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.

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.

Use Cases

1. Prioritize Variants at a Known Locus

Question

"Which variant at the TCF7L2 locus is likely causal for type 2 diabetes?" from python_implementation import prioritize_causal_variants

Prioritize variants in TCF7L2 for diabetes

result

prioritize_causal_variants ( "TCF7L2" , "type 2 diabetes" ) print ( result . get_summary ( ) )

Output shows:

- Credible sets containing TCF7L2 variants

- Posterior probabilities (via fine-mapping methods)

- Top L2G genes (which genes are likely affected)

- Associated traits

1. Fine-Map a Specific Variant

   Question

   "What do we know about rs429358 (APOE4) from fine-mapping?"

Fine-map a specific variant

result

prioritize_causal_variants ( "rs429358" )

Check which credible sets contain this variant

for

cs

in

result

.

credible_sets

:

print

(

f"Trait:

{

cs

.

trait

}

"

)

print

(

f"Fine-mapping method:

{

cs

.

finemapping_method

}

"

)

print

(

f"Top gene:

{

cs

.

l2g_genes

[

0

]

if

cs

.

l2g_genes

else

'N/A'

}

"

)

print

(

f"Confidence:

{

cs

.

confidence

}

"

)

3. Explore All Loci from a GWAS Study

Question

"What are all the causal loci from the recent T2D meta-analysis?" from python_implementation import get_credible_sets_for_study

Get all fine-mapped loci from a study

credible_sets

get_credible_sets_for_study ( "GCST90029024" )

T2D GWAS

print ( f"Found { len ( credible_sets ) } independent loci" )

Examine each locus

for

cs

in

credible_sets

:

print

(

f"\nRegion:

{

cs

.

region

}

"

)

print

(

f"Lead variant:

{

cs

.

lead_variant

.

rs_ids

[

0

]

if

cs

.

lead_variant

else

'N/A'

}

"

)

if

cs

.

l2g_genes

:

top_gene

=

cs

.

l2g_genes

[

0

]

print

(

f"Most likely causal gene:

{

top_gene

.

gene_symbol

}

(L2G:

{

top_gene

.

l2g_score

:

.3f

}

)"

)

4. Find GWAS Studies for a Disease

Question

"What GWAS studies exist for Alzheimer's disease?" from python_implementation import search_gwas_studies_for_disease

Search by disease name

studies

search_gwas_studies_for_disease ( "Alzheimer's disease" ) for study in studies [ : 5 ] : print ( f" { study [ 'id' ] } : { study . get ( 'nSamples' , 'N/A' ) } samples" ) print ( f" Author: { study . get ( 'publicationFirstAuthor' , 'N/A' ) } " ) print ( f" Has summary stats: { study . get ( 'hasSumstats' , False ) } " )

Or use precise disease ontology IDs

studies

search_gwas_studies_for_disease ( "Alzheimer's disease" , disease_id = "EFO_0000249"

EFO ID for Alzheimer's

)

5. Get Validation Suggestions

Question

"How should we validate the top causal variant?" result = prioritize_causal_variants ( "APOE" , "alzheimer" )

Get experimental validation suggestions

suggestions

result . get_validation_suggestions ( ) for suggestion in suggestions : print ( suggestion )

Output includes:

- CRISPR knock-in experiments

- Reporter assays

- eQTL analysis

- Colocalization studies

Workflow Example: Complete Fine-Mapping Analysis from python_implementation import ( prioritize_causal_variants , search_gwas_studies_for_disease , get_credible_sets_for_study )

Step 1: Find relevant GWAS studies

print ( "Step 1: Finding T2D GWAS studies..." ) studies = search_gwas_studies_for_disease ( "type 2 diabetes" , "MONDO_0005148" ) largest_study = max ( studies , key = lambda s : s . get ( 'nSamples' , 0 ) or 0 ) print ( f"Largest study: { largest_study [ 'id' ] } ( { largest_study . get ( 'nSamples' , 'N/A' ) } samples)" )

Step 2: Get all fine-mapped loci from the study

print ( "\nStep 2: Getting fine-mapped loci..." ) credible_sets = get_credible_sets_for_study ( largest_study [ 'id' ] , max_sets = 100 ) print ( f"Found { len ( credible_sets ) } credible sets" )

