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QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies

Overview of attention for article published in BMC Genomics, November 2014
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Title
QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies
Published in
BMC Genomics, November 2014
DOI 10.1186/1471-2164-15-1004
Pubmed ID
Authors

Mahdi Saatchi, Jonathan E Beever, Jared E Decker, Dan B Faulkner, Harvey C Freetly, Stephanie L Hansen, Helen Yampara-Iquise, Kristen A Johnson, Stephen D Kachman, Monty S Kerley, JaeWoo Kim, Daniel D Loy, Elisa Marques, Holly L Neibergs, E John Pollak, Robert D Schnabel, Christopher M Seabury, Daniel W Shike, Warren M Snelling, Matthew L Spangler, Robert L Weaber, Dorian J Garrick, Jeremy F Taylor

Abstract

The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental×Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake.

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The data shown below were collected from the profile of 1 X user 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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Colombia 1 1%
Argentina 1 1%
Unknown 91 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 18%
Student > Ph. D. Student 15 16%
Researcher 14 15%
Student > Bachelor 7 7%
Professor > Associate Professor 6 6%
Other 18 19%
Unknown 18 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 60%
Biochemistry, Genetics and Molecular Biology 5 5%
Veterinary Science and Veterinary Medicine 3 3%
Nursing and Health Professions 1 1%
Social Sciences 1 1%
Other 2 2%
Unknown 26 27%
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 10 January 2015.
All research outputs
#19,946,609
of 24,514,423 outputs
Outputs from BMC Genomics
#8,538
of 10,999 outputs
Outputs of similar age
#273,826
of 372,356 outputs
Outputs of similar age from BMC Genomics
#201
of 260 outputs
Altmetric has tracked 24,514,423 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,999 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 260 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.