Utilising functional genomics data to improve outcomes for rare undiagnosed disease patients with neurological and psychiatric phenotypes

This thesis explores three approaches to understanding central nervous system disorders. It validates a variant of uncertain significance using CRISPR-edited iPSCs, demonstrating how transcriptomic analysis can inform rare disease diagnosis. It identifies cell-type-specific disease associations by analyzing developmental single-nucleus data from the prefrontal cortex, revealing critical timepoints for neurological and psychiatric disorders. Finally, it applies machine learning to ALS patient data, identifying molecular signatures that could improve diagnosis and reveal treatment targets. Together, these studies integrate computational and molecular methods to advance understanding of brain disorders and establish frameworks for improved diagnosis and therapy development.