Skip to content

Getting Started

What is CREDTOOLS?

CREDTOOLS (Credible Set Tools) is a comprehensive pipeline for performing statistical fine-mapping analysis across multiple ancestries and cohorts. It provides a unified framework for:

  • Quality Control: Assess the reliability of summary statistics and LD matrices
  • Meta-Analysis: Combine data across populations and cohorts
  • Fine-Mapping: Identify causal variants using multiple statistical methods
  • Post-Processing: Combine and interpret results across studies

The CREDTOOLS Framework

CREDTOOLS's workflow can be visualized as follows:

graph TD
    A[Multiple GWAS<br/>Summary Statistics] --> B[Quality Control]
    C[Multiple LD<br/>Matrices] --> B
    B --> D{Meta-Analysis Strategy}
    D -->|Cross-Ancestry| E[Cross-Ancestry Meta]
    D -->|Within-Ancestry| F[Within-Ancestry Meta]
    D -->|No Meta| G[Keep Separate]
    E --> H[Fine-Mapping]
    F --> H
    G --> H
    H --> I{Fine-Mapping Strategy}
    I -->|Single Input| J[Run on Each<br/>Study Separately]
    I -->|Multi Input| K[Run on Combined<br/>Studies Together]
    I -->|Post-hoc Combine| L[Run Separately<br/>Then Combine Results]
    J --> M[Results Integration]
    K --> M
    L --> M
    M --> N[Credible Sets &<br/>Posterior Probabilities]

Input Data

CREDTOOLS works with two main types of input data:

  • Summary Statistics: GWAS results containing effect sizes, standard errors, and p-values
  • LD Matrices: Linkage disequilibrium correlation matrices from reference panels

Meta-analysis Methods

CREDTOOLS supports three meta-analysis approaches:

  • meta_all: Combine all studies regardless of ancestry
  • meta_by_population: Combine studies within each ancestry separately
  • no_meta: Keep all studies separate

Fine-mapping Strategies

CREDTOOLS offers three complementary fine-mapping strategies:

  • Single Input (single_input)
    Use traditional fine-mapping tools that analyze one study at a time
    Best for: Well-powered single studies, ancestry-specific analysis
  • Multi Input (multi_input)
    Use tools designed to analyze multiple studies simultaneously
    Best for: Leveraging shared causal architecture across populations
  • Post-hoc Combine (post_hoc_combine)
    Run single-input tools on each study, then combine results
    Best for: Maximum flexibility and interpretability

Quality Control

CREDTOOLS provides comprehensive QC including:

  • Consistency checks: Kriging RSS to detect allele switches
  • LD structure: Eigenvalue decomposition and 4th moment analysis
  • Cross-study comparisons: Cochran's Q test for heterogeneity
  • Frequency comparisons: MAF consistency across studies

Output Files

CREDTOOLS generates:

  • Credible sets: Sets of variants likely to contain causal variants
  • Posterior inclusion probabilities (PIPs): Individual variant probabilities
  • QC reports: Detailed quality control metrics
  • Meta-analysis results: Combined summary statistics and LD matrices

When to Use CREDTOOLS

Use Cases

Multi-ancestry studies
Leverage power across populations while accounting for LD differences
Multiple cohorts per ancestry
Combine studies within ancestry groups for increased power
Heterogeneous effect sizes
Use post-hoc combination to preserve ancestry-specific signals
Quality control focus
Extensive QC metrics help identify problematic data

Considerations

  • Requires matched summary statistics and LD matrices
  • Computational requirements scale with number of studies
  • Some tools require specific data formats or parameter tuning

Next Steps

Now that you understand the CREDTOOLS framework, let's jump into a practical example:

👉 Quick Start Guide - Run your first CREDTOOLS analysis

Or explore specific scenarios: