SNP Interactions and Their Contribution to Bicuspid Aortic Valve Pathogenesis

Background: Bicuspid Aortic Valve (BAV) is the most common congenital heart defect, affecting 1–2% of the population, characterized by two aortic valve leaflets instead of three. The clinical spectrum varies between silent disease, valve insufficiency, early stenosis, and aortic dilatation. Despite its prevalence and significant heritability index, BAV pathogenesis is not fully understood. While a few genes are linked to BAV, they explain only a fraction of cases. This study aims to explore the genetic architecture of BAV using bioinformatic tools to analyze Single Nucleotide Polymorphisms (SNPs) and their combinations in a global patient cohort.

Methods: Data from the Bicuspid Aortic Valve Consortium (BAVCon), including 2,971 BAV cases and matched controls, were analyzed to identify SNP interactions contributing to BAV. A dominant inheritance model was used to discover frequent SNP sets associated with BAV. We used bioinformatic tools, particularly the Apriori algorithm to look for SNP interatsion. To enhance robustness, linkage disequilibrium (LD) pruning reduced an initial dataset of 172,326 SNPS to 16,601 SNP combinations.

Results: The Apriori algorithm identified 161 significant association rules, of which 61 were prioritized based on biological relevance. These findings revealed distinct SNP combinations differentiating BAV patients from controls. For example, Notably, male- and female-specific patterns were observed, emphasizing the importance of gender-specific genetic frameworks.

Conclusion: We used bioinformatic tools to identify 61 NSPs interactions that are significantly more prevelant in BAV patients. These suggest potential for Long-range genetic interactions and complex regulatory mechanisms across distant genomic regions contributing to BAV. .This research highlights the potential of the Apriori algorithm and similar computational methods in studying diseases with complex inheritance, offering an approach to explore epistasis and SNP interactions. Given BAV`s prevalence and clinical impact, understanding its genetics is essential for improving diagnostics, treatments, and patient outcomes.