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A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses

Overview of attention for article published in Genetics Selection Evolution, September 2014
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  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

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7 X users

Citations

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154 Dimensions

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126 Mendeley
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Title
A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses
Published in
Genetics Selection Evolution, September 2014
DOI 10.1186/1297-9686-46-50
Pubmed ID
Authors

Rohan L Fernando, Jack CM Dekkers, Dorian J Garrick

Abstract

To obtain predictions that are not biased by selection, the conditional mean of the breeding values must be computed given the data that were used for selection. When single nucleotide polymorphism (SNP) effects have a normal distribution, it can be argued that single-step best linear unbiased prediction (SS-BLUP) yields a conditional mean of the breeding values. Obtaining SS-BLUP, however, requires computing the inverse of the dense matrix G of genomic relationships, which will become infeasible as the number of genotyped animals increases. Also, computing G requires the frequencies of SNP alleles in the founders, which are not available in most situations. Furthermore, SS-BLUP is expected to perform poorly relative to variable selection models such as BayesB and BayesC as marker densities increase.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
France 1 <1%
Brazil 1 <1%
New Zealand 1 <1%
Finland 1 <1%
Denmark 1 <1%
Mexico 1 <1%
Unknown 118 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 21%
Researcher 24 19%
Student > Master 15 12%
Student > Doctoral Student 10 8%
Professor 6 5%
Other 23 18%
Unknown 21 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 83 66%
Biochemistry, Genetics and Molecular Biology 9 7%
Chemistry 3 2%
Computer Science 2 2%
Medicine and Dentistry 2 2%
Other 6 5%
Unknown 21 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 2014.
All research outputs
#7,263,349
of 25,371,288 outputs
Outputs from Genetics Selection Evolution
#236
of 822 outputs
Outputs of similar age
#71,855
of 262,419 outputs
Outputs of similar age from Genetics Selection Evolution
#3
of 16 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 70% 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 262,419 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.