An automated multi-modal graph-based pipeline for mouse genetic discovery.

ABSTRACT:

Motivation

Our ability to identify causative genetic factors for mouse genetic models of human diseases and biomedical traits has been limited by the difficulties associated with identifying true causative factors, which are often obscured by the many false positive genetic associations produced by a GWAS.

Results

To accelerate the pace of genetic discovery, we developed a graph neural network (GNN)-based automated pipeline (GNNHap) that could rapidly analyze mouse genetic model data and identify high probability causal genetic factors for analyzed traits. After assessing the strength of allelic associations with the strain response pattern; this pipeline analyzes 29M published papers to assess candidate gene-phenotype relationships; and incorporates the information obtained

SUBMITTER: Fang Z 

PROVIDER: S-EPMC9992076 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature