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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The increasing volume of genomic and multi-omics data necessitates advanced tools for Genome-Wide Association Studies (GWAS).
• Integrating functional annotations is crucial for enhancing the power and interpretability of GWAS.
• Existing methods often lack scalability and reproducibility when handling diverse functional data types.

Purpose of the Study:

• To introduce the multi-omics data bridged Kernel Association test (MOKA) pipeline.
• To provide a scalable and reproducible workflow for integrating multi-omics data into GWAS.
• To improve variant prioritization and statistical power in genetic association studies.

Main Methods:

• Developed MOKA, a Snakemake-based workflow for SNP-set kernel-based association testing.
• Incorporated diverse multi-omics data: gene expression, transcription factor binding, conservation scores, and neural network features.
• Implemented population structure correction, parallel computation, and comprehensive post-GWAS analyses (visualization, GO annotation, pathway enrichment).

Main Results:

• Applied MOKA to a schizophrenia GWAS cohort, identifying 89 Bonferroni-significant genes.
• Achieved a 15.7% validation rate using the DisGeNET database.
• Observed enrichment in pathways relevant to neuropsychiatric diseases, demonstrating MOKA's utility.

Conclusions:

• MOKA offers a robust, scalable, and extensible framework for functional multi-omics integration in genetic studies.
• The pipeline enhances variant prioritization and statistical power in GWAS.
• MOKA is open-source, facilitating broader adoption in genetic research.