tools-only 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- Withdrawn drugs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, withdrawnmechanismOfAction: 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
Multiple evidence types strengthen confidence - Convergent evidence from different data types provides stronger support
Under-studied diseases score lower - Novel or rare diseases may have strong evidence but lower aggregate scores due to limited research
Association scores are not probabilities - Scores rank relative evidence strength, not success probability
Context matters - Evidence strength depends on:
- Disease mechanism understanding
- Target biology and druggability
- Clinical precedence in related indications
- Safety considerations
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:
- Check multiple sources - Single source may be unreliable
- Prioritize human genetic evidence - Strongest disease relevance
- Consider clinical precedence - Known drugs indicate druggability
- Assess mechanistic support - Pathway evidence supports biology
- Review literature manually - For critical decisions, read primary publications
- Validate in primary databases - Cross-reference with ClinVar, ClinGen, etc.