12 minute read
A strong showing of Angus at AAABG
Matt Reynolds, Breed Development Officer
For the first time in the conferences long history, the 24th Association for the Advancement of Animal Breeding and Genetics (AAABG) conference went online.
The conference is widely considered the premier livestock breeding conference in Australia and New Zealand and provides an opportunity for researchers to showcase the latest livestock genetics research being conducted, with presentation detailing research in a number of animal industries, most notably beef cattle, dairy cattle and sheep. The event showcased a number of the research and development activities being undertaken at Angus Australia, and the strong collaborative links that Angus Australia has developed to continue to deliver value to members. A key achievement of the 2021 conferences was the awarding of the ‘Editor’s choice: Best Special APS Issue paper’ award to CSIRO’s Brad Hine and his paper ‘Development Of Angus Steerselect - A Genomic Based Tool To Identify Performance Differences Of Australian Angus Steers During Feedlot Finishing: Phase 1 Validation’. A paper which explores the outcomes of a collaborative project between Angus Australia and CSIRO.
The Impact Of Reference Composition And Genome Build On The Accuracy Of Genotype Imputation In Australian Angus Cattle
H. Aliloo1, S.A Clark1 1 School of Environmental and Rural Science, University of New England, Armidale, NSW 2350, Australia Genotype imputation is a statistical method to obtain a large quantity of DNA-based information at a low cost. Our proposed method improves the accuracy of imputed genotypes, which is of crucial importance for their utility. The presented method is straightforward and can be implemented at no extra cost to aid in genetic improvement of beef cattle.
Assessment Of Genomic Predictions For Feedlot And Carcase Traits In Australian Angus Steers
P.A. Alexandre1 , Y. Li1 , B.C. Hine2 , C.J. Duff3 , A.B. Ingham1 , L.R. Porto-Neto1 and A. Reverter1 1CSIRO, Agriculture and Food, Queensland Bioscience Precinct, St Lucia, QLD, Australia, 2 CSIRO, Agriculture and Food, F.D. McMaster Laboratory, Armidale, NSW, Australia, 3 Angus Australia, Armidale, NSW, Australia. Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry globally. Here we used data from 3,408 Australian Angus steers from seven birth cohorts (2011 to 2017) with genotypes for 45,152 SNPs. We report genetic parameter estimates and accuracies of genomic estimated breeding values (GEBV) for feedlot and carcase traits, namely feedlot average daily gain (ADG), carcase weight (CWT) and carcase Meat Standard Australia marbling score (MBL). Prediction accuracies were estimated based on traditional method as well as method LR. The average prediction accuracies across cohorts assessed with the traditional method were 0.28 (ADG), 0.49 (CWT) and 0.50 (MBL), while method LR accuracies were 0.47 (ADG), 0.64 (CWT) and 0.59 (MBL). We found a strong correlation (0.74, P-value<0.001) between traditional accuracies and method LR accuracies. Heritability estimates were moderate to large (0.29 for ADG, 0.53 for CWT and 0.41 for MBL). The metrics of GEBV quality and heritabilities reported here suggest good potential for accurate genomic selection of Australian Angus for feedlot performance and carcase characteristics.
Ultra-Small Snp Panels To Uniquely Identify Individuals In Thousands Of Samples
S. Dominik1, C.J Duff2, A.I Byrne2, H. Daetyler3 , 4, A. Reverter5 1 CSIRO Agriculture and Food, FD McMasters Laboratories, 9308 New England Highway, Armidale, NSW 2350, Australia, 2 Angus Australia, 86 Glen Innes Road, Armidale, NSW, 2350, Australia, 3 Agriculture Victoria, AgriBio Centre, 5 Ring Road, Bundoora, Vic. 3083, Australia, 4 La Trobe University, Plenty Road and Kingsbury Drive, Bundoora, Vic. 3083, Australia, 5 CSIRO Agriculture and Food, Queensland Bioprecinct, 306 Carmody Road, St Lucia, Qld 4067, Australia. Genomic information can be used for traceability of meat products. The present study explored the required number of genetic markers to generate unique marker profiles for each animal in the dataset. Ultra-small panels of genetic markers can provide an efficient method for the large-scale task of industrywide paddock to plate traceability.
C.J. Duff1, B.J. Crook2, A.I. Byrne1 and M.J. Reynolds1 1 Angus Australia, 86 Glen Innes Road, Armidale, New South Wales, Australia, and 2 Agricultural Business Research Institute, University of New England, 2351, Armidale, Australia A common question from Angus seedstock producers is “what is the value of live-animal ultrasound scanning of breeding candidates for carcase traits, particularly young bulls, if they are already genomic tested for genetic evaluation and underpinned by a reference population with carcase data”. To help answer this question, 3 ultrasound scan phenotyping scenarios were analysed through the Trans-Tasman Angus Cattle Evaluation (TACE) to produce and compare the subsequent eye muscle area (EMA), intramuscular fat (IMF), rib fat (RIB) and rump fat (RUMP) Estimated Breeding Values (EBVs) and their accuracies. This study shows that ultrasound scanning of genotyped bulls does provide some “value” for breeding programs in terms of increasing accuracy to carcase EBVs across all traits and scenarios. However, the value differs by trait (e.g. more influence on EMA EBV compared to IMF EBV) and by scenario (e.g. more influence from heifer scans, particularly on IMF, RIB and RUMP EBVs, compared to bull scans, because of the differences in genetic parameters for the bull and heifer ultrasound scan traits). Further work is required to understand at a herd and population level the impact of a reduction in ultrasound scan phenotyping, particularly on genotyped bulls, coupled with an increasing number of direct carcase phenotypes in the Angus Australia genomics reference population. Redefining residual feed intake to account for marbling fat in beef breeding programs.
