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

ABSTRACT:

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

SUBMITTER: Wang X 

PROVIDER: S-EPMC11215832 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature