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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1369-1382 |
| Number of pages | 14 |
| Journal | Statistics in Medicine |
| Volume | 33 |
| Issue number | 8 |
| DOIs | |
| State | Published - Apr 15 2014 |
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All Science Journal Classification (ASJC) codes
- Epidemiology
- Statistics and Probability
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Nonparametric modeling and analysis of association between huntington's disease onset and CAG repeats. / Ma, Yanyuan; Wang, Yuanjia.
In: Statistics in Medicine, Vol. 33, No. 8, 15.04.2014, p. 1369-1382.Research output: Contribution to journal › Article
TY - JOUR
T1 - Nonparametric modeling and analysis of association between huntington's disease onset and CAG repeats
AU - Ma, Yanyuan
AU - Wang, Yuanjia
PY - 2014/4/15
Y1 - 2014/4/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84896717351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896717351&partnerID=8YFLogxK
U2 - 10.1002/sim.5971
DO - 10.1002/sim.5971
M3 - Article
C2 - 24027120
AN - SCOPUS:84896717351
VL - 33
SP - 1369
EP - 1382
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 8
ER -