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Published on: August 16, 2017
Change in genetic correlation due to selection using animal model evaluation
I Strandén1, E A Mäntysaari, A Mäki-Tanila
1Department of Animal Breeding, Agricultural Research Centre, Jokioinen, Finland.
This study investigated how selection impacts genetic correlation between traits using simulation and analytical models. Results showed that while methods align initially, analytical predictions of genetic correlation change diverge from simulations over multiple selection rounds.
Area of Science:
- Quantitative Genetics
- Animal Breeding
- Statistical Genetics
Background:
- Understanding genetic correlation is crucial for multi-trait selection in breeding programs.
- Selection can alter the genetic architecture of traits, impacting breeding efficiency.
Purpose of the Study:
- To investigate the effect of selection on genetic correlation between two traits.
- To compare predictions from simulation and analytical models under different selection strategies.
Main Methods:
- Monte Carlo simulation of a nucleus breeding scheme with multi-trait animal model (AM) selection.
- Derivation of analytical formulae for predicting genetic (co)variance changes due to selection.
- Comparison of phenotypic selection and a recursive two-generation AM selection index.
Main Results:
- Simulation and analytical predictions of genetic correlation changes were similar initially.
- Phenotypic selection generally predicted greater changes in genetic correlation than the recursive AM method after one round.
- After multiple selection rounds, analytical formulae predicted more change than simulations.
Conclusions:
- Selection significantly impacts genetic correlation, with different methods yielding varying predictions.
- Analytical models provide valuable insights but may overestimate changes over extended selection periods compared to simulations.
- Accurate prediction of genetic correlation changes is vital for optimizing multi-trait breeding strategies.
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