Literature DB >> 19897629

Genetic evaluation of Angus cattle for carcass marbling using ultrasound and genomic indicators.

M D MacNeil1, J D Nkrumah, B W Woodward, S L Northcutt.   

Abstract

The objectives were to estimate genetic parameters needed to elucidate the relationships of a molecular breeding value (MBV) for marbling, intramuscular fat (IMF) of yearling bulls measured with ultrasound, and marbling score (MRB) of slaughtered steers, and to assess the utility of MBV and IMF in predicting the breeding value for MRB. Records for MRB (n = 38,296) and IMF (n = 6,594) were from the American Angus Association database used for national cattle evaluation. A total of 1,006 records of MBV were used in this study. (Co)variance components were estimated with ASREML, fitting an animal model with fixed contemporary groups for MRB and IMF similar to those used in the Angus national genetic evaluation. The overall mean was the only fixed effect included in the model for MBV. Heritability estimates for carcass measures were 0.48 +/- 0.03, 0.31 +/- 0.03, and 0.98 +/- 0.05 for MRB, IMF, and MBV, respectively. Genetic correlations of IMF and MBV with MRB were 0.56 +/- 0.09 and 0.38 +/- 0.10, respectively. The genetic correlation between IMF and MBV was 0.80 +/- 0.22. These results indicate the MBV evaluated may yield a greater genetic advance of approximately 20% when used as an indicator trait for genetic prediction of MRB compared with IMF. However, neither of these indicators alone provides sufficient information to produce highly accurate prediction of breeding value for the economically relevant trait MRB. Given that the goal is a highly accurate prediction of true breeding value for MRB, results of this work point to the need to 1) continue progeny testing, and 2) continue increasing the genetic correlation between the MBV and MRB.

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Year:  2009        PMID: 19897629     DOI: 10.2527/jas.2009-2022

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


  13 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

Review 2.  The nature, scope and impact of genomic prediction in beef cattle in the United States.

Authors:  Dorian J Garrick
Journal:  Genet Sel Evol       Date:  2011-05-15       Impact factor: 4.297

3.  Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

Authors:  Gabriel Soares Campos; Fernando Antônio Reimann; Leandro Lunardini Cardoso; Carlos Eduardo Ranquetat Ferreira; Vinicius Silva Junqueira; Patricia Iana Schmidt; José Braccini Neto; Marcos Jun Iti Yokoo; Bruna Pena Sollero; Arione Augusti Boligon; Fernando Flores Cardoso
Journal:  J Anim Sci       Date:  2018-06-29       Impact factor: 3.159

4.  Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle.

Authors:  Vinzent Boerner; David J Johnston; Bruce Tier
Journal:  Genet Sel Evol       Date:  2014-10-24       Impact factor: 4.297

5.  Accounting for genomic pre-selection in national BLUP evaluations in dairy cattle.

Authors:  Clotilde Patry; Vincent Ducrocq
Journal:  Genet Sel Evol       Date:  2011-08-18       Impact factor: 4.297

6.  Comparison of molecular breeding values based on within- and across-breed training in beef cattle.

Authors:  Stephen D Kachman; Matthew L Spangler; Gary L Bennett; Kathryn J Hanford; Larry A Kuehn; Warren M Snelling; R Mark Thallman; Mahdi Saatchi; Dorian J Garrick; Robert D Schnabel; Jeremy F Taylor; E John Pollak
Journal:  Genet Sel Evol       Date:  2013-08-16       Impact factor: 4.297

7.  Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle - A review.

Authors:  Seung Ju Park; Seok-Hyeon Beak; Da Jin Sol Jung; Sang Yeob Kim; In Hyuk Jeong; Min Yu Piao; Hyeok Joong Kang; Dilla Mareistia Fassah; Sang Weon Na; Seon Pil Yoo; Myunggi Baik
Journal:  Asian-Australas J Anim Sci       Date:  2018-05-31       Impact factor: 2.509

8.  Phenotyping for Genetic Improvement of Feed Efficiency in Fish: Lessons From Pig Breeding.

Authors:  Pieter W Knap; Antti Kause
Journal:  Front Genet       Date:  2018-05-24       Impact factor: 4.599

9.  Accuracy of direct genomic breeding values for nationally evaluated traits in US Limousin and Simmental beef cattle.

Authors:  Mahdi Saatchi; Robert D Schnabel; Megan M Rolf; Jeremy F Taylor; Dorian J Garrick
Journal:  Genet Sel Evol       Date:  2012-12-07       Impact factor: 4.297

10.  Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions.

Authors:  Luis O Tedeschi
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

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