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Unlocking the Bottleneck in Forward Genetics Using Whole-Genome Sequencing and Identity by Descent to Isolate Causative Mutations

Overview of attention for article published in PLoS Genetics, January 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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8 X users
wikipedia
1 Wikipedia page

Citations

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44 Dimensions

Readers on

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87 Mendeley
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Title
Unlocking the Bottleneck in Forward Genetics Using Whole-Genome Sequencing and Identity by Descent to Isolate Causative Mutations
Published in
PLoS Genetics, January 2013
DOI 10.1371/journal.pgen.1003219
Pubmed ID
Authors

Katherine R. Bull, Andrew J. Rimmer, Owen M. Siggs, Lisa A. Miosge, Carla M. Roots, Anselm Enders, Edward M. Bertram, Tanya L. Crockford, Belinda Whittle, Paul K. Potter, Michelle M. Simon, Ann-Marie Mallon, Steve D. M. Brown, Bruce Beutler, Christopher C. Goodnow, Gerton Lunter, Richard J. Cornall

Abstract

Forward genetics screens with N-ethyl-N-nitrosourea (ENU) provide a powerful way to illuminate gene function and generate mouse models of human disease; however, the identification of causative mutations remains a limiting step. Current strategies depend on conventional mapping, so the propagation of affected mice requires non-lethal screens; accurate tracking of phenotypes through pedigrees is complex and uncertain; out-crossing can introduce unexpected modifiers; and Sanger sequencing of candidate genes is inefficient. Here we show how these problems can be efficiently overcome using whole-genome sequencing (WGS) to detect the ENU mutations and then identify regions that are identical by descent (IBD) in multiple affected mice. In this strategy, we use a modification of the Lander-Green algorithm to isolate causative recessive and dominant mutations, even at low coverage, on a pure strain background. Analysis of the IBD regions also allows us to calculate the ENU mutation rate (1.54 mutations per Mb) and to model future strategies for genetic screens in mice. The introduction of this approach will accelerate the discovery of causal variants, permit broader and more informative lethal screens to be used, reduce animal costs, and herald a new era for ENU mutagenesis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 6%
Germany 2 2%
Argentina 1 1%
France 1 1%
Greece 1 1%
China 1 1%
Unknown 76 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 30%
Student > Ph. D. Student 17 20%
Professor > Associate Professor 9 10%
Student > Postgraduate 5 6%
Student > Master 5 6%
Other 10 11%
Unknown 15 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 51%
Biochemistry, Genetics and Molecular Biology 14 16%
Medicine and Dentistry 4 5%
Immunology and Microbiology 3 3%
Business, Management and Accounting 1 1%
Other 4 5%
Unknown 17 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 09 March 2023.
All research outputs
#5,166,142
of 25,411,814 outputs
Outputs from PLoS Genetics
#3,745
of 8,964 outputs
Outputs of similar age
#51,277
of 291,006 outputs
Outputs of similar age from PLoS Genetics
#61
of 213 outputs
Altmetric has tracked 25,411,814 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.8. This one has gotten more attention than average, scoring higher than 58% of its peers.
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 291,006 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 213 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 71% of its contemporaries.