GWAS Fine-Mapping & Causal Variant Prioritization

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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)