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The Case-Only Test for Gene–Environment Interaction is Not Uniformly Powerful: An Empirical Example

Authors

  • Chen Wu,

    1. Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
    2. State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
    3. Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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  • Jiang Chang,

    1. State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
    2. Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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  • Baoshan Ma,

    1. Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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  • Xiaoping Miao,

    1. Key Laboratory for Environment and Health (Ministry of Education), School of Public Health, Huazhong University of Sciences and Technology, Wuhan, Hubei, China
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  • Yifeng Zhou,

    1. Laboratory of Cancer Molecular Genetics, Medical College of Soochow University, Suzhou, Jiangsu, China
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  • Yu Liu,

    1. State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
    2. Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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  • Yun Li,

    1. Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
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  • Tangchun Wu,

    1. Key Laboratory for Environment and Health (Ministry of Education), School of Public Health, Huazhong University of Sciences and Technology, Wuhan, Hubei, China
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  • Zhibin Hu,

    1. Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, Jiangsu, China
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  • Hongbing Shen,

    1. Department of Epidemiology and Biostatistics, Cancer Center, Nanjing Medical University, Nanjing, Jiangsu, China
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  • Weihua Jia,

    1. State Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
    2. Department of Experimental Research, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
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  • Yixin Zeng,

    1. State Key Laboratory of Oncology in Southern China, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
    2. Department of Experimental Research, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
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  • Dongxin Lin,

    Corresponding author
    1. Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
    • State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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  • Peter Kraft

    Corresponding author
    • Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts
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Correspondence to: Dongxin Lin, State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China and Peter Kraft, Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. E-mail: lindx72@cicams.ac.cn; pkraft@hsph.harvard.edu

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.

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