence map›Paper›PMID 17339206›Full record

ArticleGenetics2007

Experimental estimate of the abundance and effects of nearly neutral mutations in the RNA virus phi 6.

Christina L Burch, Sebastien Guyader, Daniel Samarov, Haipeng Shen

Open access · bronzeAbstract read
In one paragraph

Article in Genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.2field-weighted citation impact, top 11% of its field
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

14 citing papers in PubMed, 36 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Christina L BurchDepartment of Biology, University of North Carolina, Chapel Hill, North Carolina 27516, USA. cburch@bio.unc.edu
Sebastien Guyader
Daniel Samarov
Haipeng Shen
Flagstaff Medical Center · USUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the frequency and effects of neutral and nearly neutral mutations are critical to evolutionary patterns and processes governed by genetic drift, the small effects of such mutations make them difficult to study empirically. Here we present the results of a mutation-accumulation experiment designed to assess the frequencies of deleterious mutations with undetectable effects. We promoted the accumulation of spontaneous mutations by subjecting independent lineages of the RNA virus 6 to repeated population bottlenecks of a single individual. We measured fitness following every bottleneck to obtain a complete picture of the timing and effects of the accumulated mutations with detectable effects and sequenced complete genomes to determine the number of mutations that were undetected by the fitness assays. To estimate the effects of the undetected mutations, we implemented a likelihood model developed for quantitative trait locus (QTL) data (Otto and Jones 2000) to estimate the number and effects of the undetected mutations from the measured number and effects of the detected mutations. Using this method we estimated a deleterious mutation rate of U = 0.03 and a gamma effects distribution with mean s=0.093 and coefficient of variation = 0.204. Although our estimates of U and s fall within the range of recent mutation rate and effect estimates in eukaryotes, the fraction of mutations with detectable effects on laboratory fitness (39%) appears to be far higher in 6 than in eukaryotes.

Indexed as

Genome, ViralMutationRegression AnalysisRNA VirusesSelection, GeneticSequence Analysis, DNA

Identifiers

PMID17339206
PMCPMC1893061
OpenAlexW2123563299

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.