Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target hypothesis generation, druggable-fraction analysis

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. 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 : 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 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 assessment : Check gnomAD pLI, GTEx expression, PharmaGKB 5. Batch operations : Use tu.run batch() for parallel queries across targets Parameter Gotchas Issue Wrong Correct GWAS trait param gwas get associations for trait(trait=...) disease trait=... (no trait param exists) GWAS p value filter p value threshold=5e 8 No such param; filter client side after fetching results OpenTargets ensembl case ensemblID="ENSG..." ensemblId="ENSG..." (lowercase 'd') ClinicalTrials tool name ClinicalTrials search studies(...) ClinicalTrials search studies(...) DGIdb tool name DGIdb get drug gene interactions(...) DGIdb get drug gene interactions(genes=[...]) OpenTargets disease drugs OpenTargets get associated drugs by disease efoId may return HTTP 400 Fall back to DGIdb get drug gene interactions per gene GWAS study search param gwas search studies(disease trait=...) Use efo trait=... for studies (disease trait works for associations only) Interpretation: From GWAS Hit to Drug Target GWAS Signal Strength Assessment Signal Quality Criteria Drug Discovery Value Gold standard Genome wide significant (p < 5e 8), replicated across ancestries, L2G 0.5, eQTL colocalized Highest priority — genetic causality established Strong Genome wide significant, L2G 0.3, biological plausibility High priority — pursue with functional validation Moderate Suggestive (p < 1e 5), or significant but no fine mapping Medium — needs additional evidence before investment Weak Single study, no replication, low L2G, no functional support Low — hypothesis generating only Target Prioritization Decision Tree After identifying GWAS linked genes, rank them by answering: 1. Is the gene druggable? (DGIdb category: kinase/GPCR/ion channel = yes; transcription factor/scaffold = harder) If approved drug exists → REPURPOSING opportunity (fastest path) If druggable but no drug → NOVEL TARGET (standard drug discovery) If not druggable → consider antisense/PROTAC/genetic medicine 2. Is the genetic direction clear? LOF variants increase disease risk → need an AGONIST or gene therapy GOF variants increase disease risk → need an INHIBITOR (typical small molecule) Direction unclear → need functional studies before drug design 3. What's the effect size? (Odds ratio from GWAS) OR 2.0: strong effect, likely penetrant → Mendelian like, high confidence OR 1.2 2.0: moderate, common in complex disease → validate with independent data OR < 1.2: small effect → may not be clinically meaningful alone 4. Is there clinical precedent? Drug for same target approved for ANY disease → safety data exists → lower risk Drug in clinical trials → partial de risking No precedent → full de novo development risk 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

tooluniverse-gwas-drug-discovery