Estimating decreased risks for Huntington disease without a test
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Updated: Jul 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Reinier Timman1, Benno Bonke, Theo Stijnen
1Department of Medical Psychology and Psychotherapy, Netherlands Institute of Health Sciences, Erasmus University Medical Centre, Rotterdam, The Netherlands. r.timman@erasmusmc.nl
Abstract:
The majority of individuals at risk for Huntington disease (HD) is afraid to learn more precisely about their genetic status, as is suggested by the low uptake of the predictive test for HD. Subsequently, the future expectancies of individuals at risk are often based on rough risk estimates such as 50% (child of an affected individual) or 25% (grandchild). Individuals at risk can be offered a better risk estimate based on their current age, length of the disease causing CAG-repeat in the HD gene in close relatives, information on the age at onset, or test results of children. Regression modelling and Cox regression determined relations between ages at onset and CAG repeat length in a sample of 755 tested individuals. A model for calculating the adjusted residual risk status was constructed and implemented in a spreadsheet that can be used in genetic counselling. This model and accompanying spreadsheet broadens the information repertoire for genetic counsellors by providing an optimal estimation of the residual risk status.
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