Prediction of response with overlapping generations accounting for multistage selection

Area of Science:

• Quantitative genetics
• Animal breeding
• Statistical genetics

Background:

• Traditional selection models often simplify complex breeding schemes.
• Accurate prediction of genetic gain is crucial for efficient livestock improvement.

Purpose of the Study:

• To develop a generalized model for predicting genetic response to selection.
• To account for multi-stage selection processes in both sexes.
• To evaluate the impact of simplified selection models on prediction accuracy.

Main Methods:

• Generalized Hill's equations incorporating multi-stage selection.
• Modeling gene flow across selection stages.
• Application to a US dairy progeny testing scheme.

Main Results:

• The generalized method accurately incorporates genetic gains from later selection stages.
• Single-stage selection models overestimated asymptotic rates by 6% and cumulative response by 8% over 20 years.
• The developed method aligns with classical selection theory for single-cycle selection.

Conclusions:

• Multi-stage selection significantly impacts genetic gain predictions in livestock.
• Simplified selection models can lead to substantial overestimation of breeding program outcomes.
• Accurate modeling of selection stages is essential for optimizing dairy cattle breeding schemes.