MOKA: a pipeline for multiomics bridged SNP-set kernel association test
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".