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Activity Number: 204
Type: Contributed
Date/Time: Monday, August 1, 2016 : 11:35 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #321699
Title: Estimation of Genetic Risk Function with Covariates in the Presence of Missing Genotypes
Author(s): Annie J. Lee* and Yuanjia Wang and Karen Marder and Roy N. Alcalay
Companies: Columbia University and Columbia University and Columbia University and Columbia University
Keywords: Mixture distribution ; Censored data ; Semiparametric efficiency ; Sieve maximum likelihood estimation ; Parkinsons disease

In genetic epidemiological studies, family history data are collected on relatives of study participants and used to estimate the age-specific risk of disease for individuals who carry a causal mutation. However, a family member's genotype data may not be collected due to the high cost of in-person interview to obtain blood sample or death of a relative. Moreover, age of disease onset is subject to right censoring. Previously, nonparametric genotype-specific risk estimation in censored mixture data has been proposed for a univariate model. With multiple predictive risk factors, risk estimation requires a multivariate model. We propose a sieve maximum likelihood estimation method that permits adjustment for multiple covariates and interaction effects to estimate disease risk associated with genetic mutation in censored mixture data. We examine performance of the proposed methods by simulations and apply them to estimate the age-specific cumulative risk of Parkinson's disease (PD) in carriers of LRRK2 G2019S mutation using first-degree relatives. The method allows precise risk prediction by controlling for individual characteristics such as sex, ethnicity or other demographics.

Authors who are presenting talks have a * after their name.

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