Skip to main content
  • EN
Login

Main navigation

  • About FinnGen
    • Summary & Goals
      • Goals
      • FinnGen 1-2
      • FinnGen 3 (2023-2027)
    • Consortium
      • Partners
      • Funding
      • Governance
      • Steering Committee Members
      • Scientific Committee Members
      • Teams
      • Collaboration
    • Impact
      • National Impact
      • National Research Network
      • Open Science
      • Research Highlights
    • Why Finland?
      • Biobank Network
      • Legislative Review
      • National Registries
      • Population Genetic History
      • Industry partners
    • Mediabank
  • For Researchers
    • Research Activities
      • Core Analyses
      • Disease Task Forces
      • Expansion Areas
      • Global Initiatives
    • Cohort and Data
      • Register Data
      • Basic Characteristics
      • Clinical Endpoints
      • Genetic Data
      • Laboratory Values
      • Other Phenotype Data
      • Recruitment
      • Other Biological Data
      • Data Available
    • Accessing Data, Results & Tools
      • Access results
      • Computing environment
      • How We Collaborate
      • Partner researchers
      • Public data releases
      • Tools
    • Publications
    • Researcher FAQ
  • For Participants
    • Participation
      • Study Participants
      • Biobanks' Role in FinnGen
      • Biobank Contact Information
      • Recall Studies
      • Questions and answers
    • Data Protection and Security
      • Data Protection
      • Data Security
      • Sample & Data Flow
      • Data Protection Statement
    • Significance
      • Videos
  • News
  • EN

Links

  • PheWeb
  • Risteys
  • Meta-analysis Pheweb (FG-MVP-UKBB)
  • FinnGen handbook
  • Meta-analysis Pheweb (FG-UKBB)
  • Coding variant browser
  • Endpoint Browser
  • FinnGen GITHUB
  • LAVAA volcano plot
  • Multiple Manhattan Plot
  • Variant Cluster Corrector
  • Variant Cluster Call Corrector
Breadcrumb
  1. Home
  3. Leveraging Molecular-QTL Co-association To Predict Novel Disease-associated Genetic Loci Using a Graph Convolutional Neural Network

Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network

Ng-Kee-Kwong J, Bretherick AD.
PLoS One. 2025 Jun 10;20(6):e0324183. doi: 10.1371/journal.pone.0324183.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0324183
Article
Jun 10, 2025

Contact us

Contact person
finngen-info@helsinki.fi
Follow us on Bluesky
@finngen.bsky.social

Lead organisations

The University of Helsinki is the organization responsible for the FinnGen research project. The Finnish consortium partners include all Finnish public biobanks and their background organisations, so the project covers the whole of Finland. All partners are listed on this page.

About the website

Accessibility statement