C.J Duff1, J.H.J van der Werf2, P.F Parnell1, S.A Clark2 1 Angus Australia, 86 Glen Innes Rd, Armidale, NSW, 2350, Australia, 2 School of Environmental and Rural Science, University of New England, Armidale, NSW, 2351 Australia Improving meat quality and feed efficiency, which is related to input costs and environmental concerns, are important breeding objectives for many beef breeding herds. Different definitions of feed efficiency were estimated and compared with meat quality traits on over 4000 Angus animals. The study confirmed the challenges with selecting for both feed efficiency and meat quality traits as they are generally antagonist at the genetic level, and investigation in alternative approaches for beef cattle selection is warranted.
B.C Hine1, C.J Duff3, A.I Byrne3, P.F Parnell3, L.R Porto-Neto2, A.B Ingham2, A. Reverter2 1 CSIRO, Agriculture and Food, F.D. McMaster Laboratory, Armidale, NSW, Australia, 2 CSIRO, Agriculture and Food, Queensland Bioscience Precinct, St Lucia, QLD, Australia, 3 Angus Australia, Armidale, NSW, Australia. Genomic-based technologies are allowing commercial beef producers to predict the genetic merit of individual animals of unknown pedigree with increased ease and accuracy. We report here that the genomic product, Angus SteerSELECT, can predict differences in carcass weight, marbling score, ossification score and carcass value in both short-fed (100 days) and long-fed (270 days) Australian Angus steers. Genomic selection tools that can predict differences in performance of feedlot cattle have the potential to significantly increase profitability for the beef supply chain.
Macro- And Micro-Genetic Environmental Sensitivity For 400-Day Weight In Australian Angus
M.D. Madsen1, J.H.J. van der Werf1, V. Börner2, S. Clark1 1 School of Environmental and Rural Science, University of New England, 2 Animal Genetics and Breeding Unit, University of New England Genotype by environment interactions can be caused by both macro- and micro-genetic environmental sensitivity (GES). In the current study, 400 day weight (400DW) measured on Australian Angus was analysed using a variability model and a reaction norm model to obtain estimates for genetic variation due to macro- and micro-GES. The results showed additive genetic variance for both macro- and micro-GES. Over the range of contemporary group means the macroGES impacted the genetic variance and ranking of sires across environments. The presence of microGES indicated the possibility of selecting to reduce the variability of phenotypes, but further investigation into the consequences is needed.
J.E. Peart1, A.I. Byrne1 , M.J. Reynolds1 and C.J. Duff1 1 Angus Australia, Armidale, NSW, Australia This study, through the method of quantitative survey, investigates bull selection criteria preferences and understanding of genetic technologies of Australian beef producers and breed utilisation within their operation. The survey captured 1,023 producer responses from a representative proportion of beef cattle businesses in each state. Participants were asked to value bull selection criteria preferences on a 1 (lowest value) to 10 (highest value) scale. Respondents were also asked to rate their knowledge of genetics and nominate their breed of choice utilized in their operations. Nationally, temperament was ranked the most valued bull selection criteria, followed by polledness, visual appraisal and BullCHECK. The results were relatively consistent between states. Angus was the dominant breed in the female breeding population, with 5.6 million head (48%) of the Australian breeding female herd influenced by Angus genetics. Members of breed societies, particularly Angus Australia members, rated their knowledge of genetics more highly than their non-member counterparts.
Indexes Supporting Genomic Tools For Selecting Commercial Angus Heifer Replacements And Identifying Steers For Long-Fed Programmes In Australia
C.D. Quinton1, J.A. Archer1 , 2, P.R. Amer1, S. Harburg1, G. Petersen1, A. Byrne3, C. Duff3 and P. Parnell3 1 AbacusBio Limited, Dunedin 9016, New Zealand, 2 Current address: Beef + Lamb New Zealand Genetics, Dunedin 9054, New Zealand, 3 Angus Australia, Armidale, NSW 2350, Australia Angus Australia, in collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), have developed new genomic tools for early life evaluation of commercial straightbred Angus heifers and steers. To aid producers to make optimal multi-trait selection decisions, two new commercial economic indexes have been developed. These indexes are based on economic value models for core GEBVs calculated with the new genomic products. The heifer index is designed to aid selection of replacement heifers in commercial herds and is based on costs and revenues from cows and their offspring in Australian short/mid-fed and long-fed production systems. This index contains maternal (birth weight, weaning weight, milk, mature cow weight) and terminal (post-wean growth, feedlot growth and intake, rib fat and marbling) traits. Nonlinear functions are applied to value birth weight as it relates to calving ease, milk, and marbling. This index should identify more efficient heifers with genetic potential to produce progeny with improved post-wean growth, feed efficiency and carcase merit. The long-fed steer index is designed to identify steers best suited to Australian long-fed production systems. This index focuses on feedlot growth and intake, and carcase traits rib fat and marbling. This index should identify efficient steers with high marbling.
