Open Access
March 2018 Adjustment of nonconfounding covariates in case-control genetic association studies
Hong Zhang, Nilanjan Chatterjee, Daniel Rader, Jinbo Chen
Ann. Appl. Stat. 12(1): 200-221 (March 2018). DOI: 10.1214/17-AOAS1065


It has recently been reported that adjustment of nonconfounding covariates in case-control genetic association analyses may lead to decreased power when the phenotype is rare. This observation contrasts a well-known result for clinical trials where adjustment of baseline variables always leads to increased power for testing randomized treatment effects. In this paper, we propose a unified solution that guarantees increased power through covariate adjustment regardless of whether the phenotype is rare or common. Our method exploits external phenotype prevalence data through a profile likelihood function, and can be applied to fit any commonly used penetrance models including the logistic and probit regression models. Through extensive simulation studies, we showed empirically that the power of our method was indeed higher than available analysis strategies with or without covariate adjustment, and can be considerably higher when the phenotype was common and the covariate effect was strong. We applied the proposed method to analyze a case-control genetic association study on human high density lipoprotein cholesterol level.


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Hong Zhang. Nilanjan Chatterjee. Daniel Rader. Jinbo Chen. "Adjustment of nonconfounding covariates in case-control genetic association studies." Ann. Appl. Stat. 12 (1) 200 - 221, March 2018.


Received: 1 May 2016; Revised: 1 January 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894704
MathSciNet: MR3773391
Digital Object Identifier: 10.1214/17-AOAS1065

Keywords: Case-control studies , Hardy–Weinberg equilibrium , nonconfounding covariates , profile likelihood

Rights: Copyright © 2018 Institute of Mathematical Statistics

Vol.12 • No. 1 • March 2018
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