Statistical fine-mapping of GWAS loci using credible sets (SuSiE, FINEMAP) and locus-to-gene scoring (Open Targets L2G). Identifies likely causal variants and target genes — distinct from positional 'nearest gene' which is often wrong. Use for prioritizing causal variants at GWAS hits, comparing fin

COMPUTE, DON'T DESCRIBE When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it. GWAS Fine Mapping & Causal Variant Prioritization Identify and prioritize causal variants at GWAS loci using statistical fine mapping and locus to gene predictions. Overview Genome wide association studies (GWAS) identify genomic regions associated with traits, but linkage disequilibrium (LD) makes it difficult to pinpoint the causal variant. Fine mapping uses Bayesian statistical methods to compute the posterior probability that each variant is causal, given the GWAS summary statistics. REASONING STRATEGY — Start Here : Fine mapping asks: which variant at this locus is CAUSAL? Work through this chain: 1. LD structure first — variants in high LD (r² 0.8) cannot be statistically distinguished from each other. Look up the LD block via Open Targets or the GWAS Catalog before assuming any single variant is the cause. 2. Functional annotation breaks LD ties — if two variants have similar posterior probabilities but one is coding (missense, stop gain) or sits in an active regulatory element (promoter, enhancer), that variant is biologically prioritized. Functional evidence is the tiebreaker. 3. eQTL colocalization is the key bridge — a variant that is also a significant eQTL for a nearby gene in the relevant tissue (e.g., a pancreatic islet eQTL for a T2D locus) has a mechanistic story. Look up eQTL evidence via Open Targets L2G scores; don't assume the nearest gene is the effector gene. This skill provides tools to: Prioritize causal variants using fine mapping posterior probabilities Link variants to genes using locus to gene (L2G) predictions Annotate variants with functional consequences Suggest validation strategies based on fine mapping results Key Concepts Credible Sets A credible set is a minimal set of variants that contains the causal variant with high confidence (typically 95% or 99%). Each variant in the set has a posterior probability of being causal, computed using methods like: SuSiE (Sum of Single Effects) FINEMAP (Bayesian fine mapping) PAINTOR (Probabilistic Annotation INtegraTOR) Posterior Probability The probability that a specific variant is causal, given the GWAS data and LD structure. Higher posterior probability = more likely to be causal. Locus to Gene (L2G) Predictions L2G scores integrate multiple data types to predict which gene is affected by a variant: Distance to gene (closer = higher score) eQTL evidence (expression changes) Chromatin interactions (Hi C, promoter capture) Functional annotations (coding variants, regulatory regions) L2G scores range from 0 to 1, with higher scores indicating stronger gene variant links. Fine Mapping Reasoning Framework (CRITICAL) LOOK UP DON'T GUESS never assume a lead SNP is the causal variant. Always check LD structure, credible sets, and functional annotations via the tools below. Step 1: Lead SNP vs Causal Variant The lead SNP (most significant p value) is often NOT the causal variant. It is simply the best tagged variant on the genotyping array. The causal variant may be: In perfect LD (r2 0.95) with the lead SNP but with a functional consequence A non coding regulatory variant not on the array One of several independent signals at the locus (conditional analysis reveals multiple) Action : Always call OpenTargets get variant credible sets for the lead SNP. If the posterior probability is < 0.5, the lead SNP is likely NOT causal examine other variants in the credible set. Step 2: LD Structure Interpretation LD blocks define the resolution limit of fine mapping: Tight LD block (few variants, r2 0.9) : Credible set will be small; functional annotation is the tiebreaker Broad LD block (many variants) : Credible set is large; statistical fine mapping alone is insufficient need functional data (eQTL, chromatin, CRISPR) Population matters : LD patterns differ between European, African, East Asian populations. African populations have shorter LD blocks and better fine mapping resolution. Check which population the GWAS was conducted in. Step 3: Credible Set Analysis When interpreting a credible set: 1. Size matters : A 95% credible set with 1 3 variants = high resolution. With 50+ variants = low resolution, need more data. 2. Posterior probability distribution : If one variant has PP 0.5, it is the strong favorite. If PP is spread evenly across many variants, no single causal variant can be identified statistically. 