Implementation of Apriori Algorithm to Biomedical Data: Silent Mutations in GWAS-GAD Edition

Genome-wide association studies (GWAS) have revolutionized our understanding of genetic contributions to complex diseases by identifying single-nucleotide polymorphisms (SNPs) associated with disease predisposition. Despite the substantial progress made in identifying risk factors for conditions like cancer and cardiovascular diseases, interpreting the functional impact of identified variants remains a challenge, particularly when silent mutations are involved. Silent mutations, once considered irrelevant to disease mechanisms, have emerged as significant players influencing mRNA formation, splicing, and translation processes. This study utilized the Genetic Association Database (GAD) to analyze and identify the significance of silent mutations across a wide range of diseases, employing advanced machine learning techniques and the Apriori algorithm to extract association rules from a biomedical dataset. The Apriori algorithm was applied to identify strong correlations between diseases and chromosomes, using parameters such as support, confidence, and lift to evaluate the strength and importance of these associations. Our results demonstrated the capability of the Apriori algorithm to uncover biologically meaningful relationships, which could be instrumental in improving our understanding of genetic predispositions and guiding precision medicine efforts. These findings underscore the importance of silent mutations in disease etiology and highlight the potential of bioinformatics tools in unraveling complex genetic interactions.