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Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

Overview of attention for article published in BMC Genetics, January 2012
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Title
Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice
Published in
BMC Genetics, January 2012
DOI 10.1186/1471-2156-13-42
Pubmed ID
Authors

Dagmar NRG Kapell, Daniel Sorensen, Guosheng Su, Luc LG Janss, Cheryl J Ashworth, Rainer Roehe

Abstract

Genomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and pedigree information. The objective of this study was to compare polygenic, genomic and combined polygenic-genomic models, including mixture models (labelled according to the percentage of genotyped SNP markers considered to have a substantial effect, ranging from 2.5% to 100%). The data consisted of phenotypes and SNP genotypes (10,946 SNPs) of 2,188 mice. Various growth, behavioural and physiological traits were selected for the analysis to reflect a wide range of heritabilities (0.10 to 0.74) and numbers of detected quantitative traits loci (QTL) (1 to 20) affecting those traits. The analysis included estimation of variance components and cross-validation within and between families.

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

Geographical breakdown

Country Count As %
United States 3 8%
United Kingdom 2 5%
Mexico 1 3%
Indonesia 1 3%
Unknown 32 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 33%
Student > Ph. D. Student 8 21%
Student > Doctoral Student 4 10%
Student > Master 4 10%
Professor > Associate Professor 3 8%
Other 6 15%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 85%
Biochemistry, Genetics and Molecular Biology 3 8%
Unknown 3 8%

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 02 July 2012.
All research outputs
#9,905,285
of 12,372,276 outputs
Outputs from BMC Genetics
#562
of 828 outputs
Outputs of similar age
#86,563
of 120,968 outputs
Outputs of similar age from BMC Genetics
#3
of 4 outputs
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