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Published on: December 10, 2021
Nonparametric modeling and analysis of association between Huntington's disease onset and CAG repeats
1Department of Statistics, Texas A&M University, College Station, TX, U.S.A.
Insights
This study introduces a flexible statistical method to better predict Huntington's disease (HD) onset age using CAG repeat length. The new approach improves accuracy by not assuming a specific relationship between genetic factors and disease timing.
Area of Science:
- Genetics
- Neurodegenerative Disorders
- Biostatistics
Background:
- Huntington's disease (HD) is a genetic neurodegenerative disorder caused by CAG repeat expansion on chromosome 4.
- Longer CAG repeat lengths are generally associated with earlier HD onset.
- Existing models often use restrictive logistic assumptions for the relationship between CAG length and HD onset.
Purpose of the Study:
- To develop a semiparametric statistical method to model the age of Huntington's disease onset.
- To relax the rigid parametric assumptions of previous models relating CAG repeat length to HD onset.
- To incorporate family history data and handle censored age-at-onset information.
Main Methods:
- Proposed a semiparametric estimation approach using local kernel and backfitting procedures.
- Developed methodology for mixture data, accommodating individuals at risk and those potentially risk-free.
- Accounted for censored data and additional covariates beyond CAG repeat length.
Main Results:
- The study derived the asymptotic distribution of the proposed semiparametric estimator.
- The methods were applied to the Cooperative Huntington's Observational Research Trial (COHORT) data.
- Successfully estimated the Huntington's disease onset distribution using genetic and family history information.
Conclusions:
- The developed semiparametric method offers a more flexible and robust way to analyze Huntington's disease onset.
- This approach improves the understanding of the relationship between genetic factors and disease progression.
- The methodology is valuable for analyzing complex genetic disorders with available family data.
Abstract:
Huntington's disease (HD) is a neurodegenerative disorder with a dominant genetic mode of inheritance caused by an expansion of CAG repeats on chromosome 4. Typically, a longer sequence of CAG repeat length is associated with increased risk of experiencing earlier onset of HD. Previous studies of the association between HD onset age and CAG length have favored a logistic model, where the CAG repeat length enters the mean and variance components of the logistic model in a complex exponential-linear form. To relax the parametric assumption of the exponential-linear association to the true HD onset distribution, we propose to leave both mean and variance functions of the CAG repeat length unspecified and perform semiparametric estimation in this context through a local kernel and backfitting procedure. Motivated by including family history of HD information available in the family members of participants in the Cooperative Huntington's Observational Research Trial (COHORT), we develop the methodology in the context of mixture data, where some subjects have a positive probability of being risk free. We also allow censoring on the age at onset of disease and accommodate covariates other than the CAG length. We study the theoretical properties of the proposed estimator and derive its asymptotic distribution. Finally, we apply the proposed methods to the COHORT data to estimate the HD onset distribution using a group of study participants and the disease family history information available on their family members.
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