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Genetic parameters estimated with multitrait and linear spline-random regression models using Gelbvieh early growth
H Iwaisaki1, S Tsuruta, I Misztal
1Department of Agro-biology, Niigata University, Niigata 950-2181, Japan.
Journal of Animal Science
|March 9, 2005
Summary
A spline function model (SFM) provided more accurate genetic parameter estimates in beef cattle than a multitrait model (MTM). SFM offers superior modeling of age-related effects for improved breeding value predictions.
Area of Science:
- Animal Genetics
- Quantitative Genetics
- Beef Cattle Breeding
Background:
- Accurate estimation of genetic parameters is crucial for effective beef cattle breeding programs.
- Traditional multitrait models (MTM) may not fully capture age-related variations in growth traits.
- Random regression models offer a more flexible approach to modeling such variations.
Purpose of the Study:
- To compare genetic parameter estimates derived from a linear spline function model (SFM) with those from a multitrait model (MTM) in beef cattle.
- To evaluate the performance of SFM in accounting for age effects on growth traits.
- To assess the impact of different modeling approaches on direct and maternal heritability and genetic correlations.
Main Methods:
- Utilized weight data from 18,900 Gelbvieh calves for birth (BWT), weaning (WWT), and yearling (YWT) weights.
- Employed a three-trait maternal animal model for MTM analysis, including fixed regressions on age at recording.
- Applied a random regression model with a linear spline function (SFM) incorporating three knots (1, 205, 365 days) and direct permanent environmental effects.
Main Results:
- SFM and MTM showed good agreement for BWT due to lack of age variability.
- SFM estimates of variances for WWT and YWT tended to be lower than MTM estimates.
- SFM yielded lower direct and maternal heritability estimates and less negative genetic correlations compared to MTM.
- Direct heritability for YWT was 0.48 (SFM) vs. 0.59 (MTM); direct-maternal correlation for WWT was -0.33 (SFM) vs. -0.43 (MTM).
Conclusions:
- SFM may provide superior estimates of genetic parameters by better modeling age-related fixed and random effects.
- The improved modeling in SFM can lead to more accurate breeding value predictions in beef cattle.
- Further research should explore the application of SFM for optimizing genetic selection in beef cattle populations.