Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework  

作　　者：Xue Wang Shaolei Shi Md.Yousuf Ali Khan Zhe Zhang Yi Zhang 

机构地区：[1]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 Animal Science and Technology,China Agricultural University,Beijing 100193,China [2]Bangladesh Livestock Research Institute,Dhaka 1341,Bangladesh [3]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

出　　处：《Journal of Animal Science and Biotechnology》2024年第6期2216-2228,共13页畜牧与生物技术杂志(英文版)

基　　金：supported by the National Key Research and Development Program of China(2022YFD1302204);the earmarked fund CARS36;Ningxia Key Research and Development Program of China(2023BCF01004;2019NYYZ09)。

摘　　要：Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-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 SN P-set partitioning strategies by using dairy cattle datasets.The SN P-sets were partitioned based on two strategiesgene annotations and 100 kb windows,denoted as BANN_gene and BANN_100kb,respectively.The BANNs model was compared with GBLU P,random forest(RF),BayesB and BayesCπthrough five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits,type traits,and one health trait of 6,558,6,210and 5,962 Chinese Holsteins,respectively.Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLU P,RF and Bayesian methods.Specifically,the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP,RF,BayesB and BayesCrr across all traits.The average accuracy improvements of BANN_100kb over GBLU P,RF,BayesB and BayesCrr were 4.86%,3.95%,3.84%and 1.92%,and the accuracy of BANN_gene was improved by3.75%,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 prediction methods in our tested scenarios,and might serve as a promising alternative approach for genomic pred

关 键 词：Biologically annotated neural networks Dairy cattle Genomic prediction 

分 类 号：S823.2[农业科学—畜牧学]