Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitionin...Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.展开更多
基金supported by the Biological Breeding-Major Projects in National Science and Technology(No.2023ZD0404405)the Earmarked Fund for China Agriculture Research System(No.CARS-pig-35)+2 种基金the National Natural Science Foundation of China(No.3227284,32302708)the 2115 Talent Development Program of China Agricultural University,the Chinese Universities Scientific Fund(No.2023TC196)the Seed Industry Revitalization Action Project of Guangdong Province(No.2024-XPY-06-001)。
文摘Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.
