On the value of adding commercial data into the reference population of the Angus SteerSELECT genomic tool
Antonio Reverter
A * , Laercio Porto-Neto A , Brad C. Hine B , Pamela A. Alexandre A , Malshani Samaraweera C , Andrew I. Byrne C , Aaron B. Ingham A and Christian J. Duff C
+ Author Affiliations
- Author Affiliations
A CSIRO Agriculture and Food, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, Brisbane, Qld 4067, Australia.
B CSIRO Agriculture and Food, F.D. McMaster Laboratory, Chiswick, New England Highway, Armidale, NSW 2350, Australia.
C Angus Australia, 86 Glen Innes Road, Armidale, NSW 2350, Australia.
Animal Production Science 63(11) 947-956 https://doi.org/10.1071/AN22452
Submitted: 13 December 2022 Accepted: 20 February 2023 Published: 14 March 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: Angus SteerSELECT is a genomic tool designed to provide genomic estimated breeding values (GEBV) for nine traits related to growth, feedlot performance, carcase characteristics and immune competence. At present, GEBV for carcase characteristics are based on a reference population of 3766 Australian Angus steers.
Aims: We aimed to investigate the potential benefit of incorporating commercial data into the existing reference population of the Angus SteerSELECT. To this aim, we employ a population of 2124 genotyped commercial Angus steers with carcase performance data from four commercial feedlot operators.
Methods: The benefit of incorporating the commercial data (COMM) into the reference (REFE) population was assessed in terms of quality and integrity of the COMM data and meta-data to model the phenotypes adequately. We computed bias, dispersion, and accuracy of GEBV for carcase weight (CWT) and marbling (MARB) before and after including the COMM data, in whole or in partial, into the REFE population.
Key results: The genomic estimate of the Angus content in the COMM population averaged 96.9% and ranged from 32.87% to 100%. For CWT, the estimates of heritability were 0.419 ± 0.026 and 0.368 ± 0.038 for the REFE and COMM populations respectively, and with a genetic correlation of 0.756 ± 0.068. For MARB, the same three parameter estimates were 0.357 ± 0.027, 0.340 ± 0.038 and 0.879 ± 0.073 respectively. The ACC of CWT GEBV increased significantly (P < 0.0001) from 0.475 when the COMM population was not part of the REFE to 0.546 (or 15%) when a random 50% of the COMM population was included in the REFE. Similarly significant increases in ACC were observed for MARB GEBV (0.470–0.521 or 11%).
Conclusions: The strong genomic relationship between the REFE and the COMM populations, coupled with the significant increases in GEBV accuracies, demonstrated the potential benefits of including the COMM population into the reference population of a future improved version of the Angus SteerSELECT genomic tool.
Implications: Commercial feedlot operators finishing animals with a strong Angus breed component will benefit from having their data represented in the reference population of the Angus SteerSELECT genomic tool.
Keywords: accuracy, beef cattle, bias, carcase, feedlot, genomic predictions, heritability, marbling.
References
Alexandre PA, Porto-Neto LR, Hine B, Ingham A, Duff C, Samaraweera M, Reverter A (2022) Validation of HeiferSELECT genomic product using historical data. In ‘Proceedings of the 12th world congress on genetics applied to livestock production, 3–8 July 2022, Rotterdam, The Netherlands’. (Wageningen Academic Publishers)AUS-MEAT (2005) ‘Handbook of Australian meat international red meat manual.’ 7th edn. (AUS-MEAT: Sydney, NSW, Australia)
Bolormaa S, Pryce JE, Kemper K, Savin K, Hayes BJ, Barendse W, Zhang Y, Reich CM, Mason BA, Bunch RJ, Harrison BE, Reverter A, Herd RM, Tier B, Graser H-U, Goddard ME (2013) Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. Journal of Animal Science 91, 3088–3104.
| Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.Crossref | GoogleScholarGoogle Scholar |
Clark SA, Hickey JM, van der Werf JHJ (2011) Different models of genetic variation and their effect on genomic evaluation. Genetics Selection Evolution 43, 18
| Different models of genetic variation and their effect on genomic evaluation.Crossref | GoogleScholarGoogle Scholar |
Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3, e3395
| Accuracy of predicting the genetic risk of disease using a genome-wide approach.Crossref | GoogleScholarGoogle Scholar |
Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.
| The impact of genetic architecture on genome-wide evaluation methods.Crossref | GoogleScholarGoogle Scholar |
de los Campos G, Vazquez AI, Fernando R, Klimentidis YC, Sorensen D (2013) Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genetics 9, e1003608
| Prediction of complex human traits using the genomic best linear unbiased predictor.Crossref | GoogleScholarGoogle Scholar |
de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183, 1545–1553.
| Reliability of genomic predictions across multiple populations.Crossref | GoogleScholarGoogle Scholar |
Druet T, Macleod IM, Hayes BJ (2014) Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity 112, 39–47.
| Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions.Crossref | GoogleScholarGoogle Scholar |
Duff CJ, van der Werf JHJ, Parnell PF, Clark SA (2021) Redefining residual feed intake to account for marbling fat in beef breeding programs. Animal Production Science 61, 1837–1844.
| Redefining residual feed intake to account for marbling fat in beef breeding programs.Crossref | GoogleScholarGoogle Scholar |
Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257.
| Genomic selection: prediction of accuracy and maximisation of long term response.Crossref | GoogleScholarGoogle Scholar |
Habier D, Fernando RL, Dekkers JCM (2007) The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.
| The impact of genetic relationship information on genome-assisted breeding values.Crossref | GoogleScholarGoogle Scholar |
Habier D, Tetens J, Seefried F-R, Lichtner P, Thaller G (2010) The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics Selection Evolution 42, 5
| The impact of genetic relationship information on genomic breeding values in German Holstein cattle.Crossref | GoogleScholarGoogle Scholar |
Hine BC, Duff CJ, Byrne A, Parnell P, Porto-Neto L, Li Y, Ingham AB, Reverter A (2021) Development of Angus SteerSELECT: a genomic-based tool to identify performance differences of Australian Angus steers during feedlot finishing: phase 1 validation. Animal Production Science 61, 1884–1892.
| Development of Angus SteerSELECT: a genomic-based tool to identify performance differences of Australian Angus steers during feedlot finishing: phase 1 validation.Crossref | GoogleScholarGoogle Scholar |
Karaman E, Su G, Croue I, Lund MS (2021) Genomic prediction using a reference population of multiple pure breeds and admixed individuals. Genetics Selection Evolution 53, 46
| Genomic prediction using a reference population of multiple pure breeds and admixed individuals.Crossref | GoogleScholarGoogle Scholar |
Karoui S, Carabano MJ, Diaz C, Legarra A (2012) Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genetics Selection Evolution 44, 39
| Joint genomic evaluation of French dairy cattle breeds using multiple-trait models.Crossref | GoogleScholarGoogle Scholar |
Legarra A, Reverter A (2018) Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method. Genetics Selection Evolution 50, 53
| Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method.Crossref | GoogleScholarGoogle Scholar |
Liu J, Pogorzelski G, Neveu A, Legrand I, Pethick D, Ellies-Oury M-P, Hocquette J-F (2021) Are marbling and the prediction of beef eating quality affected by different grading sites? Frontiers in Veterinary Science 8, 611153
| Are marbling and the prediction of beef eating quality affected by different grading sites?Crossref | GoogleScholarGoogle Scholar |
Macedo FL, Reverter A, Legarra A (2020) Behavior of the linear regression method to estimate bias and accuracies with correct and incorrect genetic evaluation models. Journal of Dairy Science 103, 529–544.
| Behavior of the linear regression method to estimate bias and accuracies with correct and incorrect genetic evaluation models.Crossref | GoogleScholarGoogle Scholar |
Martín NP, Schreurs NM, Morris ST, López-Villalobos N, McDade J, Hickson RE (2022) Meat quality of beef-cross-dairy cattle from Angus or Hereford sires: a case study in a pasture-based system in New Zealand. Meat Science 190, 108840
| Meat quality of beef-cross-dairy cattle from Angus or Hereford sires: a case study in a pasture-based system in New Zealand.Crossref | GoogleScholarGoogle Scholar |
Mateescu RG, Garrick DJ, Garmyn AJ, VanOverbeke DL, Mafi GG, Reecy JM (2015) Genetic parameters for sensory traits in longissimus muscle and their associations with tenderness, marbling score, and intramuscular fat in Angus cattle. Journal of Animal Science 93, 21–27.
| Genetic parameters for sensory traits in longissimus muscle and their associations with tenderness, marbling score, and intramuscular fat in Angus cattle.Crossref | GoogleScholarGoogle Scholar |
McGilchrist P, Polkinghorne RJ, Ball AJ, Thompson JM (2019) The meat standards Australia index indicates beef carcass quality. Animal 13, 1750–1757.
| The meat standards Australia index indicates beef carcass quality.Crossref | GoogleScholarGoogle Scholar |
Meuwissen T, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185, 623–631.
| Accurate prediction of genetic values for complex traits by whole-genome resequencing.Crossref | GoogleScholarGoogle Scholar |
Misztal I, Legarra A (2017) Invited review: efficient computation strategies in genomic selection. Animal 11, 731–736.
