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

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