opentargets-database/references/evidence_types.md

Evidence Types and Data Sources

Overview

Evidence represents any event or set of events that identifies a target as a potential causal gene or protein for a disease. Evidence is standardized and mapped to:

• Ensembl gene IDs for targets
• EFO (Experimental Factor Ontology) for diseases/phenotypes

Evidence is organized into data types (broader categories) and data sources (specific databases/studies).

Evidence Data Types

1. Genetic Association

Evidence from human genetics linking genetic variants to disease phenotypes.

Data Sources:

GWAS (Genome-Wide Association Studies)

• Population-level common variant associations
• Filtered with Locus-to-Gene (L2G) scores >0.05
• Includes fine-mapping and colocalization data
• Sources: GWAS Catalog, FinnGen, UK Biobank, EBI GWAS

Gene Burden Tests

• Rare variant association analyses
• Aggregate effects of multiple rare variants in a gene
• Particularly relevant for Mendelian and rare diseases

ClinVar Germline

• Clinical variant interpretations
• Classifications: pathogenic, likely pathogenic, VUS, benign
• Expert-reviewed variant-disease associations

Genomics England PanelApp

• Expert gene-disease ratings
• Green (confirmed), amber (probable), red (no evidence)
• Focus on rare diseases and cancer

Gene2Phenotype

• Curated gene-disease relationships
• Allelic requirements and inheritance patterns
• Clinical validity assessments

UniProt Literature & Variants

• Literature-based gene-disease associations
• Expert-curated from scientific publications

Orphanet

• Rare disease gene associations
• Expert-reviewed and maintained

ClinGen

• Clinical genome resource classifications
• Gene-disease validity assertions

2. Somatic Mutations

Evidence from cancer genomics identifying driver genes and therapeutic targets.

Data Sources:

Cancer Gene Census

• Expert-curated cancer genes
• Tier classifications (1 = strong evidence, 2 = emerging)
• Mutation types and cancer types

IntOGen

• Computational driver gene predictions
• Aggregated from large cohort studies
• Statistical significance of mutations

ClinVar Somatic

• Somatic clinical variant interpretations
• Oncogenic/likely oncogenic classifications

Cancer Biomarkers

• FDA/EMA approved biomarkers
• Clinical trial biomarkers
• Prognostic and predictive markers

3. Known Drugs

Evidence from clinical precedence showing drugs targeting genes for disease indications.

Data Source:

ChEMBL

• Approved drugs (Phase 4)
• Clinical candidates (Phase 1-3)
• Withdrawn drugs
• Drug-target-indication triplets with mechanism of action

Clinical Trial Information:

• phase: Maximum clinical trial phase (1, 2, 3, 4)
• status: Active, terminated, completed, withdrawn
• mechanismOfAction: How drug affects target

4. Affected Pathways

Evidence linking genes to disease through pathway perturbations and functional screens.

Data Sources:

CRISPR Screens

• Genome-scale knockout screens
• Cancer dependency and essentiality data

Project Score (Cancer Dependency Map)

• CRISPR-Cas9 fitness screens across cancer cell lines
• Gene essentiality profiles

SLAPenrich

• Pathway enrichment analysis
• Somatic mutation pathway impacts

PROGENy

• Pathway activity inference
• Signaling pathway perturbations

Reactome

• Expert-curated pathway annotations
• Biological pathway representations

Gene Signatures

• Expression-based signatures
• Pathway activity patterns

5. RNA Expression

Evidence from differential gene expression in disease vs. control tissues.

Data Source:

Expression Atlas

• Differential expression data
• Baseline expression across tissues/conditions
• RNA-Seq and microarray studies
• Log2 fold-change and p-values

6. Animal Models

Evidence from in vivo studies showing phenotypes associated with gene perturbations.

Data Source:

IMPC (International Mouse Phenotyping Consortium)

• Systematic mouse knockout phenotypes
• Phenotype-disease mappings via ontologies
• Standardized phenotyping procedures

7. Literature

Evidence from text-mining of biomedical literature.

