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Validation of a clinical practice-based algorithm for the diagnosis of autosomal recessive cerebellar ataxias based on NGS identified cases

Journal of Neurology volume 263, pages 1314–1322 (2016)Cite this article

Abstract

Establishing a molecular diagnosis of autosomal recessive cerebellar ataxias (ARCA) is challenging due to phenotype and genotype heterogeneity. We report the validation of a previously published clinical practice-based algorithm to diagnose ARCA. Two assessors performed a blind analysis to determine the most probable mutated gene based on comprehensive clinical and paraclinical data, without knowing the molecular diagnosis of 23 patients diagnosed by targeted capture of 57 ataxia genes and high-throughput sequencing coming from a 145 patients series. The correct gene was predicted in 61 and 78 % of the cases by the two assessors, respectively. There was a high inter-rater agreement [K = 0.85 (0.55–0.98) p < 0.001] confirming the algorithm’s reproducibility. Phenotyping patients with proper clinical examination, imaging, biochemical investigations and nerve conduction studies remain crucial for the guidance of molecular analysis and to interpret next generation sequencing results. The proposed algorithm should be helpful for diagnosing ARCA in clinical practice.

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Acknowledgments

Sequencing was performed by the IGBMC Microarray and Sequencing platform a member of the “France Génomique” consortium (ANR-10-INBS-0009). This study was supported by funds from the Agence Nationale pour la Recherche-Maladies Rares and Maladies Neurologiques et Psychiatriques (ANR-09-MNPS-001-01 to M.K. and A.D.) the ANR/E-rare JTC 2011 “Euro-SCAR” (2011-RARE-004-01 to M.K.) and the Agence de la Biomédecine (to J.-L.M.). M.R. was supported by a fellowship from the “Journées de Neurologie de Langue Française”.

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Author notes

    Affiliations

    1. Service de Neurologie, Centre Hospitalier de Haguenau, 67500, Haguenau, France

      Martial Mallaret

    2. Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), INSERM-U964/CNRS-UMR7104/Université de Strasbourg, 67404, Illkirch, France

      Martial Mallaret, Mathilde Renaud, Claire Redin, Nathalie Drouot, Jean Muller, Jean Louis Mandel, Christine Tranchant & Mathieu Anheim

    3. Department of Neurology, Movement Disorders Unit, Hôpital de Hautepierre, 67098, Strasbourg cedex, France

      Martial Mallaret, Mathilde Renaud, Marie-Céline Fleury, Christine Tranchant & Mathieu Anheim

    4. Fédération de Médecine Translationnelle de Strasbourg (FMTS), Université de Strasbourg, Strasbourg, France

      Mathilde Renaud, Christine Tranchant & Mathieu Anheim

    5. Laboratoire de Génétique Médicale, UMR_S INSERM U1112, IGMA, Faculté de Médecine, Université de Strasbourg, Strasbourg, France

      Jean Muller & Jean Louis Mandel

    6. Laboratoire de Biostatistique et d’Informatique Médicale, Faculté de Médecine, Hôpitaux Universitaires de Strasbourg, Strasbourg, France

      Francois Severac

    7. Chaire de Génétique Humaine, Collège de France, Illkirch, France

      Jean Louis Mandel

    8. Laboratoire de Biologie Cellulaire et Moléculaire, Faculté des Sciences Biologiques, USTHB, Algiers, Algeria

      Wahiba Hamza & Traki Benhassine

    9. Département de Neurologie, Centre hospitalier universitaire Mustapha Pacha, Algiers, Algeria

      Lamia Ali-Pacha & Meriem Tazir

    10. Laboratoire de Neurosciences, Université d’Alger, Algiers, Algeria

      Meriem Tazir

    11. Fédération de Génétique, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France

      Alexandra Durr

    12. Institut du cerveau et de la moelle épinière, INSERM U1127/CNRS UMR7225, Université de Paris VI, 75013, Paris, France

      Alexandra Durr

    13. Département de Génétique et de Cytogénétique, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France

      Marie-Lorraine Monin & Cyril Mignot

    14. Département de Neurologie, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, 75651, Paris, France

      Perrine Charles

    15. Centre de Génétique Humaine, Université de Franche-Comté, Besançon, France

      Lionel Van Maldergem

    16. Research Memory Center (CMRR), CHU Besançon, Besançon, France

      Ludivine Chamard

    17. Centre de Génétique, Hôpital d’Enfants, CHU Dijon and EA4271, Université de Bourgogne, Dijon, France

      Christel Thauvin-Robinet

    18. Hôpital de Hautepierre, Neuropédiatrie, Hôpitaux Universitaires de Strasbourg, 67098, Strasbourg cedex, France

      Vincent Laugel

    19. Centre de Référence Malformations et Maladies congénitales du Cervelet, INSERM U1141, Assistance Publique-Hôpitaux de Paris, Paris, France

      Lydie Burglen

    20. Service de Génétique Médicale, Pavillon Ch Lefebvre, Hôpital Purpan-CHU de Toulouse, TSA 40031, 31059, Toulouse Cedex, France

      Patrick Calvas

    21. Laboratoire de Génétique de Maladies Rares, Institut Universitaire de Recherche Clinique, Université de Montpellier, EA 7402, CHU Montpellier, 34093, Montpellier, France

      Michel Koenig

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    1. Martial Mallaret
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    2. Mathilde Renaud
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    Correspondence to Mathieu Anheim.

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    Financial disclosure

    The authors declare no financial disclosure related to the research covered by this article. Martial Mallaret received travel grants from Ipsen and Merz. Mathieu Anheim received honoraria and travel grants from Actelion.

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    None.

    Additional information

    M. Anheim and M. Koenig equally contributed to this work.

    Electronic supplementary material

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    415_2016_8112_MOESM1_ESM.docx

    Supplementary file: A-Panel of 57 ataxia mutated genes (in alphabetical order). B- Variants Analysis of NGS data. C-Clinical and molecular data of the patients without evaluation by the clinical practice-based algorithm. D- Comparative clinical data from patients with a molecular diagnosis (positive patients) and without (negative patients) in our series (DOCX 26 kb)

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    Mallaret, M., Renaud, M., Redin, C. et al. Validation of a clinical practice-based algorithm for the diagnosis of autosomal recessive cerebellar ataxias based on NGS identified cases. J Neurol 263, 1314–1322 (2016). https://doi.org/10.1007/s00415-016-8112-5

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    • DOI: https://doi.org/10.1007/s00415-016-8112-5

    Keywords

    • Recessive ataxia
    • Next generation sequencing
    • Neurogenetics
    • Electromyography