M.J Reynolds1, C.J Duff1, P.F Parnell1 and A.I Byrne1 1 Angus Australia, 86 Glen Innes Rd, Armidale, NSW, 2350, Australia Estimated Breeding Values (EBVs) form a key component of modern cattle breeding programs and are the foundation for genetic improvement within the Angus breed in Australia. Demonstrating the ability of EBVs to predict differences in progeny performance in a practical, real world scenario is seen as vital to ensure the continued growth in industry acceptance of EBVs. This work explores the ability of EBVs predicted differences in progeny performance of sires entered in cohorts 5, 6 and 7 of the Angus Sire Benchmarking Program (ASBP) by comparing the expected differences in progeny performance based on EBVs with the observed differences in average progeny performance. The study demonstrated that EBVs predicted differences in the breeding value of sires in the ASBP for birth, growth and carcase traits, and reinforces the merits of focussed adoption strategies pertaining to EBVs within the Angus genetic improvement pipeline.
Immunedex: Updated Genomic Estimates Of Genetic Parameters And Breeding Values For Australian Angus Cattle
A. Reverter1, B.C Hine2, L.R Porto-Neto1, P.A Alexandre1, Y. Li, C.J Duff3, S. Dominik2, A.B Ingham1 1 CSIRO, Agriculture and Food, Queensland Bioscience Precinct, St Lucia, QLD, Australia, 2 CSIRO, Agriculture and Food, F.D. McMaster Laboratory, Armidale, NSW, Australia, 3 Angus Australia, Armidale, NSW, Australia. Immune competence phenotypes are moderately heritable and accurate genomic estimated breeding values can be generated for immune competence to allow for selection of cattle with an improved ability to mount an immune response. Our analyses suggest that ImmuneDEX will provide a tool to underpin long-term genetic strategies aimed at improving the immune competence of animals in production systems which in turn is expected to reduce the incidence of disease and our reliance on antibiotics to treat disease.
Determination Of Optimum Genomic Weights For Single Step Genetic Evaluation Via Genetic Variance Partitioning
J.A. Torres-Vázquez1, A.M. Samaraweera1, M G. Jeyaruban1, D.J. Johnston1, and V. Boerner1 1 Animal Genetics Breeding Unit∗ , University of New England, Armidale, NSW, 2351 Australia It is important in single-step genetic evaluations to use appropriate lambdas (λ) for calculating weighted average of NRM (numerator relationship matrix) and GRM (genomic relationship matrix) in joint relationship matrix. λ is usually estimated using a single-trait cross-validation procedure. However, it can be shown that a univariate single-step model applying a scalar λ is simply a condensed form of an extended model containing two genetic factors, factor ����~����(0, ����) and factor ����~����(0, ����), where the partitioning of the total genetic variance reflects λ. For multivariate singlestep genetic evaluation, this model condensation implies that all involved genetic variances may yield the same λ, which is highly unlikely. Hence, it is required to estimate λ by accounting for its heterogeneity using the extended model for variance component estimation. This study used an extended single-step model to estimate variances and λs for calving difficulty (CD), gestation length (GL), and birth weight (BW) using Australian Angus data. A total of 129,851 animals with 45,575 genotypes were analysed. Initial variances obtained from a pedigree-only model were then used as starting values for the extended single-step model assigning 90% of the genetic variance to factor ���� and 10% to factor ����. Since CD is a categorical trait with three categories, a threshold model-Gibbs sampling method was used to estimate variances. Heritability estimates for the extended single-step model were very similar to those from the pedigree only model implying that the single-step model was not explaining more variation in the data than the pedigree only model. For CD, GL, and BW, the total heritability estimates were 0.39 ± 0.04, 0.68 ± 0.02, and 0.44 ± 0.01, respectively. For the same traits, the total maternal heritability estimates were 0.17 ± 0.02, 0.11 ± 0.01, and 0.09 ± 0.01, respectively. In contrast, to the Gibbs sampling starting values, the genetic variance was partitioned between ���� and ���� such that direct genetic λ estimates for CD, GL, and BW were 0.36 ± 0.05, 0.62 ± 0.03, 0.75 ± 0.03, respectively. Maternal genetic λ estimates ranged from 0.01 ± 0.01 (for BW) to 0.05 ± 0.01 (for CD). The results imply that λ values are heterogeneous in multivariate single-step genomic evaluation. Further studies are needed to investigate the consequences of using heterogenous λ values for direct genetic and maternal genetic components in multivariate singlestep evaluation in terms of model dimensions, solver convergence rate, and model forward predictive ability. A full copy of the conference proceedings can be found by visiting the conference website (www.aaabg. org) or for further information contact staff at Angus Australia.
