Bioinformatic approaches to determine pathogenicity and function of clinical genetic variants across ion channels and neurodevelopmental disorder associated genes

Clinical genetic testing for rare monogenic diseases has the scope of identifying the disease-causing variants. Identification of the molecular etiology of the disease can already today improve clinical care and is essential for the administration of precision medicines that are currently in development for many disorders. However, distinguishing pathogenic variants from benign genetic variants remains a challenge – in particular for missense variants where a single amino acid is substituted. The effects of a pathogenic variant on the protein function, for example, whether it causes a gain (GoF) or a loss (LoF) of the protein function, is most of the time not understood since most genetic variants are ultra-rare and have not been molecularly tested. In particular, for genes associated with severe developmental disorders, first-generation symptomatic treatments offer often only limited relief. Consequently, the development and application of targeted treatments that promise improvement is urgently needed. Identifying the disease-causing pathogenic and predicting their function is crucial as targeted therapies can only be administered to patients with classified pathogenic variants whose functional effects are known to avoid adverse treatment outcomes. In this dissertation, I present bioinformatic approaches to enhance the assessment of variant pathogenicity and understanding of the functional effects of genetic variants. The developed approaches were applied on an exome-wide scale using public datasets and for selected disorders for which I had expert-curated clinical-genetic data available from collaborators. The major focus of this thesis is on genes implicated in neurodevelopmental disorders and diseases associated with ion channel dysfunction for which collaboration with other research groups enabled the aggregation of required genetic, clinical, and functional datasets to develop and test the bioinformatic approaches. In the first study (Bruenger and Ivaniuk et