首页|Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework
Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework
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国家科技期刊平台
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Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework
Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SNP-set annotations.As an interpretable neural network,BANNs model SNP and SNP-set effects in their input and hidden layers,respectively.Furthermore,the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales.However,its application in genomic prediction has yet to be explored.Results This study extended the BANNs framework to the area of genomic selection and explored the optimal SNP-set partitioning strategies by using dairy cattle datasets.The SNP-sets were partitioned based on two strategies-gene annotations and 100 kb windows,denoted as BANN_gene and BANN_100kb,respectively.The BANNs model was compared with GBLUP,random forest(RF),BayesB and BayesCn through five replicates of five-fold cross-valida-tion using genotypic and phenotypic data on milk production traits,type traits,and one health trait of 6,558,6,210 and 5,962 Chinese Holsteins,respectively.Results showed that the BANNs framework achieves higher genomic pre-diction accuracy compared to GBLUP,RF and Bayesian methods.Specifically,the BANN_1 00kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP,RF,BayesB and BayesCπacross all traits.The average accuracy improvements of BANN_100kb over GBLUP,RF,BayesB and BayesCπ were 4.86％,3.95％,3.84％and 1.92％,and the accuracy of BANN_gene was improved by 3.75％,2.86％,2.73％and 0.85％compared to GBLUP,RF,BayesB and BayesCπ,respectively across all seven traits.Meanwhile,both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP,RF and Bayesian methods.Conclusion Our findings demonstrated that the BANNs framework performed better than traditional genomic pre-diction methods in our tested scenarios,and might serve as a promising alternative approach for genomic prediction in dairy cattle.
Xue Wang、Shaolei Shi、Yousuf Ali Khan、Zhe Zhang、Yi Zhang
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State Key Laboratory of Animal Biotech Breeding,National Engineering Laboratory for Animal Breeding,Key Laboratory of Animal Genetics,Breeding and Reproduction of Ministry of Agriculture and Rural Affairs,College of Ani-mal Science and Technology,China Agricultural University,Beijing 100193,China
Bangladesh Livestock Research Institute,Dhaka 1341,Bangladesh
Guangdong Laboratory of Lingnan Modern Agriculture,National Engineering Research Center for Breeding Swine Industry,Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding,College of Animal Science,South China Agricultural University,Guangzhou 510642,China
国家科技期刊平台
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