GWAS and xQTL integration mini-protocol

Integrate molecular-QTL results with GWAS using enrichment, colocalization, TWAS, cTWAS, quantile TWAS, or INTACT.

Miniprotocol Timing¶

This is the total duration for the selected route; module-specific timings appear on their respective pages. Timing: TBD

Overview¶

This mini-protocol presents complementary routes for connecting molecular-QTL and GWAS evidence. Steps 1–2 call SuSiE_enloc.ipynb, steps 3–7 call twas_ctwas.ipynb, and step 8 calls intact.ipynb. Enrichment, colocalization, standard TWAS, cTWAS, quantile TWAS, and INTACT answer different questions and are not one mandatory chain. Only cTWAS steps 4–6 must be run sequentially.

Steps¶

Choose a route before running commands; the commands are not one mandatory chain.

Analysis goal	Commands to run, in order	Inputs
Estimate global xQTL–GWAS enrichment	1	`tests/fixtures/susie_enloc/protocol_example.enloc.gwas_meta.tsv`; `tests/fixtures/susie_enloc/protocol_example.enloc.xqtl_meta.tsv`; fine-mapping objects referenced by those tables
Test pairwise colocalization	2	The same GWAS and xQTL fine-mapping metadata as step 1
Run standard TWAS and MR	3	`tests/fixtures/twas/protocol_example.twas.gwas_meta.tsv`; `tests/fixtures/twas/protocol_example.twas.xqtl_meta.tsv`; `tests/fixtures/ld_reference/ld_meta_file.tsv`; `tests/fixtures/twas/protocol_example.twas.LD_blocks.chr22.bed`; `tests/fixtures/twas/protocol_example.twas.data_type_table.txt`
Fine-map genes and SNPs jointly with cTWAS	4 → 5 → 6	GWAS, xQTL, LD-reference, and region metadata used by the TWAS route
Run quantile TWAS	7	The TWAS inputs, with quantile-specific molecular-QTL weights
Combine PTWAS and fastenloc evidence	8	`tests/fixtures/intact/protocol_example.ptwas.output`; `tests/fixtures/intact/protocol_example.fastenloc.gene.out`

Run only the commands for the selected route. Steps 4–6 are one chained cTWAS analysis; the other numbered steps are independent alternatives.

1. Estimate global xQTL–GWAS enrichment¶

What it does: Estimates whether fine-mapped xQTL signals are enriched among GWAS signals across many regions and molecular contexts.

Timing: TBD

sos run pipeline/SuSiE_enloc.ipynb xqtl_gwas_enrichment \
    --gwas-meta-data tests/fixtures/susie_enloc/protocol_example.enloc.gwas_meta.tsv \
    --xqtl-meta-data tests/fixtures/susie_enloc/protocol_example.enloc.xqtl_meta.tsv \
    --xqtl-finemapping-obj preset_variants_result susie_result_trimmed \
    --xqtl-varname-obj preset_variants_result variant_names \
    --gwas-finemapping-obj AD_Bellenguez_2022 RSS_QC_RAISS_imputed susie_result_trimmed \
    --gwas-varname-obj  AD_Bellenguez_2022 RSS_QC_RAISS_imputed variant_names \
    --xqtl-region-obj   region_info grange \
    --qtl-path output/xqtl_gwas_enrichment/susie_enloc_data \
    --gwas-path output/xqtl_gwas_enrichment/susie_enloc_data \
    --context-meta tests/fixtures/susie_enloc/protocol_example.enloc.context_meta.tsv \
    --cwd output/xqtl_gwas_enrichment

2. Test pairwise colocalization¶

What it does: Tests whether an xQTL and GWAS association in the same region are consistent with a shared causal variant.

Timing: TBD

sos run pipeline/SuSiE_enloc.ipynb susie_coloc \
    --gwas-meta-data tests/fixtures/susie_enloc/protocol_example.enloc.gwas_meta.tsv \
    --xqtl-meta-data tests/fixtures/susie_enloc/protocol_example.enloc.xqtl_meta.tsv \
    --xqtl-finemapping-obj preset_variants_result susie_result_trimmed \
    --xqtl-varname-obj preset_variants_result variant_names \
    --gwas-finemapping-obj AD_Bellenguez_2022 RSS_QC_RAISS_imputed susie_result_trimmed \
    --gwas-varname-obj  AD_Bellenguez_2022 RSS_QC_RAISS_imputed variant_names \
    --xqtl-region-obj   region_info grange \
    --qtl-path output/xqtl_gwas_enrichment/susie_enloc_data \
    --gwas-path output/xqtl_gwas_enrichment/susie_enloc_data \
    --context-meta tests/fixtures/susie_enloc/protocol_example.enloc.context_meta.tsv \
    --ld-meta-file-path tests/fixtures/ld_reference/ld_meta_file.tsv \
    --skip-enrich \
    --cwd output/susie_coloc

3. Run standard TWAS and MR¶

What it does: Tests genetically predicted molecular traits for association with the GWAS trait and reports regional Mendelian-randomization results.

