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Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations

Overview of attention for article published in BMC Bioinformatics, July 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
6 tweeters
facebook
1 Facebook page

Citations

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41 Dimensions

Readers on

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73 Mendeley
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Title
Rapid screening for phenotype-genotype associations by linear transformations of genomic evaluations
Published in
BMC Bioinformatics, July 2014
DOI 10.1186/1471-2105-15-246
Pubmed ID
Authors

Jose L Gualdrón Duarte, Rodolfo JC Cantet, Ronald O Bates, Catherine W Ernst, Nancy E Raney, Juan P Steibel

Abstract

Currently, association studies are analysed using statistical mixed models, with marker effects estimated by a linear transformation of genomic breeding values. The variances of marker effects are needed when performing the tests of association. However, approaches used to estimate the parameters rely on a prior variance or on a constant estimate of the additive variance. Alternatively, we propose a standardized test of association using the variance of each marker effect, which generally differ among each other. Random breeding values from a mixed model including fixed effects and a genomic covariance matrix are linearly transformed to estimate the marker effects.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 1%
United States 1 1%
Denmark 1 1%
Argentina 1 1%
Unknown 69 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 25%
Student > Ph. D. Student 16 22%
Student > Master 10 14%
Student > Doctoral Student 5 7%
Student > Postgraduate 4 5%
Other 10 14%
Unknown 10 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 58%
Biochemistry, Genetics and Molecular Biology 6 8%
Computer Science 5 7%
Veterinary Science and Veterinary Medicine 2 3%
Medicine and Dentistry 2 3%
Other 2 3%
Unknown 14 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 February 2022.
All research outputs
#6,192,228
of 21,173,213 outputs
Outputs from BMC Bioinformatics
#2,426
of 6,883 outputs
Outputs of similar age
#57,788
of 207,603 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 18 outputs
Altmetric has tracked 21,173,213 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 6,883 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 64% 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 207,603 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 71% of its contemporaries.
We're also able to compare this research output to 18 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 55% of its contemporaries.