Develop an integrative analysis method that allows for an increased diagnosis success rate. Identify and validate novel genes associated with Episodic Ataxia (EA). Evaluation of new AI-generated prediction algorithm for a broader and more complete variants examination.

Characterized by sporadic loss of voluntary movement coordination, EA typically manifest with a late onset as well as high clinical and genetic heterogeneity, setting additional hurdles to diagnosis. Eight subtypes of EAs have been described based on the causal genes or the clinical features. They usually exhibit autosomal dominant inheritance with variable penetrance. While five genes have been linked to EA, KCNA1 (EA1) and CACNA1A (EA2) accounting for a majority of the cases, many patients are left without molecular diagnosis due to the limitations of individual next-gen DNA-sequencing methods. The integration of RNA-sequencing data offers a functional overview of the genome that can mitigate those limitations.

EA patients lacking molecular diagnosis, despite in-depth examination and presence of variants of unknown significance from a known disease in some cases, were recruited in Montreal. Whole-Genome and RNA-sequencing was performed on blood sample extracts to identify variants, differential expression, splicing events and repeat expansion. Multiple recent pathogenicity prediction algorithms were chosen to be tested concurrently to standard ones for better results.

Identification of a candidate variants potentially causing a splicing event in two patients affected by EA. Preliminary results suggest the presence of an alternative splicing transcript that could lead to nonsense-mediated mRNA decay. 

This project should provide a more definitive diagnosis that will lead to better quality of life, better understanding of prognosis and better management of care for the patient. Good results with the integrative method will promote the approach for a larger spectrum of genetic disorders and might eventually lead to the development of new therapeutic strategies.