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bioRxiv · 10.64898/2025.12.27.696666

Monte Carlo Simulation Analysis of Variant Burden Prompts Potential Oligogenic Interactions

Pata, V.·Marandi, M.·Huik, J. M.·Ounap, K.·Pajusalu, S.
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Abstract

BackgroundA large fraction of patients with suspected rare genetic disorders remain undiagnosed, suggesting that complex inheritance patterns, which are poorly detected by standard monogenic tests, may be a key factor. MethodsWe developed a computational framework that uses Monte Carlo simulations on summary-level allele counts to test for pathway-level burden enrichment. This approach was applied to a cohort of 639 neuromuscular disease (NMD) patients and 9,059 controls from the Tartu University Hospital (2015-2023) as proof of concept and compared with standard burden testing. ResultsWhile standard per-gene Z-tests failed to yield genome-wide significant results, our simulation framework identified a nominal enrichment of rare, moderate-impact variants within a predefined set of NMD-associated genes (Bonferroni-corrected empirical p = 0.02). Exploratory analysis of the most significantly burdened gene sets revealed frequent co-occurrence of CAPN3 with other critical NMD genes, including CLCN1, GFPT1, and MYOT. ConclusionOur findings suggest a potential oligogenic model in NMD, where variants in a hub gene like CAPN3 may interact with other rare variants to contribute to disease. This Monte Carlo simulation framework needs further validation but demonstrates that this framework may be a useful tool for hypothesis generation in complex genetic disorders. FundingEstonian Research Council grants PSG774 and PRG2040. Availabilitypublicly available under CC BY-NC-SA 4.0 licence at https://github.com/OligoGeneticDiseases/gen-toolbox

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Pata, V., Marandi, M., Huik, J. M., Ounap, K., Pajusalu, S.. 2025-12-27. Monte Carlo Simulation Analysis of Variant Burden Prompts Potential Oligogenic Interactions. https://doi.org/10.64898/2025.12.27.696666

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