This doctoral thesis investigates the application of computational approaches to characterize the phenotypic spectrum of two rare synaptic genetic epilepsies. As the pace of gene discovery accelerates, phenotyping remains a critical bottleneck in understanding the clinical impact of rare variants. Using BSN and DLG4 as exemplar genes, this work illustrates how structured frameworks, and computational methods can be leveraged to bridge the phenotyping gap.Through comprehensive analysis of BSN variants, I identified a novel neurodevelopmental disorder with variable expressivity and incomplete penetrance. I collected and analyzed phenotypic data from individuals with de novo and rare BSN variants, revealing age-related differences in presentation and unique phenotypic signatures. Using the Human Phenotype Ontology framework, I performed comparative phenotyping and similarity analyses to establish a gene-phenotype signature for BSN-related disorders. For DLG4, a more extensively studied but still rare synaptic epilepsy gene, I characterize the variant spectrum and phenotypic features across 101 individuals, highlighting developmental delays, motor coordination deficits, and seizure-related outcomes. Our longitudinal analysis reveals sex and genotype-based variations in milestones and provides insights into treatment responses, refining the clinical profile of DLG4-related disorders. This work demonstrates that computational phenotyping approaches can effectively capture the complexity of rare genetic epilepsies, providing a framework for better understanding synaptic dysfunction in epileptogenesis and potentially informing future diagnostic and therapeutic strategies. The methodologies developed here can be extended to other rare genetic epilepsies, advancing our ability to prognosticate and tailor treatments for patients with these challenging disorders.