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Alexandre PA, Li Y, Hine BC, Duff CJ, Ingham AB, Porto-Neto LR, Reverter A. Bias, dispersion, and accuracy of genomic predictions for feedlot and carcase traits in Australian Angus steers. Genet Sel Evol 2021; 53:77. [PMID: 34565347 PMCID: PMC8474816 DOI: 10.1186/s12711-021-00673-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 09/15/2021] [Indexed: 12/03/2022] Open
Abstract
Background Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry worldwide. Here, we used data from 3408 Australian Angus steers from seven years of birth (YOB) cohorts (2011–2017) with a minimal level of sire linkage and that were genotyped for 45,152 SNPs. Phenotypic records included two feedlot and five carcase traits, namely average daily gain (ADG), average daily dry matter intake (DMI), carcase weight (CWT), carcase eye muscle area (EMA), carcase Meat Standard Australia marbling score (MBL), carcase ossification score (OSS) and carcase subcutaneous rib fat depth (RIB). Using a 7-way cross-validation based on YOB cohorts, we tested the quality of genomic predictions using the linear regression (LR) method compared to the traditional method (Pearson’s correlation between the genomic estimated breeding value (GEBV) and its associated adjusted phenotype divided by the square root of heritability); explored the factors, such as heritability, validation cohort, and phenotype that affect estimates of accuracy, bias, and dispersion calculated with the LR method; and suggested a novel interpretation for translating differences in accuracy into phenotypic differences, based on GEBV quartiles (Q1Q4). Results Heritability (h2) estimates were generally moderate to high (from 0.29 for ADG to 0.53 for CWT). We found a strong correlation (0.73, P-value < 0.001) between accuracies using the traditional method and those using the LR method, although the LR method was less affected by random variation within and across years and showed a better ability to discriminate between extreme GEBV quartiles. We confirmed that bias of GEBV was not significantly affected by h2, validation cohort or trait. Similarly, validation cohort was not a significant source of variation for any of the GEBV quality metrics. Finally, we observed that the phenotypic differences were larger for higher accuracies. Conclusions Our estimates of h2 and GEBV quality metrics suggest a potential for accurate genomic selection of Australian Angus for feedlot performance and carcase traits. In addition, the Q1Q4 measure presented here easily translates into possible gains of genomic selection in terms of phenotypic differences and thus provides a more tangible output for commercial beef cattle producers. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-021-00673-8.
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Affiliation(s)
- Pâmela A Alexandre
- CSIRO, Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Rd., St Lucia, Brisbane, QLD, 4067, Australia
| | - Yutao Li
- CSIRO, Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Rd., St Lucia, Brisbane, QLD, 4067, Australia
| | - Brad C Hine
- CSIRO, Agriculture and Food, F.D. McMaster Laboratory, Chiswick, New England Highway, Armidale, NSW, 2350, Australia
| | - Christian J Duff
- Angus Australia, 86 Glen Innes Rd., Armidale, NSW, 2350, Australia
| | - Aaron B Ingham
- CSIRO, Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Rd., St Lucia, Brisbane, QLD, 4067, Australia
| | - Laercio R Porto-Neto
- CSIRO, Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Rd., St Lucia, Brisbane, QLD, 4067, Australia
| | - Antonio Reverter
- CSIRO, Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Rd., St Lucia, Brisbane, QLD, 4067, Australia.
