GWAS-to-Drug Target Discovery

Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.

IMPORTANT: Always use English terms in tool calls. Respond in the user's language.

Overview

This skill bridges genetic discoveries from GWAS with drug development by:

1. Identifying genetic risk factors - Finding genes associated with diseases
2. Assessing druggability - Evaluating which genes can be targeted by drugs
3. Prioritizing targets - Ranking candidates by genetic evidence strength
4. Finding existing drugs - Discovering approved/investigational compounds
5. Identifying repurposing opportunities - Matching drugs to new indications

Key insight: Targets with genetic support have 2x higher probability of clinical approval (Nelson et al., Nature Genetics 2015).

Workflow Steps

Step 1: GWAS Gene Discovery

Input: Disease/trait name (e.g., "type 2 diabetes", "Alzheimer disease")

Process: Query GWAS Catalog for associations, filter by significance (p < 5x10^-8), map variants to genes, aggregate evidence.

Tools:

• gwas_get_associations_for_trait - Get associations by disease
• gwas_search_associations - Flexible search
• gwas_get_associations_for_snp - SNP-specific associations
• OpenTargets_search_gwas_studies_by_disease - Curated GWAS data
• OpenTargets_get_variant_credible_sets - Fine-mapped loci with L2G predictions

Step 2: Druggability Assessment

Input: Gene list from Step 1

Process: Check target class, assess tractability, evaluate safety, check for tool compounds or structures.

Tools:

• OpenTargets_get_target_tractability_by_ensemblID - Druggability assessment
• OpenTargets_get_target_classes_by_ensemblID - Target classification
• OpenTargets_get_target_safety_profile_by_ensemblID - Safety data
• OpenTargets_get_target_genomic_location_by_ensemblID - Genomic context

Step 3: Target Prioritization

Scoring Formula:

Target Score = (GWAS Score x 0.4) + (Druggability x 0.3) + (Clinical Evidence x 0.2) + (Novelty x 0.1)

Rank targets by composite score. Generate target dossiers.

Step 4: Existing Drug Search

Process: Search drug-target associations, find approved drugs and clinical candidates, get MOA and indication data.

Tools:

• OpenTargets_get_associated_drugs_by_disease_efoId - Known drugs for disease
• OpenTargets_get_drug_mechanisms_of_action_by_chemblId - Drug MOA
• ChEMBL_get_target_activities - Bioactivity data
• ChEMBL_get_drug_mechanisms / ChEMBL_search_drugs - Drug data

Step 5: Clinical Evidence & Safety

Tools:

• FDA_get_adverse_reactions_by_drug_name - Safety data
• FDA_get_active_ingredient_info_by_drug_name - Drug composition
• OpenTargets_get_drug_warnings_by_chemblId - Drug warnings

Step 6: Repurposing Opportunities

Match drug targets to new disease genes, assess mechanistic fit, check contraindications, estimate repurposing probability.

Quick Start

from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True)
tu.load_tools()

# Step 1: Get GWAS associations
associations = tu.tools.gwas_get_associations_for_trait(trait="type 2 diabetes")

# Step 2: Assess druggability
tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblID="ENSG00000148737")

# Step 3: Find existing drugs
drugs = tu.tools.OpenTargets_get_associated_drugs_by_disease_efoId(efoId="EFO_0001360")

All Tools by Category

GWAS & Genetics:

• gwas_get_associations_for_trait / gwas_search_associations / gwas_get_associations_for_snp
• OpenTargets_search_gwas_studies_by_disease / OpenTargets_get_variant_credible_sets

Target Assessment:

• OpenTargets_get_target_tractability_by_ensemblID / OpenTargets_get_target_classes_by_ensemblID
• OpenTargets_get_target_safety_profile_by_ensemblID / OpenTargets_get_target_genomic_location_by_ensemblID

Drug Discovery:

• OpenTargets_get_associated_drugs_by_disease_efoId / OpenTargets_get_drug_mechanisms_of_action_by_chemblId
• ChEMBL_get_target_activities / ChEMBL_get_drug_mechanisms / ChEMBL_search_drugs

Safety & Clinical:

• FDA_get_adverse_reactions_by_drug_name / FDA_get_active_ingredient_info_by_drug_name
• OpenTargets_get_drug_warnings_by_chemblId

Literature:

• PubMed_search_articles / EuropePMC_search_articles / ClinicalTrials_search

Best Practices

1. Multi-ancestry GWAS: Include trans-ethnic meta-analyses for robust signals
2. Functional validation: Confirm with eQTL, pQTL, colocalization analysis
3. Network analysis: Group GWAS hits by pathway (KEGG, Reactome)
4. Safety assessment: Check gnomAD pLI, GTEx expression, PharmaGKB
5. Batch operations: Use tu.run_batch() for parallel queries across targets

Troubleshooting

Reference Files

• REFERENCE.md - Detailed concepts, druggability tiers, clinical translation, limitations, ethics
• EXAMPLES.md - Use cases (Huntington's, Alzheimer's, diabetes) with success stories
• REPORT_TEMPLATE.md - Output report template with scoring criteria
• PROCEDURES.md - Step-by-step implementation procedures
• QUICK_START.md - Quick start guide
• Related skills: tooluniverse-drug-repurposing, disease-intelligence-gatherer, tooluniverse-sdk