Genetic Mutation Analysis for Disease Prediction

Genetic mutations influence disease susceptibility across oncology, cardiometabolic, and neurodegenerative disor- ders. Yet, predicting which variants are truly pathogenic remains difficult due to polygenicity, epistasis, and data sparsity. We present an end-to-end framework for Genetic Mutation Analysis for Disease Prediction that integrates variant calling quality control, functional annotation, feature engineering across genes and pathways (including polygenic and burden summaries), and supervised learning with calibrated risk outputs. On a represen- tative hereditary-disease cohort, our approach improves accuracy and precision over single-variant baselines while preserving interpretability via feature importance and SHAP analyses. We further outline evaluation rigor (bootstrapped confidence inter- vals, AUC comparison), discuss ethical safeguards for clinical deployment, and position this pipeline relative to emerging genomic foundation models. The results highlight how multi- variant signals combined with model interpretability can support earlier detection and personalized care.