title: Robust analysis of secondary phenotypes in case-control genetic association studies.
abstract: The case-control study is a common design for assessing the association between genetic exposures and a disease phenotype. Though association with a given (case-control) phenotype is always of primary interest, there is often considerable interest in assessing relationships between genetic exposures and other (secondary) phenotypes. However, the case-control sample represents a biased sample from the general population. As a result, if this sampling framework is not correctly taken into account, analyses estimating the effect of exposures on secondary phenotypes can be biased leading to incorrect inference. In this paper, we address this problem and propose a general approach for estimating and testing the population effect of a genetic variant on a secondary phenotype. Our approach is based on inverse probability weighted estimating equations, where the weights depend on genotype and the secondary phenotype. We show that, though slightly less efficient than a full likelihood-based analysis when the likelihood is correctly specified, it is substantially more robust to model misspecification, and can out-perform likelihood-based analysis, both in terms of validity and power, when the model is misspecified. We illustrate our approach with an application to a case-control study extracted from the Framingham Heart Study. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, 02118, MA, U.S.A.
authors.forename: Chuanhua
authors.initials: C
authors.lastname: Xing
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, 27710, NC, U.S.A.
authors.forename: Janice
authors.initials: J
authors.lastname: M McCarthy
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, 02118, MA, U.S.A.|National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, 01702, MA, U.S.A.
authors.forename: Josée
authors.identifier: 0000-0003-2871-3603
authors.initials: J
authors.lastname: Dupuis
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, 02118, MA, U.S.A.|National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, 01702, MA, U.S.A.
authors.forename: L
authors.initials: L
authors.lastname: Adrienne Cupples
authors.affiliation: General Medicine Division, Massachusetts General Hospital, Boston, 02114, MA, U.S.A.|Department of Medicine, Harvard Medical School, Boston, 02115, MA, U.S.A.
authors.forename: James
authors.initials: J
authors.lastname: B Meigs
authors.affiliation: Department of Biostatistics, Harvard University, Cambridge, 01238, MA, U.S.A.
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, 27710, NC, U.S.A.|Center for Human Genome Variation, Duke University, Durham, 27710, NC, U.S.A.
authors.forename: Andrew
authors.initials: A
authors.lastname: S Allen
mesh_terms: D016022:Case-Control Studies; D056726:Genetic Association Studies; D055106:Genome-Wide Association Study; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide
keywords: case-control; estimating equation; genetic association study; inverse-probability-weighted; secondary phenotype