GWAS and xQTL integration mini-protocol

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

input/susie_enloc_data/protocol_example.enloc.gwas_meta.tsv; input/susie_enloc_data/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

input/twas/protocol_example.twas.gwas_meta.tsv; input/twas/protocol_example.twas.xqtl_meta.tsv; input/ld_reference/protocol_example.ld_meta_file.tsv; input/twas/protocol_example.twas.LD_blocks.chr22.bed; input/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

input/twas/protocol_example.ptwas.output; input/twas/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 input/susie_enloc_data/protocol_example.enloc.gwas_meta.tsv \
    --xqtl-meta-data input/susie_enloc_data/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 input/susie_enloc_data \
    --gwas-path input/susie_enloc_data \
    --context-meta input/susie_enloc_data/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 input/susie_enloc_data/protocol_example.enloc.gwas_meta.tsv \
    --xqtl-meta-data input/susie_enloc_data/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 input/susie_enloc_data \
    --gwas-path input/susie_enloc_data \
    --context-meta input/susie_enloc_data/protocol_example.enloc.context_meta.tsv \
    --ld-meta-file-path input/ld_reference/protocol_example.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 input/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data input/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data input/ld_reference/protocol_example.ld_meta_file.tsv \
    --ld_reference_sample_size 17000 \
    --regions input/twas/protocol_example.twas.LD_blocks.chr22.bed \
    --xqtl_type_table input/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 input/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data input/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data input/ld_reference/protocol_example.ld_meta_file.tsv \
    --regions input/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 input/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data input/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data input/ld_reference/protocol_example.ld_meta_file.tsv \
    --regions input/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 input/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data input/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data input/ld_reference/protocol_example.ld_meta_file.tsv \
    --regions input/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 input/twas/protocol_example.twas.gwas_meta.tsv \
    --xqtl_meta_data input/twas/protocol_example.twas.xqtl_meta.tsv \
    --ld_meta_data input/ld_reference/protocol_example.ld_meta_file.tsv \
    --ld_reference_sample_size 17000 \
    --regions input/twas/protocol_example.twas.LD_blocks.chr22.bed \
    --xqtl_type_table input/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 input/twas/protocol_example.fastenloc.gene.out \
    --ptwas-file input/twas/protocol_example.ptwas.output \
    --tissue DLPFC \
    --cwd output/intact

Output Files#

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