↓ Skip to main content

Detailed phenotyping identifies genes with pleiotropic effects on body composition

Overview of attention for article published in BMC Genomics, March 2016
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
25 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Detailed phenotyping identifies genes with pleiotropic effects on body composition
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2538-0
Pubmed ID
Authors

Sunduimijid Bolormaa, Ben J. Hayes, Julius H.J. van der Werf, David Pethick, Michael E. Goddard, Hans D. Daetwyler

Abstract

Genetic variation in both the composition and distribution of fat and muscle in the body is important to human health as well as the healthiness and value of meat from cattle and sheep. Here we use detailed phenotyping and a multi-trait approach to identify genes explaining variation in body composition traits. A multi-trait genome wide association analysis of 56 carcass composition traits measured on 10,613 sheep with imputed and real genotypes on 510,174 SNPs was performed. We clustered 71 significant SNPs into five groups based on their pleiotropic effects across the 56 traits. Among these 71 significant SNPs, one group of 11 SNPs affected the fatty acid profile of the muscle and were close to 8 genes involved in fatty acid or triglyceride synthesis. Another group of 23 SNPs had an effect on mature size, based on their pattern of effects across traits, but the genes near this group of SNPs did not share any obvious function. Many of the likely candidate genes near SNPs with significant pleiotropic effects on the 56 traits are involved in intra-cellular signalling pathways. Among the significant SNPs were some with a convincing candidate gene due to the function of the gene (e.g. glycogen synthase affecting glycogen concentration) or because the same gene was associated with similar traits in other species. Using a multi-trait analysis increased the power to detect associations between SNP and body composition traits compared with the single trait analyses. Detailed phenotypic information helped to identify a convincing candidate in some cases as did information from other species.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 28%
Student > Master 5 20%
Student > Ph. D. Student 5 20%
Student > Doctoral Student 2 8%
Student > Bachelor 2 8%
Other 4 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 72%
Biochemistry, Genetics and Molecular Biology 2 8%
Medicine and Dentistry 2 8%
Veterinary Science and Veterinary Medicine 2 8%
Unspecified 1 4%
Other 0 0%

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 13 March 2016.
All research outputs
#6,391,443
of 7,386,232 outputs
Outputs from BMC Genomics
#4,824
of 5,424 outputs
Outputs of similar age
#233,899
of 276,558 outputs
Outputs of similar age from BMC Genomics
#197
of 217 outputs
Altmetric has tracked 7,386,232 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,424 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 1st percentile – i.e., 1% 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 276,558 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.