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Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants

Overview of attention for article published in Genetics Selection Evolution, April 2018
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Title
Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants
Published in
Genetics Selection Evolution, April 2018
DOI 10.1186/s12711-018-0387-9
Pubmed ID
Authors

Chunyan Zhang, Robert Alan Kemp, Paul Stothard, Zhiquan Wang, Nicholas Boddicker, Kirill Krivushin, Jack Dekkers, Graham Plastow

Abstract

Increasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs. Phenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG. Use of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.

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Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Researcher 6 29%
Student > Doctoral Student 3 14%
Student > Master 2 10%
Student > Postgraduate 1 5%
Other 3 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 71%
Unspecified 3 14%
Biochemistry, Genetics and Molecular Biology 1 5%
Computer Science 1 5%
Veterinary Science and Veterinary Medicine 1 5%
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 08 April 2018.
All research outputs
#10,205,714
of 12,775,568 outputs
Outputs from Genetics Selection Evolution
#336
of 440 outputs
Outputs of similar age
#206,924
of 274,017 outputs
Outputs of similar age from Genetics Selection Evolution
#8
of 8 outputs
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