Genetic evaluation with uncertain parentage: a comparison of methods

Area of Science:

• Animal Breeding and Genetics
• Quantitative Genetics
• Statistical Genomics

Background:

• Best linear unbiased prediction with genetic groups (BLUP-G) is standard for genetic evaluation with unknown parents.
• BLUP-G unrealistically assumes unknown parents are unrelated to the population.
• This assumption can lead to biased genetic evaluations and reduced selection response.

Purpose of the Study:

• To develop and evaluate a genetic evaluation method that accounts for uncertain parentage.
• To compare the accuracy and efficiency of the new method against BLUP-G.
• To assess the impact on selection response and inbreeding.

Main Methods:

• Derived the correct numerator relationship matrix for uncertain parentage (Ā).
• Developed efficient algorithms to compute Ā and its inverse (Ā⁻¹).
• Compared BLUP-G with BLUP using Ā (BLUP-Ā1 and BLUP-Ā2) via computer simulation.

Main Results:

• BLUP-Ā1 and BLUP-Ā2 showed a 4-8% increase in genetic gain compared to BLUP-G by the fifth season.
• Selection using BLUP-Ā1 and BLUP-Ā2 resulted in higher inbreeding (17% and 12%) than BLUP-G.
• BLUP-Ā1 and BLUP-Ā2 provided unbiased estimates of selection response, unlike BLUP-G.

Conclusions:

• Accounting for uncertain parentage using BLUP-Ā significantly improves genetic evaluation accuracy.
• The BLUP-Ā method offers a more realistic and effective approach for genetic improvement in livestock.
• BLUP-Ā reduces bias in selection response estimates compared to traditional BLUP-G.