title: The case-only test for gene-environment interaction is not uniformly powerful: an empirical example.
abstract: The case-only test has been proposed as a more powerful approach to detect gene-environment (G × E) interactions. This approach assumes that the genetic and environmental factors are independent. Although it is well known that Type I error rate will increase if this assumption is violated, it is less widely appreciated that G × E correlation can also lead to power loss. We illustrate this phenomenon by comparing the performance of the case-only test to other approaches to detect G × E interactions in a genome-wide association study (GWAS) of esophageal squamous-cell carcinoma (ESCC) in Chinese populations. Some of these approaches do not use information on the correlation between exposure and genotype (standard logistic regression), whereas others seek to use this information in a robust fashion to boost power without increasing Type I error (two-step, empirical Bayes, and cocktail methods). G × E interactions were identified involving drinking status and two regions containing genes in the alcohol metabolism pathway, 4q23 and 12q24. Although the case-only test yielded the most significant tests of G × E interaction in the 4q23 region, the case-only test failed to identify significant interactions in the 12q24 region which were readily identified using other approaches. The low power of the case-only test in the 12q24 region is likely due to the strong inverse association between the single nucleotide polymorphism (SNPs) in this region and drinking status. This example underscores the need to consider multiple approaches to detect G × E interactions, as different tests are more or less sensitive to different alternative hypotheses and violations of the G × E independence assumption.
authors.affiliation: Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA.
authors.forename: Chen
authors.initials: C
authors.lastname: Wu
authors.forename: Jiang
authors.initials: J
authors.lastname: Chang
authors.forename: Baoshan
authors.initials: B
authors.lastname: Ma
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Miao
authors.forename: Yifeng
authors.initials: Y
authors.lastname: Zhou
authors.forename: Yu
authors.initials: Y
authors.lastname: Liu
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.forename: Tangchun
authors.initials: T
authors.lastname: Wu
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Hu
authors.forename: Hongbing
authors.initials: H
authors.lastname: Shen
authors.forename: Weihua
authors.initials: W
authors.lastname: Jia
authors.forename: Yixin
authors.initials: Y
authors.lastname: Zeng
authors.forename: Dongxin
authors.initials: D
authors.lastname: Lin
authors.forename: Peter
authors.initials: P
authors.lastname: Kraft
mesh_terms: D001499:Bayes Theorem; D002294:Carcinoma, Squamous Cell / Q000208:ethnology / Q000235:genetics*; D002681:China; D002881:Chromosomes, Human, Pair 12 / Q000235:genetics; D002894:Chromosomes, Human, Pair 4 / Q000235:genetics; D004777:Environment; D004938:Esophageal Neoplasms / Q000208:ethnology / Q000235:genetics*; D005260:Female; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis; D011795:Surveys and Questionnaires