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The impact of genome editing on the introduction of monogenic traits in livestock

Overview of attention for article published in Genetics Selection Evolution, April 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 522)
  • High Attention Score compared to outputs of the same age (88th percentile)

Mentioned by

blogs
1 blog
twitter
19 tweeters

Citations

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9 Dimensions

Readers on

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52 Mendeley
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Title
The impact of genome editing on the introduction of monogenic traits in livestock
Published in
Genetics Selection Evolution, April 2018
DOI 10.1186/s12711-018-0389-7
Pubmed ID
Authors

John W. M. Bastiaansen, Henk Bovenhuis, Martien A. M. Groenen, Hendrik-Jan Megens, Han A. Mulder

Abstract

Genome editing technologies provide new tools for genetic improvement and have the potential to become the next game changer in animal and plant breeding. The aim of this study was to investigate how genome editing in combination with genomic selection can accelerate the introduction of a monogenic trait in a livestock population as compared to genomic selection alone. A breeding population was simulated under genomic selection for a polygenic trait. After reaching Bulmer equilibrium, the selection objective was to increase the allele frequency of a monogenic trait, with or without genome editing, in addition to improving the polygenic trait. Scenarios were compared for time to fixation of the desired allele, selection response for the polygenic trait, and level of inbreeding. The costs, in terms of number of editing procedures, were compared to the benefits of having more animals with the desired phenotype of the monogenic trait. Effects of reduced editing efficiency were investigated. In a population of 20,000 selection candidates per generation, the total number of edited zygotes needed to reach fixation of the desired allele was 22,118, 7072, or 3912 with, no, moderate, or high selection emphasis on the monogenic trait, respectively. Genome editing resulted in up to four-fold faster fixation of the desired allele when efficiency was 100%, while the loss in long-term selection response for the polygenic trait was up to seven-fold less compared to genomic selection alone. With moderate selection emphasis on the monogenic trait, introduction of genome editing led to a four-fold reduction in the total number of animals showing the undesired phenotype before fixation. However, with a currently realistic editing efficiency of 4%, the number of required editing procedures increased by 72% and loss in selection response increased eight-fold compared to 100% efficiency. With low efficiency, loss in selection response was 29% more compared to genomic selection alone. Genome editing strongly decreased the time to fixation for a desired allele compared to genomic selection alone. Reduced editing efficiency had a major impact on the number of editing procedures and on the loss in selection response. In addition to ethical and welfare considerations of genome editing, a careful assessment of its technical costs and benefits is required.

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 38%
Student > Ph. D. Student 13 25%
Student > Postgraduate 3 6%
Student > Master 3 6%
Other 3 6%
Other 6 12%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 69%
Biochemistry, Genetics and Molecular Biology 8 15%
Computer Science 1 2%
Psychology 1 2%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 06 October 2019.
All research outputs
#895,609
of 14,568,967 outputs
Outputs from Genetics Selection Evolution
#10
of 522 outputs
Outputs of similar age
#30,381
of 275,934 outputs
Outputs of similar age from Genetics Selection Evolution
#1
of 1 outputs
Altmetric has tracked 14,568,967 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 522 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 98% 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 275,934 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 88% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them