GEVAT: Genotype-Embedding-Weighted Variance-Component Test

GEVAT (Genotype-Embedding-Weighted Variance-Component Test) is a framework that leverages variational autoencoders (VAE) to learn latent genotype representations, facilitating representation-informed association mapping and phenotype classification. This approach addresses limitations of traditional GWAS by accounting for polygenicity, epistatic interactions, and linkage disequilibrium.

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1. Clone the GEVAT repository:

git clone https://github.com/davidenoma/gevat.git

2. Clone the MOKA pipeline (required for association mapping):

git clone https://github.com/davidenoma/moka.git  ~/moka

3. (Recommended) Create a new Python 3.9 environment:

4. Install Python dependencies:

conda create -n gevat_env python=3.9
conda activate gevat_env
conda install --file requirements.txt

conda install --file requirements.txt

#### Using pip
```bash
pip install -r requirements.txt

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, the following Python package versions were used to ensure reproducibility:
numpy 1.21.6, pandas 1.3.5, tensorflow 2.6.0, keras 2.6.0, hyperopt 0.2.7, matplotlib 3.4.3, scikit-learn 0.24.2, xgboost 1.5.2, shap 0.39.0, seaborn 0.11.2, and pyyaml 5.4.1.
These versions are specified in the requirements.txt file and should be installed to replicate the computational environment used in this study.

For the benchmarking and method comparisons reported in this work, we used the following external tools and versions: REGENIE v2.2.4 — used for single-variant and whole-genome regression-based association comparisons (https://github.com/rgcgithub/regenie). SKAT R package v2.0.5 — used for sequence/kernel-based association testing comparisons (available via CRAN/Bioconductor). These versions were selected to ensure reproducibility of the results

• PLINK - For genotype data processing
• MOKA Pipeline - For association mapping

export PATH=$PATH:/path/to/plink

Your input genotype data must be in PLINK binary format (.bed, .bim, .fam files).

The framework supports both:

• Case-control studies (binary phenotypes)
• Quantitative traits (continuous phenotypes)

python runner/gevat_main.py test_geno/test_geno.raw test_geno/test_geno.bim cc

Parameters:

• test_geno/test_geno.raw: Path to PLINK raw format genotype file

plink --recode A --bfile genotype_file --out genotype_file

• test_geno/test_geno.bim: Path to corresponding BIM file
• <phenotype_type>: Either "cc" (case-control) or "quantitative"

Output:

• Trained VAE model saved in model/ or model_cc_com_qt/
• Reconstruction metrics (MSE and R²)
• Encoder and decoder weights extracted to output_weights/

python runner/gevat_XAI_main.py test_geno/test_geno.raw test_geno/test_geno.bim cc

Features:

• Hyperparameter optimization using Hyperopt
• SHAP (Shapley Additive exPlanations) for explainable AI
• Feature importance extraction
• Reconstruction quality metrics

Output:

• SHAP values for feature importance: model_outputs/hopt_AE/shap_values_*/

• Merged SNP weights: *_merged_snp_and_weights.csv

• Visualization plots: SHAP bar plots

• CV and hyperopt results saved under model_outputs/cv/ and model_outputs/hyperopt/.

For comprehensive analysis including association mapping:

python single_folder_reconstruction_and_moka.py <folder_path> [options]

Options:

• --quantitative: For quantitative traits (default: binary/case-control)
• --reconstruction: Enable genotype reconstruction
• --plink-path <path>: Specify the path to the PLINK binary (default: plink in PATH)

Example:

# Binary trait analysis with default PLINK and enabling reconstruction of 
python single_folder_reconstruction_and_moka.py /path/to/genotype_folder --reconstruction

# Quantitative trait analysis
python single_folder_reconstruction_and_moka.py /path/to/genotype_folder --quantitative

# Without genotype reconstruction, which is faster after model training 
python single_folder_reconstruction_and_moka.py /path/to/genotype_folder 

# Specify custom PLINK binary location
python single_folder_reconstruction_and_moka.py /path/to/genotype_folder --plink-path /usr/local/bin/plink

• The folder name must match the genotype file prefix. For example, if your genotype files are named test_geno.bed, test_geno.bim, and test_geno.fam, they must be placed in a folder named test_geno/.
• Example structure:

  test_geno/
    ├── test_geno.bed
    ├── test_geno.bim
    └── test_geno.fam
  

Remember to clone the MOKA pipeline (required for association mapping):

git clone https://github.com/davidenoma/moka.git  ~/moka

• If you do not provide --plink-path and PLINK is not found in your system PATH, the pipeline will automatically download and unzip PLINK for you (Linux/macOS supported).
• You can still specify a custom PLINK binary using --plink-path if needed.

# With genotype reconstruction
python single_folder_reconstruction_and_moka.py test_geno --reconstruction

# For quantitative trait model
python single_folder_reconstruction_and_moka.py test_geno --quantitative

# Run the pipeline on the provided test_geno example (binary trait)
python single_folder_reconstruction_and_moka.py test_geno

# Specify custom PLINK binary location
python single_folder_reconstruction_and_moka.py test_geno --plink-path /usr/local/bin/plink

Note: If you do not specify --plink-path and PLINK is not installed, the script will automatically download and use PLINK.

Workflow:

1. Data Preprocessing:

   • LD pruning using PLINK (--indep-pairwise 50 5 0.2)
   • Genotype folder must bear the same name as the genotype ( bed, bim and fam ) files e.g.
     • test_geno/
       • test_geno.bim
       • test_geno.fam
       • test_geno.bed
   • Conversion to raw format
   • Missing value imputation

2. Model Training:

   • VAE and Autoencoder training
   • Weight extraction (encoder, decoder, combined)
   • SHAP analysis for feature importance

3. Association Mapping:

   • Integration with MOKA pipeline
   • Multiple weight types analysis:
     • enc: Encoder weights
     • dec: Decoder weights
     • enc_dec: Combined weights
     • shap: SHAP-based weights

4. Results Generation:

   • GWAS results for each weight type
   • Manhattan plots
   • Merged association results

├── model/                          # Trained models (case-control)
├── model_cc_com_qt/               # Trained models (quantitative)
├── model_outputs/                 # Analysis results
│   ├── hopt_AE/                   # Autoencoder results
│   │   └── shap_values_*/         # SHAP analysis
│   └── gevat_classifier/           # Classification results
├── output_weights/                # Extracted weights
│   ├── hopt/                      # Binary trait weights
│   └── hopt_cc_com_or_quant/     # Quantitative trait weights
└── moka_pipeline/                  # MOKA pipeline results
    ├── result_folder/                   # Association results
    └── output_plots/                # Manhattan plots

If you use GEVAT in your research, please cite.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

This project is licensed under the MIT License - see the LICENSE file for details.

For questions and support:

• Open an issue on GitHub
• Contact: david.enoma@ucalgary.ca or quan.long@ucalgary.ca

• UK Biobank, dbGaP, and WTCCC for providing genetic data
• DisGeNET for disease-gene association validation
• The MOKA pipeline for association mapping framework