tooluniverse-gwas-drug-discovery

# 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).

## Reasoning Strategy

GWAS-to-drug translation succeeds when you think causally. A genetic association provides causal direction that observational data cannot: if a loss-of-function variant protects against disease, an inhibitor of that gene's product is the hypothesis to test. The direction of effect (LOF vs. GOF) determines whether you need an inhibitor or an agonist — get this wrong and the drug works backwards. GWAS effect sizes are small (odds ratios of 1.1–1.5 are typical), but the drug effect may be much larger or smaller than the genetic effect; the genetic signal validates the target, not the dose. Always integrate multiple lines of evidence (eQTL colocalization, pQTL, L2G score) before committing to a target, because many GWAS variants tag the causal gene only indirectly.

**LOOK UP DON'T GUESS**: Do not assume which gene a GWAS variant implicates — use `OpenTargets_get_variant_credible_sets` or `gwas_get_associations_for_snp` to get the actual mapped gene and L2G score. Do not guess the direction of effect, odds ratio, or whether a drug already exists for the target; always query the tools.

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## 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 (use disease_trait not trait; no p_value_threshold param)
associations = tu.tools.gwas_get_associations_for_trait(disease_trait="type 2 diabetes")

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

# Step 3: Find existing drugs per target via DGIdb (OpenTargets drug query may return HTTP 400)
drugs = tu.tools.DGIdb_get_drug_gene_interactions(genes=["TCF7L2"])
```

---

## 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_studies`

---

## 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