Data Source:

Europe PMC

• Co-occurrence of genes and diseases in abstracts
• Normalized citation counts
• Weighted by publication type and recency

Evidence Scoring

Each evidence source has its own scoring methodology:

Score Ranges

• Most scores normalized to 0-1 range
• Higher scores indicate stronger evidence
• Scores are NOT confidence levels but relative strength indicators

Common Scoring Approaches:

Binary Classifications:

• ClinVar: Pathogenic (1.0), Likely pathogenic (0.99), etc.
• Gene2Phenotype: Confirmed/probable ratings
• PanelApp: Green/amber/red classifications

Statistical Measures:

• GWAS: L2G scores incorporating multiple lines of evidence
• Gene Burden: Statistical significance of variant aggregation
• Expression: Adjusted p-values and fold-changes

Clinical Precedence:

• Known Drugs: Phase weights (Phase 4 = 1.0, Phase 3 = 0.8, etc.)
• Clinical status modifiers

Computational Predictions:

• IntOGen: Q-values from driver mutation analysis
• PROGENy/SLAPenrich: Pathway activity/enrichment scores

Evidence Interpretation Guidelines

Strengths by Data Type

Genetic Association - Strongest human genetic evidence

• Direct link between genetic variation and disease
• Mendelian diseases: high confidence
• GWAS: requires L2G to identify causal gene
• Consider ancestry and population-specific effects

Somatic Mutations - Direct evidence in cancer

• Strong for oncology indications
• Driver mutations indicate therapeutic potential
• Consider cancer type specificity

Known Drugs - Clinical validation

• Highest confidence: approved drugs (Phase 4)
• Consider mechanism relevance to new indication
• Phase 1-2: early evidence, higher risk

Affected Pathways - Mechanistic insights

• Supports biological plausibility
• May not predict clinical success
• Useful for hypothesis generation

RNA Expression - Observational evidence

• Correlation, not causation
• May reflect disease consequence vs. cause
• Useful for biomarker identification

Animal Models - Translational evidence

• Strong for understanding biology
• Variable translation to human disease
• Most useful when phenotype matches human disease

Literature - Exploratory signal

• Text-mining captures research focus
• May reflect publication bias
• Requires manual literature review for validation

Important Considerations

1. Multiple evidence types strengthen confidence - Convergent evidence from different data types provides stronger support

2. Under-studied diseases score lower - Novel or rare diseases may have strong evidence but lower aggregate scores due to limited research

3. Association scores are not probabilities - Scores rank relative evidence strength, not success probability

4. Context matters - Evidence strength depends on:

   • Disease mechanism understanding
   • Target biology and druggability
   • Clinical precedence in related indications
   • Safety considerations

5. Data source reliability varies - Weight expert-curated sources (ClinGen, Gene2Phenotype) higher than computational predictions

Using Evidence in Queries

Filtering by Data Type

query = """
  query evidenceByType($ensemblId: String!, $efoId: String!, $dataTypes: [String!]) {
    disease(efoId: $efoId) {
      evidences(ensemblIds: [$ensemblId], datatypes: $dataTypes) {
        rows {
          datasourceId
          score
        }
      }
    }
  }
"""
variables = {
    "ensemblId": "ENSG00000157764",
    "efoId": "EFO_0000249",
    "dataTypes": ["genetic_association", "somatic_mutation"]
}

Accessing Data Type Scores

Data type scores aggregate all source scores within that type:

query = """
  query associationScores($ensemblId: String!, $efoId: String!) {
    target(ensemblId: $ensemblId) {
      associatedDiseases(efoIds: [$efoId]) {
        rows {
          disease {
            name
          }
          score
          datatypeScores {
            componentId
            score
          }
        }
      }
    }
  }
"""

Evidence Quality Assessment

When evaluating evidence:

1. Check multiple sources - Single source may be unreliable
2. Prioritize human genetic evidence - Strongest disease relevance
3. Consider clinical precedence - Known drugs indicate druggability
4. Assess mechanistic support - Pathway evidence supports biology
5. Review literature manually - For critical decisions, read primary publications
6. Validate in primary databases - Cross-reference with ClinVar, ClinGen, etc.