Interpret a single GWAS SNP across multiple databases — GWAS Catalog hits, LD/haplotype context, eQTL evidence, regulatory annotation, ClinVar pathogenicity, gnomAD frequency. Use for 'what does this SNP do', SNP-to-mechanism tracing, and resolving lead-SNP-vs-causal-variant ambiguity. Always consid

GWAS SNP Interpretation Skill SNP interpretation : a GWAS hit is a REGION, not a single causal variant. The lead SNP may not be causal — it may be in LD with the causal variant. Always check LD structure and functional annotation before concluding a specific SNP is mechanistically responsible. Use LDlink get proxies(variant="rs...", population="EUR") to retrieve the high R² LD proxies (needs a free LDLINK TOKEN) — a proxy in a coding/regulatory region is a better mechanistic candidate than the lead SNP itself. Fine mapping (SuSiE, FINEMAP credible sets) narrows the causal set but rarely identifies a single variant with certainty. L2G scores integrate eQTL, chromatin interaction, and distance data to predict the causal gene — a lead SNP mapping to gene A may actually regulate gene B 500 kb away via a distal enhancer. LOOK UP DON'T GUESS : never assume a SNP's functional consequence, mapped gene, or population frequency — always call gwas get snp by id and OpenTargets get variant info to retrieve current annotations. Overview Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context. Use Cases: "Interpret rs7903146" (TCF7L2 diabetes variant) "What diseases is rs429358 associated with?" (APOE Alzheimer's variant) "Clinical significance of rs1801133" (MTHFR variant) "Is rs12913832 in any fine mapped loci?" (Eye color variant) What It Does The skill provides a comprehensive interpretation of SNPs by: 1. SNP Annotation : Retrieves basic variant information including genomic coordinates, alleles, functional consequence, and mapped genes 2. Association Discovery : Finds all GWAS trait/disease associations with statistical significance 3. Fine Mapping Evidence : Identifies credible sets the variant belongs to (fine mapped causal loci) 4. Gene Mapping : Uses Locus to Gene (L2G) predictions to identify likely causal genes 5. Clinical Summary : Aggregates evidence into actionable clinical significance Workflow Data Sources GWAS Catalog (EMBL EBI) SNP annotations : Functional consequences, mapped genes, population frequencies Associations : P values, effect sizes, study metadata Coverage : 350,000+ publications, 670,000+ associations Open Targets Genetics Fine mapping : Statistical credible sets from SuSiE, FINEMAP methods L2G predictions : Machine learning based gene prioritization Colocalization : QTL evidence for causal genes Coverage : UK Biobank, FinnGen, and other large cohorts Input Parameters Required rs id (str): dbSNP rs identifier Format: "rs" + number (e.g., "rs7903146") Must be valid rsID in GWAS Catalog Optional include credible sets (bool, default=True): Query fine mapping data True: Complete interpretation (slower, ~10 30s) False: Fast associations only (~2 5s) p threshold (float, default=5e 8): Genome wide significance threshold max associations (int, default=100): Maximum associations to retrieve Output Format Returns SNPInterpretationReport containing: 1. SNP Basic Info 2. Trait Associations 3. Credible Sets (Fine Mapping) 4. Clinical Significance Example Usage See QUICK START.md for platform specific examples. Tools Used GWAS Catalog Tools 1. gwas get snp by id : Get SNP annotation 2. gwas get associations for snp : Get all trait associations Open Targets Tools 3. OpenTargets get variant info : Get variant details with population frequencies 4. OpenTargets get variant credible sets : Get fine mapping credible sets with L2G Interpretation Guide P value Significance Levels p < 5e 8 : Genome wide significant (strong evidence) p < 5e 6 : Suggestive (moderate evidence) p < 0.05 : Nominal (weak evidence) L2G Score Interpretation 0.5 : High confidence causal gene 0.1 0.5 : Moderate confidence < 0.1 : Low confidence Clinical Actionability 1. High : Multiple genome wide significant associations + in credible sets + high L2G scores 2. Moderate : Genome wide significant associations but limited fine mapping 3. Low : Suggestive associations or limited replication Limitations 1. Variant ID Conversion : OpenTargets requires chr pos ref alt format, which may need allele lookup 2. Population Specificity : Associations may vary by ancestry 3. Effect Sizes : Beta values are study dependent (different phenotype scales) 4. Causality : Associations don't prove causation; fine mapping improves confidence 5. Currency : Data reflects published GWAS; latest studies may not be included Best Practices 1. Use Full Interpretation : Enable include credible sets=True for clinical decisions 2. Check Multiple Variants : Look at other variants in the same locus 3. Validate Populations : Consider ancestry specific effect sizes 4. Review Publications : Check original studies for context 5. Integrate Evidence : Combine with functional data, eQTLs, pQTLs Technical Notes Performance Fast mode (no credible sets): 2 5 seconds Full mode (with credible sets): 10 30 seconds Bottleneck : OpenTargets GraphQL API rate limits Error Handling Invalid rs id: Returns error message No associations: Returns empty list with note API failures: Graceful degradation (returns partial results) Related Skills Gene Function Analysis : Interpret predicted causal genes Disease Ontology Lookup : Understand trait classifications PubMed Literature Search : Find original GWAS publications Variant Effect Prediction : Functional consequence analysis References 1. GWAS Catalog: https://www.ebi.ac.uk/gwas/ 2. Open Targets Genetics: https://genetics.opentargets.org/ 3. GWAS Significance Thresholds: Fadista et al. 2016 4. L2G Method: Mountjoy et al. 2021 (Nature Genetics) Version Version : 1.0.0 Last Updated : 2026 02 13 ToolUniverse Version : = 1.0.0 Tools Required : gwas get snp by id, gwas get associations for snp, OpenTargets get variant credible sets

tooluniverse-gwas-snp-interpretation