🌉 Multi-omics bridged Kernel Association test (MOKA) Pipeline

MOKA implements a Snakemake pipeline to automate data bridge kernel-based association tests. This pipeline offers flexibility of GWAS analysis & visualizations with different multi-omics variant specific weights. Publication available at: https://www.medrxiv.org/content/10.1101/2025.07.06.25330974v1

🚀 Usage

To run the moka pipeline:

1.Minimal data inputs

-GWAS genotype files in PLINK format (bed, bim & fam)

-Variant specific weights for each SNP ('SNP_ID, CHROMOSOME, POSITION, WEIGHT)

2. Install Snakemake
   • Snakemake Installation Guide

     conda install -n base -c conda-forge mamba
     mamba create -c conda-forge -c bioconda -n snakemake
     mamba activate snakemake
     snakemake --help

3. Install Plink, Python & R (Rscript configure) !important Check Dependencies section
4. Download and install moka

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

5. Configure the pipeline parameters in the config.yaml file.
6. Execute the pipeline using the software:

📚 Rules

Rule: moka association_test

• Input: Preprocessed genotype data and weight files.
• Output: Results of association tests.

snakemake --cores <num_cores>

If you do not have all the dependencies with Python and R you can get it configured on conda, utilize with:

snakemake --cores <num_cores> --use-conda

However, some R packages are not available to best to be installed R package manager.

Rule: merge_results

• Input: Individual association test results.
• Output: Merged association test results.

snakemake --cores 1 merge_moka_results

Rule: annotate_results

• Input: Merged association test results.
• Output: Annotated association test results with DisGeNet database

snakemake --cores 1 disgenet_annotation_005

Rule: visualize_results

• Input: Merged association test results.
• Output: Manhattan plots with visual representations of association test results.

snakemake --cores 1 manhattan_plots

Rule: go_analysis

• Input: Merged association test results.
• Output: GO analysis results.

snakemake --cores 1 go_analysis

Rule: kegg_pathway_analysis

• Input: Merged association test results.
• Output: KEGG pathway analysis results.

snakemake --cores 1 kegg_pathway_analysis

Rule: Skat test with linear kernel

• Input: Genotype
• Output: results for association mapping, folder: output_association/

snakemake --cores 22 skat

Dependencies

Software

They must be configured on your path

• Snakemake (8.0.1+)
• R(4.2.0+)
• Python (3.9+)
• PLINK (1.9+): [https://www.cog-genomics.org/plink/1.9/]
• Rscript

Python Packages

• FaST-LMM Factored Spectrally Transformed Linear Mixed Models, is a program for performing genome-wide association studies (GWAS) on datasets of all sizes
• PySnpTools PySnpTools is a library for reading and manipulating genetic data.

pip install pysnptools fastlmm

R Packages

• manhattan: R package for creating manhattan plots, commonly used in genome-wide association studies (GWAS).
• SKAT: R package for SKAT (Sequence Kernel Association Test) which is a powerful gene-based association test.
• QQMAN: R package for creating QQ (Quantile-Quantile) plots, commonly used in GWAS to assess whether observed p-values deviate from the expected distribution under the null hypothesis.
• GGPLOT: R package for creating highly customizable plots and graphics.
• gprofiler2: R package for gene set enrichment analysis (GO analysis).
• pathfindR: R package for pathway analysis, including KEGG pathway analysis.

Installation steps:

install.packages(c("BiocManager","SKAT","ggplot2"))
BiocManager::install(c( "gprofiler2", "pathfindR","manhattan","qqman"))

Other Software

• Parallel: Linux Parallel GNU : https://www.gnu.org/software/parallel/

apt install parallel #linux or WSL windows
brew install parallel #macos

Input file format

• Data Files: Plink https://www.cog-genomics.org/plink/1.9/ format genotyped BIM, BED & FAM files [!required]
• Multi-omics Bridge weights.csv file (SNP_ID,Chromosome,Position,Weight) [!required for moka]
• Gene regions file provied in GRCh38 or hg38. (Genome Research Consortium Human Build 38)
• DisGeNET gene disease database reference file ( If disease external validation needed)

Liftover protocol

You much lift over to GRCh38 format check here: Liftover GWAS: [https://github.com/davidenoma/LiftOver]

📋 Configuration

• genotype_prefix: Prefix for genotype data files.
• weights_type: Text string for type of bridge weights to be used e.g. "eqtl", "imaging"
• genotype_file_path: Path to genotype data files.
• weight_file: Path to weight files used for association tests.
• disgenet_reference_file: External disease database specific gene-disease associations from https://disgenet.org [For gene disease associations only!]
• spectral decomposition: Flag for performation decomposition and transformation of genotype and phenotype, default: TRUE
• is_binary: Flag for binary/ quantitative trait, default: TRUE
• Plink: Path to plink installation e.g. "~/software/plink"

📖 Additional Information

For more information on the MOKA pipeline and its usage, refer to the documentation provided in the repository or contact the project maintainers. david.enoma@ucalgary.ca

Publication reference

MOKA: A pipeline for multi-omics bridged SNP-set kernel association test https://www.medrxiv.org/content/10.1101/2025.07.06.25330974v1