Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error...

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Statistical Genetics

Background:

• Comparability of breeding values across contemporary groups is crucial in genetic evaluation.
• Genetic connectedness measures are essential but computationally intensive, often relying on the prediction error variance-covariance matrix.
• Existing alternative statistics for genetic connectedness may be inappropriate in certain scenarios.

Purpose of the Study:

• To develop a computationally efficient method for calculating genetic connectedness.
• To provide an accurate measure of the prediction error variance-covariance matrix for genetic evaluations.

Main Methods:

• A correction to the variance-covariance matrix of estimated contemporary group fixed effects was derived.
• The method was demonstrated for univariate models with single or multiple fixed effects and one random effect.

Main Results:

• The corrected variance-covariance matrix of fixed effects accurately yields the prediction error variance-covariance matrix averaged by contemporary group.
• Approximations based solely on the variance-covariance matrix of fixed effects were shown to be inappropriate.

Conclusions:

• A computationally feasible method for calculating genetic connectedness based on the prediction error variance-covariance matrix was established.
• The proposed method significantly reduces computational requirements by focusing on the smaller variance-covariance matrix of fixed effects.