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Defining the genetic etiology of late onset episodic ataxias

We want to present the research of Martine Tétreault

Assistant professor
Department of neuroscience, University of Montreal

The identification of the genetic causes and mechanisms associated with rare Mendelian diseases has always been an important challenge for the medical field. The identification of mutations and/or genes associated with a disease has forever been driven by technological progress. High-throughput DNA-sequencing technology has led to the identification of a large number of disease-causing variants or genes. Although, this technology succeeded in improving the diagnosis of rare diseases, there is a significant number of patients that remain without a molecular diagnosis. For most patients, a diagnostic test will be inconclusive since many candidate variants with no clear functional impact are identified. Clinical tests are usually targeting a single gene or a panel of genes and thus are not able to identify variants in potential novel-disease genes. Moreover. our, current knowledge and tools available limit our capacity to interpret these variants. For these reasons, the diagnostic yield is reported to be25-30% in several large studies. The integration of functional genomic data. such as RNA-sequencing, is gaining in popularity to improve variants interpretation and increase our knowledge of molecular mechanisms associated with diseases. ln addition to the technical limitations, neurological diseases complexity is increased by the high variability in clinical presentation and genes involved. This variability is not only observed between unrelated patients but also within a single family. The study of late-onset diseases is even more challenging since the familial history is often unclear preventing the identification of additional affected cases that could confirm the genetic nature of the disease as well as the mode of transmission. Episodic ataxias are a group of neurodegenerative diseases characterized by recurrent attacks of ataxia in which high variability is observed. To date, eight forms have been described but there are still patients who remain without a known genetic cause. In this project, we  propose to combine genomic and transcriptomic data to identify the genetic cause in a cohort of unresolved late-onset episodic ataxia patients. Using this approach, we will be able to detect a large spectrum of possible aberrations. Our results will have a direct impact on patients by increasing the diagnostic yield of episodic ataxia as well as contributing to our understanding of the biological mechanisms leading to the disease. The identification of genetic causes as well as deregulated pathways will ultimately lead to the development of novel therapeutic strategies.

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