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Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models
M Bermann1, I Aguilar2, A Alvarez Munera1
1Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602.
JDS Communications
|December 9, 2024
Summary
We developed a new algorithm to approximate the reliabilities of random-regression models (RRM) when including genomic information via single-step GBLUP. This method efficiently estimates breeding values for large dairy cattle populations.
Area of Science:
- Animal Breeding and Genetics
- Quantitative Genetics
- Statistical Genomics
Background:
- Random-regression models (RRM) are crucial for national genetic evaluations of longitudinal traits, providing breeding indices and their reliabilities.
- Calculating exact reliabilities requires inverting the coefficient matrix of mixed model equations (MME), which is computationally infeasible for large datasets.
- Approximating RRM reliabilities, especially with genomic information from single-step GBLUP, lacks extensive literature.
Purpose of the Study:
- To develop and validate an efficient algorithm for approximating the reliabilities of random-regression models incorporating genomic information using single-step GBLUP.
- To address the computational challenge of calculating exact reliabilities for large-scale genetic evaluations.
Main Methods:
- Developed a novel algorithm combining RRM reliabilities (without genomic data) and GBLUP reliabilities (effective record contributions).
- Applied the algorithm to a 3-lactation milk yield dataset from the Czech Republic, including 30 million test-day records, 2.5 million animals, and 54,000 genotyped animals.
- Validated the approximated reliabilities against those derived from the inversion of MME.
Main Results:
- Achieved a high correlation (0.98) between approximated and MME-derived reliabilities.
- Regression analysis showed a slope of 0.91 and an intercept of 0.02, indicating strong agreement.
- The approximation algorithm required only 21 minutes for the large Czech dataset, demonstrating significant computational efficiency.
Conclusions:
- The developed algorithm provides an accurate and computationally efficient method for approximating RRM reliabilities in large populations with genomic data.
- This approach facilitates more feasible and timely genetic evaluations, particularly for dairy cattle breeding programs.
- The method effectively integrates pedigree and genomic information for improved reliability estimation.
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