Open Targets Genetics

Open Targets Genetics facilitates identification and prioritization of causal genetic variants and their target genes from GWAS, functional genomics, and multi-omics data (transcriptomic, proteomic, epigenomic) to inform target discovery and interpretation of complex traits and diseases.

Key Features:

• Systematic Fine Mapping: Systematically fine-maps 133,441 published human GWAS loci by integrating genetic associations from the GWAS Catalog and UK Biobank with functional genomics data across 92 cell types and tissues.
• Gene Prioritization: Employs machine learning models trained on fine-mapped genetics, functional genomics data, and curated gold-standard GWAS loci to distinguish causal genes from neighboring genes and improve prioritization versus distance-based methods.
• Cross-Trait Colocalization Analyses: Performs systematic disease-disease and disease–molecular trait colocalization analyses to identify shared genetic architectures across traits.
• Integration with Functional Genomics: Aggregates functional genomics evidence including eQTLs, pQTLs, gene expression, protein abundance, chromatin interactions, and conformation data to link GWAS loci and variants to likely causal genes.
• Data Visualization: Provides visual representations of GWAS signals including Manhattan-like plots, regional plots, credible set overlaps between studies, and PheWAS plots.

Scientific Applications:

• Drug Discovery and Repurposing: Supports identification and prioritization of targets for therapeutic development and assessment of drug repositioning opportunities via shared genetic architecture.
• Genetic Research: Enables prioritization of candidate causal variants and genes to investigate the genetic architecture and molecular mechanisms of complex traits and diseases.

Methodology:

Applies statistical fine-mapping methods across thousands of trait-associated loci (including systematic fine-mapping of 133,441 GWAS loci), integrates genetic associations from the GWAS Catalog and UK Biobank with functional genomics across 92 cell types and tissues, links variants to proximal and distal target genes using a single evidence score, trains machine learning models on fine-mapped genetics, functional genomics and curated gold-standard GWAS loci, and performs systematic disease–disease and disease–molecular trait colocalization analyses.