| Invited review: efficient computation strategies in genomic selection.Crossref | GoogleScholarGoogle Scholar |
Pérez-Enciso M, Misztal I (2011) Qxpak.5: old mixed model solutions for new genomics problems. BMC Bioinformatics 12, 202
| Qxpak.5: old mixed model solutions for new genomics problems.Crossref | GoogleScholarGoogle Scholar |
Porto-Neto LR, Barendse W, Henshall JM, McWilliam SM, Lehnert SA, Reverter A (2015) Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection. Genetics Selection Evolution 47, 84
| Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection.Crossref | GoogleScholarGoogle Scholar |
Reverter A, Porto-Neto LR, Fortes MRS, McCulloch R, Lyons RE, Moore S, Nicol D, Henshall J, Lehnert SA (2016) Genomic analyses of tropical beef cattle fertility based on genotyping pools of Brahman cows with unknown pedigree. Journal of Animal Science 94, 4096–4108.
| Genomic analyses of tropical beef cattle fertility based on genotyping pools of Brahman cows with unknown pedigree.Crossref | GoogleScholarGoogle Scholar |
Reverter A, Hudson NJ, McWilliam S, Alexandre PA, Li Y, Barlow R, Welti N, Daetwyler H, Porto-Neto LR, Dominik S (2020) A low-density SNP genotyping panel for the accurate prediction of cattle breeds. Journal of Animal Science 98, skaa337
| A low-density SNP genotyping panel for the accurate prediction of cattle breeds.Crossref | GoogleScholarGoogle Scholar |
Reverter A, Hine BC, Porto-Neto L, Alexandre PA, Li Y, Duff CJ, Dominik S, Ingham AB (2021a) ImmuneDEX: updated genomic estimates of genetic parameters and breeding values for Australian Angus cattle. Animal Production Science 61, 1919–1924.
| ImmuneDEX: updated genomic estimates of genetic parameters and breeding values for Australian Angus cattle.Crossref | GoogleScholarGoogle Scholar |
Reverter A, Hine BC, Porto-Neto L, Li Y, Duff CJ, Dominik S, Ingham AB (2021b) ImmuneDEX: a strategy for the genetic improvement of immune competence in Australian Angus cattle. Journal of Animal Science 99, skaa384
| ImmuneDEX: a strategy for the genetic improvement of immune competence in Australian Angus cattle.Crossref | GoogleScholarGoogle Scholar |
Reverter A, Alexandre PA, Li Y, Hine BC, Duff CJ, Ingham AB, Porto-Neto LR (2022) Genomic prediction accuracy: how low can we go? In ‘Proceedings of the 12th world congress on genetics applied to livestock production, 3–8 July 2022, Rotterdam, The Netherlands’. (Wageningen Academic Publishers)
Ríos Utrera A, Van Vleck LD (2004) Heritability estimates for carcass traits of cattle: a review. Genetics and Molecular Research 30, 380–394.
Simeone R, Misztal I, Aguilar I, Legarra A (2011) Evaluation of the utility of diagonal elements of the genomic relationship matrix as a diagnostic tool to detect mislabelled genotyped animals in a broiler chicken population. Journal of Animal Breeding and Genetics 128, 386–393.
| Evaluation of the utility of diagonal elements of the genomic relationship matrix as a diagnostic tool to detect mislabelled genotyped animals in a broiler chicken population.Crossref | GoogleScholarGoogle Scholar |
Takeda M, Inoue K, Oyama H, Uchiyama K, Yoshinari K, Sasago N, Kojima T, Kashima M, Suzuki H, Kamata T, Kumagai M, Takasugi W, Aonuma T, Soma Y, Konno S, Saito T, Ishida M, Muraki E, Inoue Y, Takayama M, Nariai S, Hideshima R, Nakamura R, Nishikawa S, Kobayashi H, Shibata E, Yamamoto K, Yoshimura K, Matsuda H, Inoue T, Fujita A, Terayama S, Inoue K, Morita S, Nakashima R, Suezawa R, Hanamure T, Zoda A, Uemoto Y (2021) Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle. BMC Genomics 22, 799
| Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle.Crossref | GoogleScholarGoogle Scholar |
van Grevenhof IEM, van der Werf JHJ (2015) Design of reference populations for genomic selection in crossbreeding programs. Genetics Selection Evolution 47, 14
| Design of reference populations for genomic selection in crossbreeding programs.Crossref | GoogleScholarGoogle Scholar |
VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
| Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar |
Wientjes YCJ, Veerkamp RF, Calus MPL (2013) The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics 193, 621–631.
| The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction.Crossref | GoogleScholarGoogle Scholar |
Wientjes YCJ, Calus MPL, Goddard ME, Hayes BJ (2015) Impact of QTL properties on the accuracy of multi-breed genomic prediction. Genetics Selection Evolution 47, 42
| Impact of QTL properties on the accuracy of multi-breed genomic prediction.Crossref | GoogleScholarGoogle Scholar |
Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM (2013) Pitfalls of predicting complex traits from SNPs. Nature Reviews Genetics 14, 507–515.
| Pitfalls of predicting complex traits from SNPs.Crossref | GoogleScholarGoogle Scholar |
