Related Experiment Videos
Comparison of methods of estimating variance components in pigs
J W Keele1, T E Long, R K Johnson
1U.S. Department of Agriculture, Agricultural Research Service, Clay Center, NE 68933-0166.
Journal of Animal Science
|April 1, 1991
Summary
The Pseudo Expectation Approach (PE) provides more precise estimates of genetic parameters for swine traits like litter size and backfat compared to other methods. This improved accuracy in estimating genetic variance can lead to significant gains in animal breeding programs.
Area of Science:
- Animal Genetics
- Quantitative Genetics
- Swine Breeding
Background:
- Accurate estimation of genetic parameters is crucial for effective animal breeding programs.
- Traditional methods for estimating variance components may not fully utilize all available pedigree information.
- The Nebraska Gene Pool swine population provides a valuable dataset for genetic parameter estimation.
Purpose of the Study:
- To evaluate the Pseudo Expectation Approach (PE) for estimating genetic and common environmental variance components.
- To compare the precision of PE with nested ANOVA and offspring-on-parent regression (REGOP) methods.
- To assess the accuracy of genetic parameter estimates for litter size (LS), backfat (BF), and average daily gain (ADG) in swine.
Main Methods:
- Variance components (sigma 2g, sigma 2c, sigma 2e) were estimated using the Pseudo Expectation Approach (PE).
- Data from the Nebraska Gene Pool swine population (1967-1986) on LS, BF, and ADG were utilized.
- Simulations of 200 repetitions were conducted to compare mean square errors (MSE) of PE, nested ANOVA, and REGOP.
Main Results:
- PE provided heritability (h2) estimates: LS (0.18 ± 0.06), BF (0.56 ± 0.06), ADG (0.16 ± 0.05).
- PE provided common environmental effects (c2) estimates: LS (0.01 ± 0.03), BF (0.09 ± 0.02), ADG (0.19 ± 0.03).
- PE showed smaller MSE than REGOP for BF and ADG, and smaller MSE than nested ANOVA for all traits, indicating higher precision.
Conclusions:
- The Pseudo Expectation Approach (PE) offers superior precision for estimating genetic parameters in swine compared to REGOP and nested ANOVA.
- Accounting for all genetic relationships, rather than subsets, significantly enhances the precision of genetic parameter estimation.
- Increased precision in estimating genetic parameters can lead to substantial improvements in selection efficiency and genetic gains in swine breeding.