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A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle

Overview of attention for article published in PLoS Genetics, March 2014
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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6 tweeters

Citations

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77 Dimensions

Readers on

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110 Mendeley
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Title
A Multi-Trait, Meta-analysis for Detecting Pleiotropic Polymorphisms for Stature, Fatness and Reproduction in Beef Cattle
Published in
PLoS Genetics, March 2014
DOI 10.1371/journal.pgen.1004198
Pubmed ID
Authors

Sunduimijid Bolormaa, Jennie E. Pryce, Antonio Reverter, Yuandan Zhang, William Barendse, Kathryn Kemper, Bruce Tier, Keith Savin, Ben J. Hayes, Michael E. Goddard

Abstract

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V-1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.

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 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Poland 1 <1%
Brazil 1 <1%
Colombia 1 <1%
Unknown 104 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 28%
Researcher 26 24%
Student > Doctoral Student 16 15%
Student > Master 15 14%
Unspecified 6 5%
Other 16 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 81 74%
Biochemistry, Genetics and Molecular Biology 12 11%
Unspecified 9 8%
Medicine and Dentistry 3 3%
Computer Science 1 <1%
Other 4 4%

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 31 March 2014.
All research outputs
#3,390,480
of 12,637,311 outputs
Outputs from PLoS Genetics
#3,242
of 6,406 outputs
Outputs of similar age
#49,302
of 189,624 outputs
Outputs of similar age from PLoS Genetics
#109
of 200 outputs
Altmetric has tracked 12,637,311 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 6,406 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one is in the 49th percentile – i.e., 49% 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 189,624 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 73% of its contemporaries.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.