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Estimation of genetic risk function with covariates in the presence of missing genotypes.
Lee, Annie J; Marder, Karen; Alcalay, Roy N; Mejia-Santana, Helen; Orr-Urtreger, Avi; Giladi, Nir; Bressman, Susan; Wang, Yuanjia.
Afiliação
  • Lee AJ; Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, U.S.A.
  • Marder K; Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, U.S.A.
  • Alcalay RN; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, U.S.A.
  • Mejia-Santana H; Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, U.S.A.
  • Orr-Urtreger A; Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, U.S.A.
  • Giladi N; Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, U.S.A.
  • Bressman S; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Wang Y; Genetic Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Stat Med ; 36(22): 3533-3546, 2017 Sep 30.
Article em En | MEDLINE | ID: mdl-28656686
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 because of the high cost of in-person interview to obtain blood sample or death of a relative. Previously, efficient nonparametric genotype-specific risk estimation in censored mixture data has been proposed without considering covariates. With multiple predictive risk factors available, risk estimation requires a multivariate model to account for additional covariates that may affect disease risk simultaneously. Therefore, it is important to consider the role of covariates in genotype-specific distribution estimation using family history data. We propose an estimation method that permits more precise risk prediction by controlling for individual characteristics and incorporating interaction effects with missing genotypes in relatives, and thus, gene-gene interactions and gene-environment interactions can be handled within the framework of a single model. 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 the LRRK2 G2019S mutation using first-degree relatives who are at genetic risk for PD. The utility of estimated carrier risk is demonstrated through designing a future clinical trial under various assumptions. Such sample size estimation is seen in the Huntington's disease literature using the length of abnormal expansion of a CAG repeat in the HTT gene but is less common in the PD literature. Copyright © 2017 John Wiley & Sons, Ltd.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Medição de Risco / Predisposição Genética para Doença / Modelos Genéticos Tipo de estudo: Clinical_trials / Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Stat Med Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Medição de Risco / Predisposição Genética para Doença / Modelos Genéticos Tipo de estudo: Clinical_trials / Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Stat Med Ano de publicação: 2017 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Reino Unido