3. Multiple credible sets at one locus : Indicates multiple independent causal signals (allelic heterogeneity). Each set represents a different causal mechanism. Step 4: Colocalization Reasoning Colocalization asks: do two association signals (e.g., GWAS + eQTL) share the SAME causal variant? High L2G score ( 0.7) + eQTL in relevant tissue : Strong evidence the variant affects disease THROUGH gene expression changes High GWAS signal but no eQTL : Variant may act through protein coding change, splicing, or a tissue/cell type not yet profiled eQTL for distant gene (not nearest) : The effector gene is NOT the nearest gene. LOOK UP the L2G score do not default to nearest gene Step 5: Prioritization Tiebreakers When multiple variants have similar posterior probabilities: 1. Coding variant (missense, stop gain) regulatory intronic intergenic 2. In active chromatin mark (H3K27ac, H3K4me1) in disease relevant tissue 3. Disrupts transcription factor binding motif 4. Conserved across species (PhyloP, GERP) 5. eQTL in disease relevant tissue with consistent direction of effect Common Queries "Which variant at the TCF7L2 locus is likely causal for type 2 diabetes?" → Use OpenTargets get variant credible sets or gwas search snps with gene=TCF7L2 "Fine map rs429358 (APOE4)" → Use OpenTargets get variant info then OpenTargets get variant credible sets "All causal loci from GWAS study GCST90029024" → Use OpenTargets get study credible sets "GWAS studies for Alzheimer's disease" → Use OpenTargets search gwas studies by disease or gwas search studies Tools Used Open Targets Genetics (GraphQL) OpenTargets get variant info : Variant details and allele frequencies OpenTargets get variant credible sets : Credible sets containing a variant OpenTargets get credible set detail : Detailed credible set information OpenTargets get study credible sets : All loci from a GWAS study OpenTargets search gwas studies by disease : Find studies by disease GWAS Catalog (REST API) gwas search snps : Find SNPs by gene or rsID gwas get snp by id : Detailed SNP information gwas get associations for snp : All trait associations for a variant gwas search studies : Find studies by disease/trait Understanding Fine Mapping Output Interpreting Posterior Probabilities 0.5 : Very likely causal (strong candidate) 0.1 0.5 : Plausible causal variant 0.01 0.1 : Possible but uncertain < 0.01 : Unlikely to be causal Interpreting L2G Scores 0.7 : High confidence gene variant link 0.5 0.7 : Moderate confidence 0.3 0.5 : Weak but possible link < 0.3 : Low confidence Common Questions Q: Why don't all variants have credible sets? A: Fine mapping requires: 1. GWAS summary statistics (not just top hits) 2. LD reference panel 3. Sufficient signal strength (p < 5e 8) 4. Computational resources Q: Can a variant be in multiple credible sets? A: Yes! A variant can be causal for multiple traits (pleiotropy) or appear in different studies for the same trait. Q: What if the top L2G gene is far from the variant? A: This suggests regulatory effects (enhancers, promoters). Check: eQTL evidence in relevant tissues Chromatin interaction data (Hi C) Regulatory element annotations (Roadmap, ENCODE) Q: How do I choose between variants in a credible set? A: Prioritize by: 1. Posterior probability (higher = better) 2. Functional consequence (coding regulatory intergenic) 3. eQTL evidence 4. Evolutionary conservation 5. Experimental feasibility Limitations 1. LD dependent : Fine mapping accuracy depends on LD structure matching the study population 2. Requires summary stats : Not all studies provide full summary statistics 3. Computational intensive : Fine mapping large studies takes significant resources 4. Prior assumptions : Bayesian methods depend on priors (number of causal variants, effect sizes) 5. Missing data : Not all GWAS loci have been fine mapped in Open Targets Best Practices 1. Start with study level queries when exploring a new disease 2. Check multiple studies for replication of signals 3. Combine with functional data (eQTLs, chromatin, CRISPR screens) 4. Consider ancestry LD differs across populations 5. Validate experimentally fine mapping provides candidates, not proof References 1. Wang et al. (2020) "A simple new approach to variable selection in regression, with application to genetic fine mapping." JRSS B (SuSiE) 2. Benner et al. (2016) "FINEMAP: efficient variable selection using summary data from genome wide association studies." Bioinformatics 3. Ghoussaini et al. (2021) "Open Targets Genetics: systematic identification of trait associated genes using large scale genetics and functional genomics." NAR 4. Mountjoy et al. (2021) "An open approach to systematically prioritize causal variants and genes at all published human GWAS trait associated loci." Nat Genet Related Skills tooluniverse gwas explorer : Broader GWAS analysis tooluniverse eqtl colocalization : Link variants to gene expression tooluniverse gene prioritization : Systematic gene ranking

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