Multi-scale inference of genetic trait architecture using biologically annotated neural networks.

ABSTRACT: In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment anal

SUBMITTER: Demetci P 

PROVIDER: S-EPMC8407593 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature