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.