The field of clinical diagnosis of recessive cerebellar ataxias (ARCA) is particularly complex and Next Generation Sequencing (NGS) techniques have revolutionized this neuro-genetic field. The current challenge is to optimize the analysis of genetic data generated by NGS because: the processing of data remains very laborious; diagnostic yeld less than 50%; the interpretation of the variants sometimes very difficult. For this purpose of optimization, the team of the University Hospital of Strasbourg has developed a computer algorithm based on 124 clinical and para-clinical parameters (derived from the data of the literature), useful to guide the genes to be targeted in priority by genetic analysis, in the context of a suspicion of ARCA (\> 60 known genes); this algorithm was validated retrospectively in 834 patients with genetically confirmed ARCA (92% Sense, 95% Spec). However, these 834 patients are often the same as those described in the literature and used for the elaboration of the algorithm. This introduces a bias in the initial evaluation of the algorithm, which therefore requires validation in clinical practice, from a cohort of patients referred for suspected ARCA (with or without a found genetic mutation). At the same time, Montpellier's genetics laboratory has developed a bioinformatic method for the search for copy number variations (CNV) that can be applied in a targeted manner to the genes predicted by the algorithm. The principal aim of this study is the validation of a semi-automated clinical algorithm for NGS molecular diagnosis of ARCA; the secondary objective is to evaluate if the application of this algorithm coupled with a targeted bioinformatic analysis can increase the diagnostic yield of the NGS analysis.
Age range
18 Years
Sex
ALL
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Agreement between the prediction of the algorithm and the result of the standard NGS analysis
Timeframe: 1 day