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Reverter A, Hine BC, Porto-Neto L, Li Y, Duff CJ, Dominik S, Ingham AB. ImmuneDEX: a strategy for the genetic improvement of immune competence in Australian Angus cattle. J Anim Sci 2021; 99:6156144. [PMID: 33677583 DOI: 10.1093/jas/skaa384] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 11/22/2020] [Indexed: 01/07/2023] Open
Abstract
In animal breeding and genetics, the ability to cope with disease, here defined as immune competence (IC), with minimal detriment to growth and fertility is a desired objective which addresses both animal production and welfare considerations. However, defining and objectively measuring IC phenotypes using testing methods which are practical to apply on-farm has been challenging. Based on previously described protocols, we measured both cell-mediated immune response (Cell-IR) and antibody-mediated immune response (Ab-IR) and combined these measures to determine an animal's IC. Using a population of 2,853 Australian Angus steers and heifers, we compared 2 alternative methods to combine both metrics into a single phenotype to be used as a tool for the genetic improvement of IC. The first method, named ZMEAN, is obtained by taking the average of the individual metrics after subjecting each to a Z-score standardization. The second, ImmuneDEX (IDEX), is a weighted average that considers the correlation between Cell-IR and Ab-IR, as well as the difference in ranking of individuals by each metric, and uses these as weights in the averaging. Both simulation and real data were used to understand the behavior of ZMEAN and IDEX. To further ascertain the relationship between IDEX and other traits of economic importance, we evaluated a range of traits related to growth, feedlot performance, and carcass characteristics. We report estimates of heritability of 0.31 ± 0.06 for Cell-IR, 0.42 ± 0.06 for Ab-IR, 0.42 ± 0.06 for ZMEAN and 0.370 ± 0.06 for IDEX, as well as a unity genetic correlation (rg) between ZMEAN and IDEX. While a moderately positive rg was estimated between Cell-IR and Ab-IR (rg = 0.33 ± 0.12), strongly positive estimates were obtained between IDEX and Cell-IR (rg = 0.80 ± 0.05) and between IDEX and Ab-IR (rg = 0.85 ± 0.04). We obtained a moderately negative rg between IC traits and growth including an rg = -0.38 ± 0.14 between IDEX and weaning weight, and negligible with carcass fat measurements, including an rg = -0.03 ± 0.12 between IDEX and marbling. Given that breeding with a sole focus on production might inadvertently increase susceptibility to disease and associated antibiotic use, our analyses suggest that ImmuneDEX will provide a basis to breed animals that are both highly productive and with an enhanced ability to resist disease.
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Affiliation(s)
- Antonio Reverter
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia
| | - Brad C Hine
- CSIRO Agriculture & Food, F.D. McMaster Laboratory, Chiswick, New England Highway, Armidale, NSW, Australia
| | - Laercio Porto-Neto
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia
| | - Yutao Li
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia
| | | | - Sonja Dominik
- CSIRO Agriculture & Food, F.D. McMaster Laboratory, Chiswick, New England Highway, Armidale, NSW, Australia
| | - Aaron B Ingham
- CSIRO Agriculture & Food, Queensland Bioscience Precinct, St. Lucia, Brisbane, QLD, Australia
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Live animal predictions of carcass components and marble score in beef cattle: model development and evaluation. Animal 2020; 14:s396-s405. [PMID: 32172725 DOI: 10.1017/s1751731120000324] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Until recently, beef carcass payment grids were predominantly based on weight and fatness categories with some adjustment for age, defined as number of adult teeth, to determine the price received by Australian beef producers for slaughter cattle. With the introduction of the Meat Standards Australia (MSA) grading system, the beef industry has moved towards payments that account for intramuscular fat (IMF) content (marble score (MarbSc)) and MSA grades. The possibility of a payment system based on lean meat yield (LMY, %) has also been raised. The BeefSpecs suite of tools has been developed to assist producers to meet current market specifications, specifically P8-rump fat and hot standard carcass weight (HCW). A series of equations have now been developed to partition empty body fat and fat-free weight into carcass fat-free mass (FFM) and fat mass (FM) and then into flesh FFM (FleshFFM) and flesh FM (FleshFM) to predict carcass components from live cattle assessments. These components then predict denuded lean (kg) and finally LMY (%) that contribute to emerging market specifications. The equations, along with the MarbSc equation, are described and then evaluated using two independent datasets. The decomposition of evaluation datasets demonstrates that error in prediction of HCW (kg), bone weight (BoneWt, kg), FleshFFM (kg), FleshFM (kg), MarbSc and chemical IMF percentage (ChemIMF%) is shown to be largely random error (%) in evaluation dataset 1, though error for ChemIMF% was primarily slope bias (%) in evaluation dataset 1, and BoneWt had substantial mean bias (%) in evaluation dataset 2. High modelling efficiencies of 0.97 and 0.95 for predicting HCW for evaluation datasets 1 and 2, respectively, suggest a high level of accuracy and precision in the prediction of HCW. The new outputs of the model are then described as to their role in estimating MSA index scores. The modelling system to partition chemical components of the empty body into carcass components is not dependent on the base modelling system used to derive empty body FFM and FM. This can be considered a general process that could be used with any appropriate model of body composition.
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