Abstract
The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result in a substantial loss of power. To address this issue, the MAX3 test (the maximum of three separate test statistics) has been proposed as a robust test that performs plausibly regardless of the underlying genetic model. However, the original implementation of MAX3 utilizes the trend test so it cannot adjust for any covariates such as age and gender. This drawback has significantly limited the application of the MAX3 in GWASs, as covariates account for a considerable amount of variability in these disorders. In this paper, we extended the MAX3 and proposed the CMAX3 (covariate-adjusted MAX3) based on logistic regression. The proposed test yielded a similar robust efficiency as the original MAX3 while easily adjusting for any covariate based on the likelihood framework. The asymptotic formula to calculate the p-value of the proposed test was also developed in this paper. The simulation results showed that the proposed test performed desirably under both the null and alternative hypotheses. For the purpose of illustration, we applied the proposed test to re-analyze a case-control GWAS dataset from the Collaborative Studies on Genetics of Alcoholism (COGA). The R code to implement the proposed test is also introduced in this paper and is available for free download.
Highlights
The goal of a genetic association study is to identify candidate genetic factors that are associated with specific disease status [1,2,3,4,5,6]
In this paper, we extended the original CMAX3 to adjust for covariate effects based on the likelihood function framework
The simulation results and an application to the Collaborative Studies on Genetics of Alcoholism (COGA) dataset demonstrated the benefit of adopting the new CMAX3 for the evaluation of genetic association in the presence of covariate effects
Summary
The goal of a genetic association study is to identify candidate genetic factors that are associated with specific disease status [1,2,3,4,5,6] Toward this goal, a variety of statistical methods have been developed to test the association between disease and genetic variants [7,8,9,10,11,12]. The logit transformation of the disease risk is linked with a linear combination of genetic effects and covariate effects. This method naturally controls the covariate effects by estimating the corresponding coefficients from the regression term [6]. The penetrance increases with the number of disease alleles under the alternative hypothesis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
