credtools 0.3.0

multi-ancestry fine-mapping pipeline.

credtools

Multi-ancestry fine-mapping pipeline.

• Documentation: https://Jianhua-Wang.github.io/credtools
• GitHub: https://github.com/Jianhua-Wang/credtools
• PyPI: https://pypi.org/project/credtools/
• Free software: MIT

Features

• Whole-genome preprocessing: Start from raw GWAS summary statistics and genotype data
  • Standardize and munge summary statistics from various formats
  • Prepare LD matrices and fine-mapping inputs automatically
• Multi-ancestry fine-mapping: Support for multiple fine-mapping tools (SuSiE, FINEMAP, etc.)
• Meta-analysis capabilities: Combine results across populations and cohorts
• Quality control: Built-in QC metrics and visualizations
• Command-line interface: Easy-to-use CLI for all operations

Installation

Basic Installation

pip install credtools

Install with uv

uv pip install credtools

Quick Start

Command Line Usage

# Complete workflow: from whole-genome data to fine-mapping results
# Step 1: Standardize summary statistics
credtools munge raw_gwas_eur.txt,raw_gwas_asn.txt output/munged/

# Step 2: Identify independent loci and chunk data
credtools chunk output/munged/*.munged.txt.gz output/chunks/

# Step 3: Prepare LD matrices and final inputs
credtools prepare output/chunks/chunk_info.txt genotype_config.json output/prepared/

# Step 4: Run fine-mapping pipeline
credtools pipeline output/prepared/final_loci_list.txt output/results/

Preprocessing Workflow

credtools now supports starting from whole-genome summary statistics and genotype data, eliminating the need for manual preprocessing:

Step 1: Munge Summary Statistics (credtools munge)

• Purpose: Standardize and clean GWAS summary statistics from various formats
• Features:
  • Automatic header detection and mapping
  • Data validation and quality control
  • Support for multiple file formats
• Input: Raw GWAS files with various column headers
• Output: Standardized .munged.txt.gz files

Step 2: Chunk Loci (credtools chunk)

• Purpose: Identify independent loci and create regional chunks for fine-mapping
• Features:
  • Distance-based independent SNP identification
  • Cross-ancestry loci coordination
  • Configurable significance thresholds
• Input: Munged summary statistics files
• Output: Locus-specific chunked files and metadata

Step 3: Prepare Inputs (credtools prepare)

• Purpose: Generate LD matrices and final fine-mapping input files
• Features:
  • LD matrix computation from genotype data
  • Variant intersection and quality control
  • Multi-threaded processing
• Input: Chunked files + genotype data configuration
• Output: credtools-ready input files (.sumstats.gz, .ld.npz, .ldmap.gz)

Multi-Ancestry Support

• Consistent loci definition: Union approach across ancestries
• Flexible input formats: Support for various GWAS summary statistics formats
• Coordinated processing: Ensure compatibility across populations

Documentation

For detailed documentation, see https://Jianhua-Wang.github.io/credtools