Investigation of methods for inclusion of systematic environmental effects in weaning and post-weaning weights for meat sheep in large-scale genetic evaluation
U. Paneru
A B * , D. J. Brown C , P. M. Gurman C and J. H. J. van der Werf A
A School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
B Nepal Agricultural Research Council, Singhadurbar Plaza, Kathmandu, Bagmati 44600, Nepal.
C Animal Genetics and Breeding Unit (AGBU is a joint venture of NSW Department of Primary Industries and the University of New England), University of New England, Armidale, NSW 2351, Australia.
Handling Editor: Suzanne Mortimer
Animal Production Science 63(1) 41-50 https://doi.org/10.1071/AN21300
Submitted: 4 June 2021 Accepted: 15 June 2022 Published: 15 July 2022
© 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: Genetic evaluation of Australian sheep is conducted for millions of animals for more than 100 traits. Currently, the Australian sheep genetic-analysis software (OVIS) applies a pre-adjustment of phenotype for fixed effects rather than fitting all fixed and random effects jointly in a linear mixed model to estimate breeding values. However, the current correction factors might be outdated and potential interactions among fixed effects not accounted for, which could lead to bias in estimated breeding values (EBVs).
Aims: This study aimed to assess whether correction factors used in OVIS for early bodyweights recorded in meat sheep breeds are appropriate, so as to explore whether the pre-adjustment method is still suitable and how this compares with a linear mixed model, and to estimate the significance of interactions between fixed effects.
Methods: Correlations between EBVs from different models and regression slopes from forward prediction were calculated, using weaning-weight data on 365 956 White Suffolk and 370 649 Poll Dorset sheep and post-weaning weight data on 292 538 White Suffolk and 303 864 Poll Dorset sheep.
Key results: The current OVIS procedure resulted in regression slopes of progeny performance on sire EBVs (averaged over breeds) of 0.37 and 0.35 for weaning and post-weaning weights respectively. Updated pre-adjustment factors improved the regression slopes to 0.40 and 0.38 respectively. Analysis with a linear mixed model produced significantly better regression slopes than did pre-adjustment (0.47 and 0.44 respectively). Further, regression slopes obtained from the linear mixed model with flock by sex by age interaction averaged over breeds were 0.48 for weaning and 0.46 for post-weaning weight respectively, which was a moderate improvement over the current OVIS model. Including a flock by sex by age interaction produced significantly better improvement in Poll Dorset sheep and modest improvement in White Suffolk sheep than did linear mixed model without interaction.
Conclusions: Using a linear mixed model with a flock by sex by age interaction significantly improves the utility of estimated breeding values for weaning and post-weaning weight in predicting the performance of future progeny.
Implications: To account for systematic environmental effects, a linear mixed model should be used in OVIS to jointly estimate the fixed effects and EBVs.
Keywords: accuracy, bias, estimated breeding values, interaction, linear mixed model, phenotype, pre-adjustment, regression slope.
References
Albuquerque LG, Meyer K (2001) Estimates of covariance functions for growth from birth to 630 days of age in Nelore cattle. Journal of Animal Science 79, 2776–2789.| Estimates of covariance functions for growth from birth to 630 days of age in Nelore cattle.Crossref | GoogleScholarGoogle Scholar | 11768105PubMed |
Ali M, Mohammad F (2017) Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances. Genetika 49, 469–482.
| Genetic evaluation of growth traits in Iranian Kordi Sheep using random regression model with homogeneous and heterogeneous residual variances.Crossref | GoogleScholarGoogle Scholar |
Brown DJ, Tier B, Reverter A, Banks R, Graser HU (2000) OVIS: a multiple trait breeding value estimation program for genetic evaluation of sheep. Wool Technology and Sheep Breeding 48, 285–297.
Brown DJ, Huisman AE, Swan AA, Graser H-U, Woolaston RR, Ball AJ, Atkins KD, Banks RG (2007) Genetic evaluation for the Australian sheep industry. In ‘The proceedings of the association for the advancement of animal breeding and genetics’. (University of New England: Armidale, NSW, Australia)
Brown D, Swan A, Johnston D, Graser H (2009) Sire by flock year interactions for body weight in Poll Dorset sheep. In ‘The association for the advancement of animal breeding and genetics, South Australia’. (Association for the Advancement of Animal Breeding and Genetics: Brisbane, Qld, Australia)
Brown DJ, Swan AA, Gill JS, Ball AJ, Banks RG (2016) Genetic parameters for liveweight, wool and worm resistance traits in multi-breed Australian meat sheep. 1. Description of traits, fixed effects, variance components and their ratios. Animal Production Science 56, 1442–1448.
| Genetic parameters for liveweight, wool and worm resistance traits in multi-breed Australian meat sheep. 1. Description of traits, fixed effects, variance components and their ratios.Crossref | GoogleScholarGoogle Scholar |
Gilmour AR, Gogel BJ, Cullis BR, Welham SJ, Thompson R (2015) ‘ASReml user guide 4.1 functional specification.’ (VSN International Ltd: Hemel Hempstead, UK)
Henderson CR (1982) Analysis of covariance in the mixed model: higher-level, nonhomogeneous, and random regressions. Biometrics 38, 623–640.
| Analysis of covariance in the mixed model: higher-level, nonhomogeneous, and random regressions.Crossref | GoogleScholarGoogle Scholar | 7171692PubMed |
Henderson CR (1984) ‘Application of linear models in animal breeding.’ (University of Guelph: Guelph, ON, Canada)
Huisman AE, Brown DJ, Fogarty NM (2015) Ability of sire breeding values to predict progeny bodyweight, fat and muscle using various transformations across environments in terminal sire sheep breeds. Animal Production Science 56, 95–101.
| Ability of sire breeding values to predict progeny bodyweight, fat and muscle using various transformations across environments in terminal sire sheep breeds.Crossref | GoogleScholarGoogle Scholar |
Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38, 963–974.
| Random-effects models for longitudinal data.Crossref | GoogleScholarGoogle Scholar | 7168798PubMed |
Meyer K (1998) Estimating covariance functions for longitudinal data using a random regression model. Genetics Selection Evolution 30, 221
| Estimating covariance functions for longitudinal data using a random regression model.Crossref | GoogleScholarGoogle Scholar |
Meyer K (2004) Scope for a random regression model in genetic evaluation of beef cattle for growth. Livestock Production Science 86, 69–83.
| Scope for a random regression model in genetic evaluation of beef cattle for growth.Crossref | GoogleScholarGoogle Scholar |
Notter DR, Brown DJ (2015) Effects of birth-rearing type on weaning weights in meat sheep are systematically associated with differences in mean performance among flocks. Genetics Selection Evolution 47, 57
| Effects of birth-rearing type on weaning weights in meat sheep are systematically associated with differences in mean performance among flocks.Crossref | GoogleScholarGoogle Scholar |
Paneru U, Brown DJ, Moghaddar N, Van der Werf JHJ (2021) Investigation of methods for inclusion of fixed effects for ultrasound scan carcasss traits in large scale sheep genetic evaluation. In ‘The association for the advancement of animal breeding and genetics.’ (University of Adelaide: Adelaide, SA, Australia)
Raymond CA (1982) ‘Systematic environmental effects on beef cattle growth (Master).’ (University of New England)
Safari E, Fogarty NM, Gilmour AR (2005) A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livestock Production Science 92, 271–289.
| A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep.Crossref | GoogleScholarGoogle Scholar |
