Robust analysis of secondary phenotypes in case-control genetic association studies

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...

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Bibliographic Details
Published in:Statistics in medicine 2016-10, Vol.35 (23), p.4226-4237
Main Authors: Xing, Chuanhua, M. McCarthy, Janice, Dupuis, Josée, Adrienne Cupples, L., B. Meigs, James, Lin, Xihong, S. Allen, Andrew
Format: Article
Language:English
Subjects:
case-control
Case-Control Studies
estimating equation
Genetic Association Studies
genetic association study
Genetics
Genome-Wide Association Study
Genotype & phenotype
Humans
inverse-probability-weighted
Likelihood Functions
Medical statistics
Models, Genetic
Phenotype
Polymorphism, Single Nucleotide
Probability
Robust control
Sampling
secondary phenotype
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.6976