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Welcome to edge-gwas Documentation

EDGE-GWAS (Elastic Data-Driven Encoding GWAS) identifies nonadditive SNP effects using flexible genetic encoding, rather than assuming additive inheritance.

Warning

⚠️ Current Version 0.1.2 - Under Public Testing

Recommended to use v0.1.2 - more stable and more functions.

Note

The original EDGE implementation (v0.0.0) is available at https://github.com/nicenzhou/EDGE. Version 0.0.0 is no longer maintained; users are encouraged to migrate to v0.1.0+.

Key Features

  • Two-stage analysis: Calculate alpha on training data, apply to test data

  • Flexible encoding: Detects under-recessive, recessive, additive, dominant, and over-dominant effects

  • Multiple outcomes: Binary and quantitative traits

  • Multiple genotype formats: PLINK binary (.bed/.bim/.fam), PLINK2 (.pgen/.pvar/.psam), BGEN, and VCF/VCF.GZ files

  • Visualization: Manhattan, QQ, and alpha distribution plots

Quick Start

from edge_gwas import EDGEAnalysis
from edge_gwas.utils import load_plink_data, prepare_phenotype_data

# Load data
geno, info = load_plink_data('data.bed', 'data.bim', 'data.fam')
pheno = prepare_phenotype_data('pheno.txt', 'disease', ['age', 'sex'])

# Run analysis
edge = EDGEAnalysis(outcome_type='binary')
alpha_df, gwas_df = edge.run_full_analysis(
    train_geno, train_pheno, test_geno, test_pheno,
    outcome='disease', covariates=['age', 'sex']
)

Support

Contents:

See Also

Documentation:

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Last updated: 2026-02-10 for edge-gwas v0.1.2

For questions or issues, visit: https://github.com/nicenzhou/edge-gwas/issues