Step 3: Find loci near genes of interest

print ( "\nStep 3: Finding TCF7L2 loci..." ) tcf7l2_loci = [ cs for cs in credible_sets if any ( gene . gene_symbol == "TCF7L2" for gene in cs . l2g_genes ) ] print ( f"TCF7L2 appears in { len ( tcf7l2_loci ) } loci" )

Step 4: Prioritize variants at TCF7L2

print ( "\nStep 4: Prioritizing TCF7L2 variants..." ) result = prioritize_causal_variants ( "TCF7L2" , "type 2 diabetes" )

Step 5: Print summary and validation plan

print

(

"\n"

+

"="

*

60

)

print

(

"FINE-MAPPING SUMMARY"

)

print

(

"="

*

60

)

print

(

result

.

get_summary

(

)

)

print

(

"\n"

+

"="

*

60

)

print

(

"VALIDATION STRATEGY"

)

print

(

"="

*

60

)

suggestions

=

result

.

get_validation_suggestions

(

)

for

suggestion

in

suggestions

:

print

(

suggestion

)

Data Classes

FineMappingResult

Main result object containing:

query_variant

Variant annotation

query_gene

Gene symbol (if queried by gene)

credible_sets

List of fine-mapped loci

associated_traits

All associated traits

top_causal_genes

L2G genes ranked by score

Methods:

get_summary()

Human-readable summary

get_validation_suggestions()

Experimental validation strategies

CredibleSet

Represents a fine-mapped locus:

study_locus_id

Unique identifier

region

Genomic region (e.g., "10:112861809-113404438")

lead_variant

Top variant by posterior probability

finemapping_method

Statistical method used (SuSiE, FINEMAP, etc.)

l2g_genes

Locus-to-gene predictions

confidence

Credible set confidence (95%, 99%)

L2GGene

Locus-to-gene prediction:

gene_symbol

Gene name (e.g., "TCF7L2")

gene_id

Ensembl gene ID

l2g_score

Probability score (0-1)

VariantAnnotation

Functional annotation for a variant:

variant_id

Open Targets format (chr_pos_ref_alt)

rs_ids

dbSNP identifiers

chromosome

,

position

Genomic coordinates

most_severe_consequence

Functional impact

allele_frequencies

Population-specific MAFs

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

Fine-Mapping Methods Compared

Method

Approach

Strengths

Use Case

SuSiE

Sum of Single Effects

Handles multiple causal variants

Multi-signal loci

FINEMAP

Bayesian shotgun stochastic search

Fast, scalable

Large studies

PAINTOR

Functional annotations

Integrates epigenomics

Regulatory variants

CAVIAR

Colocalization

Finds shared causal variants

eQTL overlap

Common Questions

Q: Why don't all variants have credible sets?

A: Fine-mapping requires:

GWAS summary statistics (not just top hits)

LD reference panel

Sufficient signal strength (p < 5e-8)

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:

Posterior probability (higher = better)

Functional consequence (coding > regulatory > intergenic)

eQTL evidence

Evolutionary conservation

Experimental feasibility

Limitations

LD-dependent

Fine-mapping accuracy depends on LD structure matching the study population

Requires summary stats

Not all studies provide full summary statistics

Computational intensive

Fine-mapping large studies takes significant resources

Prior assumptions

Bayesian methods depend on priors (number of causal variants, effect sizes)

Missing data

Not all GWAS loci have been fine-mapped in Open Targets Best Practices Start with study-level queries when exploring a new disease Check multiple studies for replication of signals Combine with functional data (eQTLs, chromatin, CRISPR screens) Consider ancestry - LD differs across populations Validate experimentally - fine-mapping provides candidates, not proof References Wang et al. (2020) "A simple new approach to variable selection in regression, with application to genetic fine mapping." JRSS-B (SuSiE) Benner et al. (2016) "FINEMAP: efficient variable selection using summary data from genome-wide association studies." Bioinformatics Ghoussaini et al. (2021) "Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics." NAR Mountjoy et al. (2021) "An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci." Nat Genet