Prioritizing Disease-Associated Genes

This project was a study to make predictions about disease-gene associations. Our approach learns what types of paths in a heterogeneous network occur more frequently between genes and diseases that have been associated by GWAS. Using hetnet edge prediction we predict the probability that each gene associates with each disease.

Applications of these results include:

  • Using the predications as prior probabilities of association to increase the power of GWAS analysis
  • Determining candidate causal genes within genomic regions identified by GWAS
  • Identifying genes of biological interest for a specific disease
  • Identifying diseases of interest for a specific gene
  • Exploring a high-confidence, gene-centric translation of the GWAS Catalog
  • Comparing the informativeness of various information domains for identifying pathogenic variants
  • Viewing the contibution of each network-based feature composing an association prediction

Browse predictions by gene or disease and learn more about the network-based features. Results include context-specific summaries and performance assessments.


The disease-associated gene predictions are released under CC0 1.0. Information included from external sources are released under their own respective licenses.