Literature DB >> 30821333

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

Everestus C Akanno1, Chinyere Ekine-Dzivenu1, Liuhong Chen1, Michael Vinsky2, Mohammed K Abo-Ismail1,3, Michael D MacNeil4, Graham Plastow1, John Basarab1,5, Changxi Li1,2, Carolyn Fitzsimmons1,2.   

Abstract

This study evaluated the use of molecular breeding values (MBVs) for carcass traits to sort steers into quality grid and lean meat yield (LMY) groups. A discovery set of 2,609 animals with genotypes and carcass phenotypes was used to predict MBVs for LMY and marbling score (MBS) for 299 Angus, 181 Charolais, and 638 Kinsella Composite steers using genomic best linear unbiased prediction. Steers were sorted in silico into four MBV groups namely Quality (with MBVs greater than the mean for LMY and MBS), Lean (with MBVs greater than the mean for LMY but less than or equal to the mean for MBS), Marbling (with MBVs greater than the mean for MBS but less than or equal to the mean for LMY), and Other (with MBVs lower than the mean for LMY and MBS). Carcass phenotypes on the steers were then collected at slaughter and evaluated for consistency with the assigned MBV groups using descriptive statistics and least square analysis. Accuracy of MBV predictions was assessed by Pearson's correlation between predicted MBV and adjusted phenotype divided by the square root of trait heritability. Genomic breed compositions were predicted for all steers to correct for possible population stratification and breed effects in the test model. The number of steers that met the expected carcass outcome was counted to produce actual percentages for each MBV group. Results showed that on average, Quality and Marbling groups had greater back-fat and more marbling across the three populations while Lean group had leaner carcasses. Carcass weights were similar across MBV groups. Within MBV groups, decreases in variability were observed for most traits suggesting improvement in carcass uniformity. Greater than 70% of the steers in Quality, Lean, and Marbling groups met the Quality Grid and Y1-LMY target for Angus and Charolais but not for Kinsella composite. The accuracy of MBV prediction ranged from 0.43 to 0.59 indicating that up to 35% of the observed carcass trait variability can be predicted, which suggests utility of MBV as a marker-assisted management tool to sort feeder cattle into uniform carcass endpoint groups under similar environmental and management conditions. Further investigation is warranted to evaluate the performance of feeder cattle sorted based on MBV and managed for different carcass endpoints as well as the cost-benefit implications for feedlot operations. © Canadian Crown Copyright 2019.

Entities:  

Keywords:  beef carcass; genomics; marker-assisted management; steers

Mesh:

Year:  2019        PMID: 30821333      PMCID: PMC6396238          DOI: 10.1093/jas/skz026

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


  9 in total

1.  Prediction of total genetic value using genome-wide dense marker maps.

Authors:  T H Meuwissen; B J Hayes; M E Goddard
Journal:  Genetics       Date:  2001-04       Impact factor: 4.562

Review 2.  Application of ultrasound for feeding and finishing animals: a review.

Authors:  P L Houghton; L M Turlington
Journal:  J Anim Sci       Date:  1992-03       Impact factor: 3.159

3.  Relationship among GeneSTAR marbling marker, intramuscular fat deposition, and expected progeny differences in early weaned Simmental steers.

Authors:  C B Rincker; N A Pyatt; L L Berger; D B Faulkner
Journal:  J Anim Sci       Date:  2006-03       Impact factor: 3.159

4.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

5.  Fast model-based estimation of ancestry in unrelated individuals.

Authors:  David H Alexander; John Novembre; Kenneth Lange
Journal:  Genome Res       Date:  2009-07-31       Impact factor: 9.043

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

Authors:  M D MacNeil; J D Nkrumah; B W Woodward; S L Northcutt
Journal:  J Anim Sci       Date:  2009-11-06       Impact factor: 3.159

7.  Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle.

Authors:  L Chen; M Vinsky; C Li
Journal:  Anim Genet       Date:  2014-11-13       Impact factor: 3.169

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

Authors:  E C Akanno; G Plastow; B W Woodward; S Bauck; H Okut; X-L Wu; C Sun; J L Aalhus; S S Moore; S P Miller; Z Wang; J A Basarab
Journal:  J Anim Sci       Date:  2014-05-06       Impact factor: 3.159

9.  Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes.

Authors:  Everestus C Akanno; Mohammed K Abo-Ismail; Liuhong Chen; John J Crowley; Zhiquan Wang; Changxi Li; John A Basarab; Michael D MacNeil; Graham S Plastow
Journal:  J Anim Sci       Date:  2018-04-03       Impact factor: 3.159

  9 in total
  1 in total

Review 1.  Genetics and nutrition impacts on herd productivity in the Northern Australian beef cattle production cycle.

Authors:  Aduli E O Malau-Aduli; Jessica Curran; Holly Gall; Erica Henriksen; Alina O'Connor; Lydia Paine; Bailey Richardson; Hannake van Sliedregt; Lucy Smith
Journal:  Vet Anim Sci       Date:  2021-12-26
  1 in total

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