Literature DB >> 24802042

Reliability of molecular breeding values for Warner-Bratzler shear force and carcass traits of beef cattle - an independent validation study.

E C Akanno1, G Plastow1, B W Woodward2, S Bauck3, H Okut4, X-L Wu5, C Sun6, J L Aalhus7, S S Moore8, S P Miller9, Z Wang1, J A Basarab10.   

Abstract

Interest in genetic improvement of carcass and tenderness traits of beef cattle using genome-based selection (GS) and marker-assisted management programs is increasing. The success of such a program depends on the presence of linkage disequilibrium between the observed markers and the underlying QTL as well as on the relationship between the discovery, validation, and target populations. For molecular breeding values (MBV) predicted for a target population using SNP markers, reliabilities of these MBV can be obtained from validation analyses conducted in an independent population distinct from the discovery set. The objective of this study was to test MBV predicted for carcass and tenderness traits of beef cattle in a Canadian-based validation population that is largely independent of a United States-based discovery set. The discovery data set comprised of genotypes and phenotypes from >2,900 multibreed beef cattle while the validation population consisted of 802 crossbred feeder heifers and steers. A bivariate animal model that fitted actual phenotype and MBV was used for validation analyses. The reliability of MBV was defined as square of the genetic correlation (R(2) g) that represents the proportion of the additive genetic variance explained by the SNP markers. Several scenarios involving different starting marker panels (384, 3K, 7K, and 50K) and different sets of SNP selected to compute MBV (50, 100, 200, 375, 400, 600, and 800) were investigated. Validation results showed that the most reliable MBV (R(2) g) were 0.34 for HCW, 0.36 for back fat thickness, 0.28 for rib eye area, 0.30 for marbling score, 0.25 for yield grade, and 0.38 for Warner-Bratzler shear force across the different scenarios explored. The results indicate that smaller SNP panels can be developed for use in genetic improvement of beef carcass and tenderness traits to exploit GS benefits.

Entities:  

Keywords:  genetic improvement; genome-based selection; single nucleotide polymorphism; tenderness; validation

Mesh:

Substances:

Year:  2014        PMID: 24802042     DOI: 10.2527/jas.2013-7374

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  5 in total

1.  Evaluation of a genomic-enhanced sorting system for feeder cattle1.

Authors:  Everestus C Akanno; Chinyere Ekine-Dzivenu; Liuhong Chen; Michael Vinsky; Mohammed K Abo-Ismail; Michael D MacNeil; Graham Plastow; John Basarab; Changxi Li; Carolyn Fitzsimmons
Journal:  J Anim Sci       Date:  2019-03-01       Impact factor: 3.159

2.  Phenotypic and genetic correlations of fatty acid composition in subcutaneous adipose tissue with carcass merit and meat tenderness traits in Canadian beef cattle.

Authors:  C Ekine-Dzivenu; M Vinsky; J A Basarab; J L Aalhus; M E R Dugan; C Li
Journal:  J Anim Sci       Date:  2017-12       Impact factor: 3.159

3.  Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle.

Authors:  Jiyuan Li; Yining Wang; Robert Mukiibi; Brian Karisa; Graham S Plastow; Changxi Li
Journal:  Sci Rep       Date:  2022-03-01       Impact factor: 4.379

4.  Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle.

Authors:  B Zhu; P Guo; Z Wang; W Zhang; Y Chen; L Zhang; H Gao; Z Wang; X Gao; L Xu; J Li
Journal:  Anim Genet       Date:  2019-09-09       Impact factor: 3.169

5.  Genomic Heritability and Genome-Wide Association Studies of Plasma Metabolites in Crossbred Beef Cattle.

Authors:  Jiyuan Li; Everestus C Akanno; Tiago S Valente; Mohammed Abo-Ismail; Brian K Karisa; Zhiquan Wang; Graham S Plastow
Journal:  Front Genet       Date:  2020-09-24       Impact factor: 4.599

  5 in total

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