A new model for prediction of the age of onset and penetrance for Huntington's disease based on CAG length

Authors

  • DR Langbehn,

    1. Department of Psychiatry, and
    2. Department of Biostatistics, University of Iowa College of Medicine, Iowa City, IA, USA;
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  • RR Brinkman,

    1. Department of Medical Genetics, Center for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada;
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  • D Falush,

    1. Mathematical Genetics Group, Department of Statistics, Oxford University, Oxford, Great Britain;
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    • *

      Dr D Falush was affiliated with the Max-Planck Institut für Infektionsbiologie, Berlin, Germany during the conduct of this Research.

  • JS Paulsen,

    1. Department of Psychiatry, and
    2. Department of Neurology, University of Iowa College of Medicine, Iowa City, IA, USA
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  • MR Hayden,

    Corresponding author
      Michael R. Hayden, Department of Medical Genetics, Center for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.
      Tel.: +1 604 875 3535;
      fax: +1 604 875 3819;
      e-mail: mrh@cmmt.ubc.ca
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  • on behalf of an International Huntington's Disease Collaborative Group

    1. Department of Medical Genetics, Center for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada;
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    • For a complete list of participants, see Appendix.


Michael R. Hayden, Department of Medical Genetics, Center for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada.
Tel.: +1 604 875 3535;
fax: +1 604 875 3819;
e-mail: mrh@cmmt.ubc.ca

Abstract

Huntington's disease (HD) is a neurodegenerative disorder caused by an unstable CAG repeat. For patients at risk, participating in predictive testing and learning of having CAG expansion, a major unanswered question shifts from “Will I get HD?” to “When will it manifest?” Using the largest cohort of HD patients analyzed to date (2913 individuals from 40 centers worldwide), we developed a parametric survival model based on CAG repeat length to predict the probability of neurological disease onset (based on motor neurological symptoms rather than psychiatric onset) at different ages for individual patients. We provide estimated probabilities of onset associated with CAG repeats between 36 and 56 for individuals of any age with narrow confidence intervals. For example, our model predicts a 91% chance that a 40-year-old individual with 42 repeats will have onset by the age of 65, with a 95% confidence interval from 90 to 93%. This model also defines the variability in HD onset that is not attributable to CAG length and provides information concerning CAG-related penetrance rates.

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