Pipeline overview

Regenerate this diagram with make docs-metro-map after changing main.nf or its subworkflows; the source lives at docs/architecture/pipeline-metro-map.mmd.

Outputs

The workflow’s output {} block (in main.nf) is the authoritative source for what gets published. All paths below are relative to params.output_dir.

Output	Path	Produced by	Contents
Loci	locus_breaker_clumped_study_locus/	LocusBreaker	Per-study clumped-locus Parquet, filtered to studies that passed manifest validation.
Collected Loci	collected_loci/full_overlaps/	Locus Collection	Canonical multi-ancestry locus sets assembled from all input studies for a run; the sole candidate-set output carried forward into LD annotation and fine-mapping.
Collection Stats	collected_loci/stats/	Locus Collection	Size and count statistics for the canonical-region collection.
Annotated Locus Sets	locus_annotation/	LD Annotation	LD-annotated locus-set Parquet. The pairwise LD matrix itself is a transient work artifact and is not published.
LD-pair Stats	locus_annotation/stats/	LD Annotation	Per-locus-set statistics on LD-pair coverage.
Credible Sets	multisusie/<runId>/<fineMappingLocusSetId>/study_locus.parquet	Fine Mapping	Gentropy-compatible StudyLocus Parquet with the fitted credible sets.
AnnData	multisusie/<runId>/<fineMappingLocusSetId>/fit.h5ad	Fine Mapping	Extended AnnData (.h5ad) result with component-level posteriors and provenance.
Fine-mapping Stats	multisusie/<runId>/<fineMappingLocusSetId>/stats.json	Fine Mapping	JSON status record for the locus-set fit.
Manifest Validation Report	validation/manifest/	Manifest Validation	JSONL records of manifest rows rejected during validation.
LocusBreaker Status	status/locus_breaker/	LocusBreaker	JSONL records flagging runs with empty LocusBreaker output.
Locus Collection Status	status/locus_collection/	Locus Collection	JSONL records flagging runs with empty collection output.
Locus Annotation Status	status/locus_annotation/	LD Annotation	JSONL records flagging locus sets with no valid LD pairs.

IDIC performs summary-statistics fine-mapping using out-of-sample LD. The workflow is designed for multi-study and multi-ancestry analyses and emits Gentropy-compatible datasets for downstream Open Targets processing.

The major stages are:

1. Read and filter study metadata from a manifest.

2. Break summary statistics into study loci.

3. Collect canonical multi-ancestry locus sets across studies in a shared runId.

4. Prepare LD and fine-mapping inputs.

5. Run a configured fine-mapping route, such as MultiSuSiE.

The pipeline can run locally for development or on Google Cloud through the provided Nextflow profiles.

Data resolution

The workflow operates at summary-statistics resolution. It does not require individual-level genotypes. LD is supplied through external reference panels, which makes the workflow suitable for large-scale GWAS Catalog analyses while introducing the usual out-of-sample LD limitations.