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Efficient Breeding by Genomic Mating

Overview of attention for article published in Frontiers in Genetics, November 2016
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Title
Efficient Breeding by Genomic Mating
Published in
Frontiers in Genetics, November 2016
DOI 10.3389/fgene.2016.00210
Pubmed ID
Authors

Deniz Akdemir, Julio I. Sánchez

Abstract

Selection in breeding programs can be done by using phenotypes (phenotypic selection), pedigree relationship (breeding value selection) or molecular markers (marker assisted selection or genomic selection). All these methods are based on truncation selection, focusing on the best performance of parents before mating. In this article we proposed an approach to breeding, named genomic mating, which focuses on mating instead of truncation selection. Genomic mating uses information in a similar fashion to genomic selection but includes information on complementation of parents to be mated. Following the efficiency frontier surface, genomic mating uses concepts of estimated breeding values, risk (usefulness) and coefficient of ancestry to optimize mating between parents. We used a genetic algorithm to find solutions to this optimization problem and the results from our simulations comparing genomic selection, phenotypic selection and the mating approach indicate that current approach for breeding complex traits is more favorable than phenotypic and genomic selection. Genomic mating is similar to genomic selection in terms of estimating marker effects, but in genomic mating the genetic information and the estimated marker effects are used to decide which genotypes should be crossed to obtain the next breeding population.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
France 1 <1%
Korea, Republic of 1 <1%
Unknown 155 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 23%
Researcher 35 22%
Student > Master 15 9%
Student > Doctoral Student 11 7%
Other 10 6%
Other 22 14%
Unknown 30 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 96 60%
Biochemistry, Genetics and Molecular Biology 14 9%
Engineering 5 3%
Veterinary Science and Veterinary Medicine 4 3%
Mathematics 2 1%
Other 7 4%
Unknown 31 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 November 2016.
All research outputs
#18,483,671
of 22,903,988 outputs
Outputs from Frontiers in Genetics
#7,081
of 11,949 outputs
Outputs of similar age
#304,685
of 416,538 outputs
Outputs of similar age from Frontiers in Genetics
#35
of 45 outputs
Altmetric has tracked 22,903,988 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,949 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 27th percentile – i.e., 27% 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 416,538 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.