A Genetic Algorithm-Driven Personalized Genome Mutation Pathway Predictor for Early Diagnosis of Rare Polygenic Disorders

Abstract

Accurate prediction of rare polygenic disorders remains a significant chal-lenge in precision medicine, primarily due to the fact that they involve a complicated genetic architecture and current computational models are re-stricted. Traditional polygenic risk scores (PRS) have additive assumptions and finite cross-population validity and hence are not appropriate for rare disorders. In this study, a novel GA-based approach is presented that models individualized forward mutational routes, enabling early identification of risk genomic configurations. Each GA chromosome represents a binary vector of rare variants from whole-genome sequencing data, and evolutionary processes are guided by a composite fitness function. The function integrates pathogenicity scores, disease associations, and population rarity to yield biologically relevant simulations. Using 1000 Genomes Project data, we simulate 500 mutational trajectories in 500 different individuals. Results determine an average 27.2% increase in pathogenicity and 38.4% increase in harmful variants, with more than 60% convergence to known disease profiles in European and South Asian genomes. Approximately 24% of simulated genomes per individual exceed high-risk thresholds, outperforming PRS in identifying non-additive and epistatic effects. This GA strategy offers a dynamic, ancestry-aware approach to predicting rare disease risk, broadening the scope of predictive genomics and enabling earlier, more specific clinical interventions.