Integrating genomics and multi trait model to improve body weight and egg production in yellow-feathered broiler breeders

Juntu Lan; Shaoyan Jia; Zhirong Cai; Jianbo Li; Pengchong Wan; Dingming Shu; Tianfei Liu; Hao Qu; Juntu Lan; Shaoyan Jia; Zhirong Cai; Jianbo Li; Pengchong Wan; Dingming Shu; Tianfei Liu; Hao Qu

doi:10.3934/aas.2025007

AIMS Animal Science

2025, Volume 1, Issue 1: 149-161. doi: 10.3934/aas.2025007

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Research article

Integrating genomics and multi trait model to improve body weight and egg production in yellow-feathered broiler breeders

1.
State 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
2.
State Key Laboratory of Swine and Poultry Breeding Industry, Agro-Biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
3.
Guangdong Wiz Agricultural Science & Technology Co. Ltd, Guangzhou, 510640, China

Received: 28 September 2025 Revised: 08 November 2025 Accepted: 19 November 2025 Published: 11 December 2025

Body weight and egg production are crucial economic traits in broiler breeding; yet, their negative genetic correlation poses challenges for simultaneous genetic improvement. In this study, we estimated the genetic parameters of these traits in yellow-feathered broiler breeders using a multi-trait model and evaluated the predictive performance of single-step genomic BLUP (ssGBLUP) and pedigree-based BLUP (PBLUP) in both single- and multi-trait analyses. Phenotypic data included cumulative egg production up to 43 weeks and body weight at 8 weeks from 4,712 hens, with pedigrees tracing three generations. Genomic data derived from low-coverage sequencing yielded 315K SNPs. Genetic evaluations were conducted using PBLUP and ssGBLUP models, with prediction accuracy assessed via five-fold cross-validation based on estimated breeding value (EBV) accuracy, rank correlation, and unbiasedness. Heritability estimates were 0.285 for body weight and 0.396 for egg production, while genetic correlation analysis revealed a stable, moderate negative correlation between the traits (approximately -0.30, P < 0.01) across pedigree and genomic data. Compared to PBLUP, ssGBLUP significantly enhanced prediction accuracy for egg production by 19.88% (single-trait) and 21.18% (multi-trait), and for body weight by 17.18% and 18.90%, respectively (P < 0.01). These results demonstrated that ssGBLUP, particularly when integrated with a multi-trait model, more effectively leverages genomic information and trait correlations, offering a superior strategy for balanced genetic improvement of antagonistic traits in broilers.
- antagonistic traits,
- genomic selection,
- accuracy,
- negative genetic correlation
Citation: Juntu Lan, Shaoyan Jia, Zhirong Cai, Jianbo Li, Pengchong Wan, Dingming Shu, Tianfei Liu, Hao Qu. Integrating genomics and multi trait model to improve body weight and egg production in yellow-feathered broiler breeders[J]. AIMS Animal Science, 2025, 1(1): 149-161. doi: 10.3934/aas.2025007

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Abstract

Body weight and egg production are crucial economic traits in broiler breeding; yet, their negative genetic correlation poses challenges for simultaneous genetic improvement. In this study, we estimated the genetic parameters of these traits in yellow-feathered broiler breeders using a multi-trait model and evaluated the predictive performance of single-step genomic BLUP (ssGBLUP) and pedigree-based BLUP (PBLUP) in both single- and multi-trait analyses. Phenotypic data included cumulative egg production up to 43 weeks and body weight at 8 weeks from 4,712 hens, with pedigrees tracing three generations. Genomic data derived from low-coverage sequencing yielded 315K SNPs. Genetic evaluations were conducted using PBLUP and ssGBLUP models, with prediction accuracy assessed via five-fold cross-validation based on estimated breeding value (EBV) accuracy, rank correlation, and unbiasedness. Heritability estimates were 0.285 for body weight and 0.396 for egg production, while genetic correlation analysis revealed a stable, moderate negative correlation between the traits (approximately -0.30, P < 0.01) across pedigree and genomic data. Compared to PBLUP, ssGBLUP significantly enhanced prediction accuracy for egg production by 19.88% (single-trait) and 21.18% (multi-trait), and for body weight by 17.18% and 18.90%, respectively (P < 0.01). These results demonstrated that ssGBLUP, particularly when integrated with a multi-trait model, more effectively leverages genomic information and trait correlations, offering a superior strategy for balanced genetic improvement of antagonistic traits in broilers.

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