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Genome position specific priors for genomic prediction

Overview of attention for article published in BMC Genomics, October 2012
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Title
Genome position specific priors for genomic prediction
Published in
BMC Genomics, October 2012
DOI 10.1186/1471-2164-13-543
Pubmed ID
Authors

Rasmus Froberg Brøndum, Guosheng Su, Mogens Sandø Lund, Philip J Bowman, Michael E Goddard, Benjamin J Hayes

Abstract

The accuracy of genomic prediction is highly dependent on the size of the reference population. For small populations, including information from other populations could improve this accuracy. The usual strategy is to pool data from different populations; however, this has not proven as successful as hoped for with distantly related breeds. BayesRS is a novel approach to share information across populations for genomic predictions. The approach allows information to be captured even where the phase of SNP alleles and casuative mutation alleles are reversed across populations, or the actual casuative mutation is different between the populations but affects the same gene. Proportions of a four-distribution mixture for SNP effects in segments of fixed size along the genome are derived from one population and set as location specific prior proportions of distributions of SNP effects for the target population. The model was tested using dairy cattle populations of different breeds: 540 Australian Jersey bulls, 2297 Australian Holstein bulls and 5214 Nordic Holstein bulls. The traits studied were protein-, fat- and milk yield. Genotypic data was Illumina 777K SNPs, real or imputed.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 2 4%
France 1 2%
Denmark 1 2%
United States 1 2%
Poland 1 2%
Unknown 49 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 36%
Student > Ph. D. Student 12 22%
Student > Master 6 11%
Student > Doctoral Student 5 9%
Other 4 7%
Other 4 7%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 76%
Computer Science 3 5%
Biochemistry, Genetics and Molecular Biology 2 4%
Medicine and Dentistry 2 4%
Sports and Recreations 1 2%
Other 1 2%
Unknown 4 7%
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 12 October 2012.
All research outputs
#17,285,668
of 25,373,627 outputs
Outputs from BMC Genomics
#7,120
of 11,244 outputs
Outputs of similar age
#125,794
of 191,530 outputs
Outputs of similar age from BMC Genomics
#121
of 192 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 192 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.