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  • Introduction to Glow
  • Getting Started
  • GWAS Tutorial
  • Benchmarks
  • Variant Data Manipulation
  • Tertiary Analysis
    • The Pipe Transformer for Parallelizing Command-Line Bioinformatics Tools
    • Spark as a Workflow Orchestrator to Parallelize Command-Line Bioinformatics Tools
    • Python Statistics Libraries
    • GloWGR: Whole Genome Regression
    • GloWGR: Genome-Wide Association Study (GWAS) Regression Tests
  • Troubleshooting
  • Contributing
  • Blog Posts
  • Additional Resources
  • Python API
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Tertiary Analysis

Perform population-scale statistical analyses of genetic variants.

  • The Pipe Transformer for Parallelizing Command-Line Bioinformatics Tools
    • Usage
    • Integrating with bioinformatics tools
    • Options
    • Cleanup
    • Examples
  • Spark as a Workflow Orchestrator to Parallelize Command-Line Bioinformatics Tools
    • When to use workflow orchestrator vs pipe transformer architecture
    • Example
  • Python Statistics Libraries
    • pandas example notebook
  • GloWGR: Whole Genome Regression
    • Performance
    • Overview
    • Data preparation
    • Stage 1. Genotype matrix blocking
    • Stage 2. Dimensionality reduction
    • Stage 3. Estimate phenotypic predictors
    • Proceed to GWAS
    • Troubleshooting
    • Example notebook
  • GloWGR: Genome-Wide Association Study (GWAS) Regression Tests
    • Linear regression
    • Logistic regression
    • Offset
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