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CREDTOOLS

CREDTOOLS helps you go from GWAS summary statistics to fine-mapping results. It handles the boring parts first: clean the files, split the genome into loci, build matched LD inputs, run QC, and then run a fine-mapping tool.

If you are new, start with the path below. If you already have prepared locus files, jump straight to the existing-loci tutorial.

The Short Version

graph LR
    A[Raw GWAS summary stats] --> B[munge]
    B --> C[chunk + LD extraction]
    C --> D[pipeline]
    D --> E[PIPs + credible sets]
    D --> F[QC reports]
    F --> G[plots]

Most projects use this workflow:

credtools munge population_config.tsv work/munged --force
credtools chunk work/munged/sumstat_info_updated.txt work/chunks --threads 4
credtools pipeline work/chunks/loci_list.txt work/results --tool susie
credtools plot work/results/<locus_id> --type locusplot --output work/results/locus.png

Do not memorize the whole workflow yet

The docs are written so you can follow one page at a time. Start with the quickstart, then come back to the guide pages when you need to make choices.

What CREDTOOLS Produces

The main outputs are easy to recognize:

  • pips.txt.gz: one row per variant, with posterior inclusion probabilities.
  • credible_sets_summary.txt.gz: one row per credible set.
  • causal_variants.txt.gz: variants that belong to at least one credible set.
  • QC tables such as expected_z.txt.gz, dentist_s.txt.gz, and compare_maf.txt.gz.
  • Optional plots from credtools plot.

Where to Go Next

  1. Read Core Concepts if terms like LD, locus, PIP, or credible set are still fuzzy.
  2. Run Quickstart to check your installation.
  3. Use Input Files when you are preparing your own data.
  4. Read Known Limitations before a large run.
  5. Use Choosing a Fine-Mapping Tool before running a large analysis.
  6. Keep File Schemas open when creating inputs by hand.