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Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs

Overview of attention for article published in Genetics Selection Evolution, January 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#12 of 822)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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1 news outlet
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16 X users
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1 Google+ user
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1 Redditor

Citations

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36 Dimensions

Readers on

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112 Mendeley
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Title
Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs
Published in
Genetics Selection Evolution, January 2017
DOI 10.1186/s12711-016-0280-3
Pubmed ID
Authors

Serap Gonen, Janez Jenko, Gregor Gorjanc, Alan J. Mileham, C. Bruce A. Whitelaw, John M. Hickey

Abstract

This paper uses simulation to explore how gene drives can increase genetic gain in livestock breeding programs. Gene drives are naturally occurring phenomena that cause a mutation on one chromosome to copy itself onto its homologous chromosome. We simulated nine different breeding and editing scenarios with a common overall structure. Each scenario began with 21 generations of selection, followed by 20 generations of selection based on true breeding values where the breeder used selection alone, selection in combination with genome editing, or selection with genome editing and gene drives. In the scenarios that used gene drives, we varied the probability of successfully incorporating the gene drive. For each scenario, we evaluated genetic gain, genetic variance [Formula: see text], rate of change in inbreeding ([Formula: see text]), number of distinct quantitative trait nucleotides (QTN) edited, rate of increase in favourable allele frequencies of edited QTN and the time to fix favourable alleles. Gene drives enhanced the benefits of genome editing in seven ways: (1) they amplified the increase in genetic gain brought about by genome editing; (2) they amplified the rate of increase in the frequency of favourable alleles and reduced the time it took to fix them; (3) they enabled more rapid targeting of QTN with lesser effect for genome editing; (4) they distributed fixed editing resources across a larger number of distinct QTN across generations; (5) they focussed editing on a smaller number of QTN within a given generation; (6) they reduced the level of inbreeding when editing a subset of the sires; and (7) they increased the efficiency of converting genetic variation into genetic gain. Genome editing in livestock breeding results in short-, medium- and long-term increases in genetic gain. The increase in genetic gain occurs because editing increases the frequency of favourable alleles in the population. Gene drives accelerate the increase in allele frequency caused by editing, which results in even higher genetic gain over a shorter period of time with no impact on inbreeding.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 <1%
Spain 1 <1%
United States 1 <1%
Denmark 1 <1%
Unknown 108 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 19%
Student > Ph. D. Student 20 18%
Student > Master 15 13%
Student > Bachelor 5 4%
Student > Postgraduate 5 4%
Other 15 13%
Unknown 31 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 43%
Biochemistry, Genetics and Molecular Biology 15 13%
Social Sciences 4 4%
Veterinary Science and Veterinary Medicine 2 2%
Environmental Science 2 2%
Other 6 5%
Unknown 35 31%
Attention Score in Context

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 26 September 2018.
All research outputs
#1,760,881
of 25,374,647 outputs
Outputs from Genetics Selection Evolution
#12
of 822 outputs
Outputs of similar age
#35,370
of 422,004 outputs
Outputs of similar age from Genetics Selection Evolution
#1
of 15 outputs
Altmetric has tracked 25,374,647 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 822 research outputs from this source. They receive a mean Attention Score of 4.1. 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 422,004 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.