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Variance decomposition in the estimation of genetic variance with selected data
1Department of Animal Breeding, Wageningen Agricultural University, The Netherlands.
Journal of Animal Science
|October 1, 1992
Summary
Estimating genetic variance in selected populations requires careful consideration of base animal assumptions. Treating selected base animals as fixed can lead to biased genetic variance estimates, impacting population genetic studies.
Area of Science:
- Quantitative Genetics
- Animal Breeding
- Population Genetics
Background:
- Accurate estimation of genetic variance is crucial for animal breeding and genetic improvement programs.
- Assumptions regarding base animals (founding populations) significantly influence genetic variance estimations in selected populations.
- Current understanding of how different assumptions about base animals affect genetic variance estimation in selected populations remains incomplete.
Purpose of the Study:
- To investigate the consequences of different assumptions about base animals on the estimation of genetic variance in selected populations.
- To quantify the differences between models that treat base animals in various ways, particularly focusing on selected base animals.
- To demonstrate how selection can be incorporated into a complete genetic model and identify reasons for biased variance component estimation.
Main Methods:
- Development of variance decomposition methods for simple experimental designs.
- Construction of independent contrasts to compare different modeling approaches.
- Application of Restricted Maximum Likelihood (REML) to estimate variance components under various designs and selection rules.
Main Results:
- Models treating selected base animals as fixed can lead to biased estimates of genetic variance.
- Variance decomposition effectively quantifies discrepancies between models with differing base animal assumptions.
- The proposed method accurately accounts for selection within a comprehensive genetic model.
Conclusions:
- Assumptions made about base animals, especially when treated as fixed, have significant consequences for genetic variance estimation in selected populations.
- Failure to properly account for selection in base animals can introduce bias into variance component estimates.
- The presented methodology provides a framework for more accurate genetic variance estimation by correctly modeling selection.