Timing: TBD

sos run pipeline/twas_ctwas.ipynb twas \
    --cwd output --name protocol_example \
    --gwas_meta_data tests/fixtures/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data tests/fixtures/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data tests/fixtures/ld_reference/ld_meta_file.tsv \
    --ld_reference_sample_size 17000 \
    --regions tests/fixtures/twas/protocol_example.twas.LD_blocks.chr22.bed \
    --xqtl_type_table tests/fixtures/twas/protocol_example.twas.data_type_table.txt \
    --rsq_pval_cutoff 0.05 --rsq_cutoff 0.01 \
    --region-name chr22_10000000_19000000

4. Assemble cTWAS region data¶

What it does: Harmonizes GWAS statistics, xQTL weights, and LD for the regions that will enter cTWAS fine-mapping.

Timing: TBD

sos run pipeline/twas_ctwas.ipynb ctwas \
    --cwd output --name protocol_example \
    --thin 1 --prior_var_structure shared_all \
    --gwas_meta_data tests/fixtures/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data tests/fixtures/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data tests/fixtures/ld_reference/ld_meta_file.tsv \
    --regions tests/fixtures/twas/protocol_example.twas.LD_blocks.chr22.bed \
    --twas_weight_cutoff 0 \
    --region-name chr22_10000000_19000000

5. Estimate cTWAS global parameters¶

What it does: Reuses the assembled inputs from step 4 to estimate the prior parameters required by cTWAS.

Timing: TBD

sos run pipeline/twas_ctwas.ipynb ctwas \
    --run_param_est --skip_assembly --thin 1 \
    --prior_var_structure shared_all \
    --cwd output --name protocol_example \
    --gwas_meta_data tests/fixtures/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data tests/fixtures/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data tests/fixtures/ld_reference/ld_meta_file.tsv \
    --regions tests/fixtures/twas/protocol_example.twas.LD_blocks.chr22.bed

6. Fine-map genes and SNPs with cTWAS¶

What it does: Uses the assembled inputs and estimated parameters to calculate joint posterior inclusion probabilities for genes and SNPs.

Timing: TBD

sos run pipeline/twas_ctwas.ipynb ctwas \
    --run_finemapping --skip_assembly \
    --prior_var_structure shared_all \
    --cwd output --name protocol_example \
    --gwas_meta_data tests/fixtures/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data tests/fixtures/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data tests/fixtures/ld_reference/ld_meta_file.tsv \
    --regions tests/fixtures/twas/protocol_example.twas.LD_blocks.chr22.bed \
    --region-name chr22_10000000_19000000

7. Run quantile TWAS¶

What it does: Runs the quantile-TWAS route for molecular-trait effects that may differ across the phenotype distribution.

Timing: TBD

sos run pipeline/twas_ctwas.ipynb quantile_twas \
    --cwd output --name protocol_example \
    --gwas_meta_data tests/fixtures/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data tests/fixtures/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data tests/fixtures/ld_reference/ld_meta_file.tsv \
    --ld_reference_sample_size 17000 \
    --regions tests/fixtures/twas/protocol_example.twas.LD_blocks.chr22.bed \
    --xqtl_type_table tests/fixtures/twas/protocol_example.twas.data_type_table.txt \
    --region-name chr22_10000000_19000000

8. Combine PTWAS and colocalization with INTACT¶

What it does: Combines PTWAS z-scores with fastenloc colocalization probabilities to prioritize genes supported by both association and colocalization evidence.

Timing: TBD

sos run pipeline/intact.ipynb intact \
    --fastenloc-file tests/fixtures/intact/protocol_example.fastenloc.gene.out \
    --ptwas-file tests/fixtures/intact/protocol_example.ptwas.output \
    --tissue DLPFC \
    --cwd output/intact

Output¶

Step	Relative path	Contents
1	`output/xqtl_gwas_enrichment/<name>.<context>.enrichment.rds`	Global enrichment estimate for each GWAS–xQTL-context pair
2	`output/susie_coloc/susie_coloc/<name>.<context>@<gene>.coloc.rds`	Regional SuSiE colocalization probabilities
3	`output/twas/twas/<name>.<region_id>.<gene>.twas.rds`	Per-gene TWAS results
3	`output/twas/twas/<name>.<region_id>.twas.tsv.gz`	Regional TWAS summary
3	`output/twas/twas/<name>.<region_id>.mr_result.tsv.gz`	Regional Mendelian-randomization summary
4	`output/ctwas/<name>.ctwas_inputs.rds`	Assembled cTWAS inputs
5	`output/ctwas/<name>.ctwas_est.rds`	Estimated cTWAS prior parameters
6	`output/ctwas/<name>.ctwas_finemap.rds`	Joint gene and SNP fine-mapping results
7	`output/quantile_twas/<name>.*`	Quantile-TWAS results, with stems determined by the analysis regions
8	`output/intact/DLPFC.INTACT.rds`	Gene-level INTACT posterior probabilities

Some workflows derive context, gene, or region identifiers from metadata, so their output contains one file per analysis unit.

Anticipated Results¶

Enrichment evaluates global sharing between molecular-QTL and GWAS signals, whereas colocalization evaluates individual regions. TWAS prioritizes traits whose genetically predicted levels associate with the GWAS outcome; cTWAS jointly fine-maps genes and SNPs; INTACT prioritizes genes supported by both PTWAS and colocalization.

These analyses provide complementary evidence, not interchangeable proof of causality. Interpret prioritized genes together with fine-mapping quality, LD, prediction performance, colocalization support, tissue relevance, and sensitivity analyses.

Command Interface¶

sos run pipeline/SuSiE_enloc.ipynb -h

sos run pipeline/twas_ctwas.ipynb -h

sos run pipeline/intact.ipynb -h