tooluniverse-gwas-drug-discovery - Claude MCP Skill

# 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

```python
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

| Problem | Solution |
|---------|----------|
| No GWAS hits for disease | Try broader trait name, check synonyms, use OpenTargets |
| Gene not in druggable class | Consider antibody/antisense modalities, check pathway neighbors |
| No existing drugs for target | Target may be novel - check tool compounds in ChEMBL |
| Low L2G score | Variants may be regulatory - check eQTL/pQTL evidence |

---

## 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