Browsing by Subject "genetic model"

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    CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates
    (MDPI, 2021) Chen, Zhongxue; Zang, Yong; Biostatistics, School of Public Health
    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
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    Robust tests for gene–environment interaction in case-control and case-only designs
    (Elsevier, 2019-01) Zang, Yong; Fung, Wing Kam; Cao, Sha; Ng, Hon Keung Tony; Zhang, Chi; Biostatistics, School of Public Health
    The case-control and case-only designs are commonly used to detect the gene–environment (G–E) interaction. In principle, the tests based on these two designs require a pre-specified genetic model to achieve an expected power of detecting the G–E interaction. Unfortunately, for most complex diseases the underlying genetic models are unknown. It is well known that mis-specification of the genetic model can result in a substantial loss of power in the detection of the main genetic effect. However, limited effort has been dedicated to the study of G–E interaction. This issue has been investigated in this article with a conclusion that the genetic model mis-specification can not only undermine the power of detecting G–E interaction in both case-control and case-only designs but also distort the type I error rate in case-control design. To tackle this problem, a class of robust tests, namely MAX3, have been proposed for both the case-control and case-only designs. The proposed tests can well control the type I error rate and yield satisfactory power even when the genetic model is mis-specified. The asymptotic distribution and the