Abstract
Huntington's disease (HD) is a progressive neurodegenerative disorder caused by an expansion of CAG repeats in the IT15 gene. The age-at-onset (AAO) of HD is inversely related to the CAG repeat length and the minimum length thought to cause HD is 36. Accurate estimation of the AAO distribution based on CAG repeat length is important for genetic counseling and the design of clinical trials. In the Cooperative Huntington's Observational Research Trial (COHORT) study, the CAG repeat length is known for the proband participants. However, whether a family member shares the huntingtin gene status (CAG expanded or not) with the proband is unknown. In this work, we use the expectation-maximization (EM) algorithm to handle the missing huntingtin gene information in first-degree family members in COHORT, assuming that a family member has the same CAG length as the proband if the family member carries a huntingtin gene mutation. We perform simulation studies to examine performance of the proposed method and apply the methods to analyze COHORT proband and family combined data. Our analyses reveal that the estimated cumulative risk of HD symptom onset obtained from the combined data is slightly lower than the risk estimated from the proband data alone.
Highlights
Huntington’s disease HD is a severe, autosomal dominantly inherited neurodegenerative disorder that affects motor, cognitive, and psychiatric function and is uniformly fatal
Since 2005, COHORT probands from sites with IRB approval have participated in family history interviews and have provided information on HD affection status in their family members
Information on CAG repeat length, age at time of evaluation and the probability of being a carrier receiving huntingtin mutation from the proband was available for 2851 family members of 1151 probands
Summary
Huntington’s disease HD is a severe, autosomal dominantly inherited neurodegenerative disorder that affects motor, cognitive, and psychiatric function and is uniformly fatal. Using a large clinical data set, they observed that separate exponential relationships with CAG length gave excellent empirical goodness of fit to both the mean AAO and its variance. Other parametric models, such as Gamma distribution, have been proposed in the literature 12, 13. We treat the unknown huntingtin gene sharing status in first-degree family members CAG-elongated or not as missing data and use the EM algorithm to carry out the maximum likelihood estimation of the proband and family data jointly. Our results show a slightly lower estimated cumulative risk of HD symptom onset using the combined data compared to using proband data alone
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