mims-harvard/tooluniverse-gwas-finemapping

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