# MOKA: a pipeline for multiomics bridged SNP-set kernel association test

## TL;DR

MOKA is a new pipeline that integrates multiomics data into GWAS to improve variant prioritization and biological relevance of results.

## Contribution

MOKA introduces a scalable, automated pipeline for multiomics-integrated SNP-set kernel association testing with enhanced GWAS power.

## Key findings

- MOKA identified 89 Bonferroni-significant genes in a schizophrenia GWAS cohort.
- The pipeline achieved a 15.7% validation rate in the DisGeNET database.
- Results were enriched in pathways relevant to neuropsychiatric disease.

## Abstract

The explosion of genomic and multiomics data has created a need for scalable, reproducible tools that integrate functional annotations into genome-wide association studies (GWAS). We introduce the multiomics data bridged Kernel Association test (MOKA) pipeline, a Snakemake-based workflow that automates SNP-set kernel-based association testing by incorporating multiomics data, including gene expression, transcription factor binding, evolutionary conservation scores, and neural network-derived features. This data-bridged architecture enhances variant prioritization and aggregation, improving statistical power in GWAS. MOKA supports population structure correction via spectral decomposition, parallel computation, and post-GWAS analyses, including visualization, Gene Ontology annotation, pathway enrichment, and validation. As a use case, we applied MOKA to a schizophrenia GWAS cohort, identified 89 Bonferroni-significant genes, with a 15.7% validation rate in the disease-specific DisGeNET database and enrichment in pathways relevant to neuropsychiatric disease. MOKA provides a robust, scalable, and extensible framework for functional multiomics integration in genetic studies. It is open-source and available at https://github.com/davidenoma/moka.

Enoma et al. present MOKA, an automated Snakemake pipeline that integrates multi-omics annotations such as gene expression, transcription factor binding, evolutionary conservation, and neural network-derived features into SNP-set kernel association tests. MOKA enhances GWAS discovery power and reduces genomic inflation with optional spectral decomposition. Applied to schizophrenia GWAS data, MOKA identified 89 significant genes with improved biological relevance and a 15.7% validation rate in DisGeNET, providing a robust and reproducible framework for post-GWAS analysis.