Validation of a Clinical Algorithm for the Diagnosis of Recessive Ataxias
Validation of a Clinical Algorithm for the Diagnosis of Recessive Ataxias
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.
Design: Retrospective study based on clinico-genetic data. Total research duration: 22 months
 Plan of the study : More than 150 patients referred for ARCA suspicion have been analyzed by
 NGS (Montpellier platform) since September 2013. The clinical data of these patients will be
 entered at the computer level into the algorithm to obtain the prediction of the gene
 involved (under the form of a probability for each of the genes known to ARCA). The
 investigators will compare this result with that obtained by standard NGS analysis. For
 patients without molecular diagnosis defined after standard NGS analysis, the investigators
 will take the 5 most probable genes selected on the basis of the algorithm in order to carry
 out an in-depth analysis of the variants found and a bioinformatic analysis by semi-automatic
 detection of CNVs. Main Evaluation Criterion: Concordance of the algorithm with standard NGS
 analysis for the genetic diagnosis of ARCA. Secondary evaluation criterion:% of patients for
 whom the prediction based on the algorithm suggested the correct diagnosis (subsequently
 confirmed after revision in the detail of the genetic data) while the standard genetic
 analysis was not not informative. Statistical Analysis: The investigators will perform a
 concordance analysis (Cohen's k) to validate the algorithm in clinical practice. For a given
 patient, there will be concordance if: i) one of the first 5 genes predicted with the highest
 probability by the algorithm is compatible with the mutation found after standard NGS or ii)
 if no gene is predicted by the algorithm (score <20) and no mutation found by NGS analysis.
Inclusion criteria:

 - having a predominant phenotype of pure or complex cerebellar ataxia of probable genetic
 origin (after exclusion of acquired, inflammatory, tumoral, infectious or toxic forms) -
 having started before the age of 50; - evoking a modality of recessive transmission
 (sporadic cases, brothers or sister affected, consanguinity of the parents) - in which
 dominant genetic forms due to CAG expansion (SCA1, SCA2, SCA3, SCA6, SCA7) and pre-mutation
 of the FMR1 gene (if onset after age 45)

 Exclusion criteria:

 - objecting to the computer processing of the data contained in the medical file.