Deregressing estimated breeding values and weighting information for genomic regression analyses

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Genomic Selection

Background:

• Genomic prediction uses training analyses to estimate the impact of genomic regions on traits.
• Data sources for genomic prediction include phenotypes, family records, and estimated breeding values (EBV).
• Inconsistent methods exist for using EBV and deregressed data, particularly regarding weighting for heterogeneous variance.

Purpose of the Study:

• To introduce a logical approach for using information in genomic prediction.
• To demonstrate appropriate weighting methods for heterogeneous variance.
• To clarify the process of deregressing and weighting EBV.

Main Methods:

• Introduced a logical approach for genomic prediction data analysis.
• Developed methods for appropriate weighting of observations with heterogeneous variance.
• Explained the process of removing parent average effects, deregressing, and weighting EBV.

Main Results:

• Proposed EBV/r² (where EBV excludes parent information and r² is reliability) as the appropriate deregression method.
• Identified the optimal weighting ratio as (1 - h²)/[(c + (1 - r²)/r²)h²] for deregressed breeding values.
• Demonstrated that reliability or prediction error variance are not the appropriate weights.

Conclusions:

• Phenotypic data and deregressed data can be effectively combined in genomic analyses.
• Appropriate weighting is crucial for integrating diverse data sources in genomic evaluations.
• The proposed methods enhance the accuracy of genomic prediction models.