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            "description": "\u003cp align=\"center\"\u003e\n  \u003cpicture\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-dnaprs_logo_dark.png\"\u003e\n    \u003cimg src=\"docs/images/nf-core-dnaprs_logo_light.png\" alt=\"dnaprs\"\u003e\n  \u003c/picture\u003e\n\u003c/p\u003e\n\n[![nf-test status](https://github.com/paulYRP/dnaprs/actions/workflows/nf-test.yml/badge.svg)](https://github.com/paulYRP/dnaprs/actions/workflows/nf-test.yml)\n[![nf-core linting status](https://github.com/paulYRP/dnaprs/actions/workflows/linting.yml/badge.svg)](https://github.com/paulYRP/dnaprs/actions/workflows/linting.yml)\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat\u0026logo=nextflow\u0026logoColor=white\u0026color=%230DC09D\u0026link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.1.0-green?style=flat\u0026logo=nfcore\u0026logoColor=white\u0026color=%2324B064\u0026link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.1.0)\n[![nf-test](https://img.shields.io/badge/tested_with-nf--test-337ab7)](https://www.nf-test.com/)\n[![MIT licence](https://img.shields.io/badge/license-MIT-111111)](LICENSE)\n\n## Introduction\n\n**dnaprs** is a portable Nextflow DSL2 pipeline for generating and evaluating polygenic\nrisk scores from raw target genotypes and raw GWAS summary statistics.\n\nThe pipeline discovers PGEN, BED, PED/MAP, BGEN, VCF/BCF, or GenomeStudio FinalReport\ninputs; records raw genotype EDA; resolves markers against pinned GRCh37 references;\napplies two target-QC checkpoints; and projects participants onto unrelated 1000 Genomes\nreference PCA axes. It can impute with Beagle, generate PLINK C+T and/or SBayesRC\nscores, apply participant eligibility consistently, and fit phenotype models.\n\n[![dnaprs workflow metro map showing raw inputs, target QC, reference handling, PLINK C+T, SBayesRC, phenotype models, and reporting](docs/images/dnaprs-workflow.svg)](docs/images/dnaprs-workflow.png)\n\nBeagle imputation and phenotype modelling are optional. REF icons identify resources\nfrom the verified reference cache. Direct-score sensitivity uses unimputed genotypes\nand remains separate from the primary scores.\n\nThe workflow publishes one run under `dnaprs/model1/` by default:\n\n- `data/` — generated records, checkpoints, QC, weights, scores, and model tables;\n- `figures/` — a convenient copy of every report figure;\n- `logs/` — scientific and execution logs;\n- `reports/` — the portable HTML website, downloads, and provenance.\n\n## Minimal run\n\nInstall Java 17 or newer, Nextflow 25.10.4 or newer, and Docker, Apptainer, or Singularity. With one raw target dataset in `data/plink/raw/` and raw GWAS files in `data/gwas/raw/`:\n\n```bash\nnextflow run . -profile singularity -resume\n```\n\nThe defaults are equivalent to:\n\n```bash\nnextflow run . \\\n  -profile singularity \\\n  --input data/plink/raw \\\n  --gwas data/gwas/raw \\\n  --outdir dnaprs \\\n  --run_name model1 \\\n  -resume\n```\n\nAdd one phenotype model from a phenotype table:\n\n```bash\nnextflow run . \\\n  -profile singularity \\\n  --input data/plink/raw \\\n  --gwas data/gwas/raw \\\n  --phenotype data/pheno/pheno.csv \\\n  --outcome depression_score \\\n  --covariates age,sex \\\n  --model_type gaussian \\\n  -resume\n```\n\nIf the participant-ID column cannot be matched uniquely to target IDs, also supply `--participant_id \u003ccolumn\u003e`.\n\nRepeated phenotype rows are supported through an explicit timepoint selection in\n`params.yml`, such as `timepoint_column: Timepoint` and `timepoint_values: [1]`. The input\nrows remain unchanged; the model uses one agreeing record per participant at the\nrequested value. Analysis values may differ between visits.\n\n## References\n\n`--reference_mode auto` is the default. The pipeline downloads only the required assets\nfrom a pinned catalogue, requires and verifies SHA-256 plus size for every asset, and\nreuses each valid asset from\n`references/dnaprs/grch37-v1/`. Beagle and unbref3 are handled in the same way.\n\nTo use an existing reference collection:\n\n```bash\nnextflow run . \\\n  -profile singularity \\\n  --references /path/to/rData \\\n  --reference_mode local \\\n  -resume\n```\n\nTo build or verify only the reusable reference cache, set `reference_only: true` in a\nsmall YAML parameter file and run with `-params-file`.\n\n## Configured and HPC runs\n\n`--input` and `--gwas` each accept either a conventional raw-data directory or a YAML\nlist of explicit records. The pipeline resolves both forms into validated TSV records\nfor internal hand-off and provenance. A GenomeStudio assay manifest is an input file\npaired with a FinalReport and is distinct from these generated records. Paths, GWAS\ncolumn roles, thresholds, selected methods, and multiple phenotype models can all be\ndeclared in YAML. See [the complete usage guide](docs/usage.md) and\n[`examples/params.yml`](examples/params.yml).\n\nProcesses use standard nf-core-style resource labels and pass `task.cpus` and bounded\ntask memory to capable tools. Nextflow handles cohort, chromosome-imputation, trait,\nmethod, reference, and model tasks concurrently, then validates deterministic gathers.\nScheduler, queue, project, and filesystem settings remain\noutside the pipeline, so the same workflow runs locally or through PBS Pro, Slurm, SGE,\nLSF, and other Nextflow executors. Site-specific launchers and module commands belong\noutside the portable pipeline repository.\n\nSBayesRC is a high-memory, long-running method. Use `--methods plink_ct` for a smaller\nrun; selecting fewer methods changes the selected analysis, not the executor.\n\n## Report and validation\n\nOpen `dnaprs/model1/reports/index.html` after completion. The report preserves every\navailable plot and provides SVG plus high-resolution PNG, TIFF, and JPEG downloads. Its\nLogs page displays execution artifacts in expandable, scrollable panels.\nWhen imputation is enabled, the PLINK page also reports typed-versus-imputed scoring\ncoverage and agreement between the primary imputed score and direct-genotype sensitivity\nscore; sensitivity scores are not added to phenotype models.\n\nRun the minimal real-tool pipeline test with Docker:\n\n```bash\nnf-test test tests/default.nf.test --profile +docker --ci\n```\n\nRun the full graph test explicitly in stub mode:\n\n```bash\nnf-test test tests/stub.nf.test --profile +test_full --ci\n```\n\nRun the focused early-stage and automatic-reference graph checks:\n\n```bash\nnf-test test tests/stages.nf.test tests/reference.nf.test --profile +docker --ci\n```\n\nThese are software-contract tests, not biological or clinical validation. Phenotype\nassociations are research estimates and do not make a PRS a clinical risk prediction.\n\n## Documentation\n\n- [Input, reference, phenotype, and run options](docs/usage.md)\n- [Output files and interpretation](docs/output.md)\n- [Pinned software environments](containers/README.md)\n- [Contributing and testing](docs/CONTRIBUTING.md)\n- [Citations](CITATIONS.md)\n\nThe code is released under the [MIT licence](LICENSE). External tools, GWAS data, and\nreference resources retain their own licences and access conditions.\n",
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