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Published on: August 22, 2018
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Accuracy of single-step genomic selection for egg production in yellow-feathered broilers using a random regression
Shaoyan Jia1, Tianfei Liu1, Jie Ma1
1State Key Laboratory of Swine and Poultry Breeding Industry, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China.
Poultry Science
|February 15, 2026
Summary
Random regression models (RRM) combined with single-step genomic selection improve accuracy for poultry egg production. This approach enhances estimated breeding values more effectively than traditional methods.
Area of Science:
- Animal Genetics and Breeding
- Quantitative Genetics
- Poultry Science
Background:
- Egg production is a key economic trait in poultry, influenced by dynamic environmental and physiological factors.
- Conventional models (STM, MTM) have limitations in capturing continuous temporal patterns of egg production.
- Random regression models (RRM) offer a flexible approach for analyzing longitudinal data and time-dependent genetic effects.
Purpose of the Study:
- To compare the accuracy and unbiasedness of single-step genomic selection (ssGBLUP) combined with RRM against multi-trait models (MTM) and single-trait models (STM) for egg production.
- To evaluate the effectiveness of RRM in dissecting time-dependent genetic effects for improved breeding value prediction in yellow-feathered broilers.
Main Methods:
- Utilized a dataset of 168,596 egg production records from 18,221 yellow-feathered broilers.
- Applied RRM (Legendre polynomials), MTM (three laying phases), and STM (cumulative yield) for analysis.
- Integrated ssGBLUP with RRM, MTM, and STM to estimate breeding values.
Main Results:
- RRM with ssGBLUP significantly outperformed MTM and STM in accuracy and unbiasedness of estimated breeding values (EBVs).
- RRM improved EBV accuracy by up to 7% (pedigree) and 6.8% (genomic) over STM, and up to 3% (pedigree) and 2.9% (genomic) over MTM.
- Combining RRM with ssGBLUP enhanced EBV accuracy by up to 84% and unbiasedness by 26% compared to pedigree-based models.
Conclusions:
- The integration of RRM with ssGBLUP is a highly effective strategy for enhancing genomic selection programs in poultry.
- This approach provides more accurate and unbiased predictions for longitudinal production traits like egg number.
- RRM-ssGBLUP offers a promising avenue for accelerating genetic improvement in poultry breeding.
Keywords:
Genetic parameterGenomic selectionLongitudinal traitRandom regression modelYellow-feathered broilerMore Related Videos
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