IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v107y2012i500p1441-1452.html
   My bibliography  Save this article

Testing for Gene--Environment and Gene--Gene Interactions Under Monotonicity Constraints

Author

Listed:
  • Summer S. Han
  • Philip S. Rosenberg
  • Nilanjan Chatterjee

Abstract

Recent genome-wide association studies (GWASs) designed to detect the main effects of genetic markers have had considerable success with many findings validated by replication studies. However, relatively few findings of gene--gene or gene--environment interactions have been successfully reproduced. Besides the main issues associated with insufficient sample size in current studies, a complication is that interactions that rank high based on p -values often correspond to extreme forms of joint effects that are biologically less plausible. To reduce false positives and to increase power, we develop various gene--environment/gene--gene tests based on biologically more plausible constraints using bivariate isotonic regressions for case--control data. We extend our method to exploit gene--environment or gene--gene independence information, integrating the approach proposed by Chatterjee and Carroll. We propose appropriate nonparametric and parametric permutation procedures for evaluating the significance of the tests. Simulations show that our method gains power over traditional unconstrained methods by reducing the sizes of alternative parameter spaces. We apply our method to several real-data examples, including an analysis of bladder cancer data to detect interactions between the NAT2 gene and smoking. We also show that the proposed method is computationally feasible for large-scale problems by applying it to the National Cancer Institute (NCI) lung cancer GWAS data.

Suggested Citation

  • Summer S. Han & Philip S. Rosenberg & Nilanjan Chatterjee, 2012. "Testing for Gene--Environment and Gene--Gene Interactions Under Monotonicity Constraints," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1441-1452, December.
  • Handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1441-1452
    DOI: 10.1080/01621459.2012.726892
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2012.726892
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01621459.2012.726892?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nilanjan Chatterjee & Raymond J. Carroll, 2005. "Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies," Biometrika, Biometrika Trust, vol. 92(2), pages 399-418, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bhramar Mukherjee & Jaeil Ahn & Stephen B. Gruber & Malay Ghosh & Nilanjan Chatterjee, 2010. "Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 934-948, September.
    2. Colin O. Wu & Gang Zheng & Minjung Kwak, 2013. "A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples," Biometrics, The International Biometric Society, vol. 69(2), pages 417-426, June.
    3. Bhramar Mukherjee & Li Zhang & Malay Ghosh & Samiran Sinha, 2007. "Semiparametric Bayesian Analysis of Case–Control Data under Conditional Gene-Environment Independence," Biometrics, The International Biometric Society, vol. 63(3), pages 834-844, September.
    4. Tina Tsz-Ting Chui & Wen-Chung Lee, 2014. "Estimating Risks and Relative Risks in Case-Base Studies under the Assumptions of Gene-Environment Independence and Hardy-Weinberg Equilibrium," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-5, August.
    5. Summer S. Han & Philip S. Rosenberg & Arpita Ghosh & Maria Teresa Landi & Neil E. Caporaso & Nilanjan Chatterjee, 2015. "An exposure‐weighted score test for genetic associations integrating environmental risk factors," Biometrics, The International Biometric Society, vol. 71(3), pages 596-605, September.
    6. Liang, Liang & Ma, Yanyuan & Carroll, Raymond J., 2019. "A semiparametric efficient estimator in case-control studies for gene–environment independent models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 38-50.
    7. James Y. Dai & Michael LeBlanc & Charles Kooperberg, 2009. "Semiparametric Estimation Exploiting Covariate Independence in Two-Phase Randomized Trials," Biometrics, The International Biometric Society, vol. 65(1), pages 178-187, March.
    8. Wu Cen & Zhong Ping-Shou & Cui Yuehua, 2018. "Additive varying-coefficient model for nonlinear gene-environment interactions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(2), pages 1-18, April.
    9. Bhramar Mukherjee & Nilanjan Chatterjee, 2008. "Exploiting Gene‐Environment Independence for Analysis of Case–Control Studies: An Empirical Bayes‐Type Shrinkage Estimator to Trade‐Off between Bias and Efficiency," Biometrics, The International Biometric Society, vol. 64(3), pages 685-694, September.
    10. Jinbo Chen & Dongyu Lin & Hagit Hochner, 2012. "Semiparametric Maximum Likelihood Methods for Analyzing Genetic and Environmental Effects with Case-Control Mother–Child Pair Data," Biometrics, The International Biometric Society, vol. 68(3), pages 869-877, September.
    11. Brisa N. Sánchez & Shan Kang & Bhramar Mukherjee, 2012. "A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures," Biometrics, The International Biometric Society, vol. 68(2), pages 466-476, June.
    12. Yulia V. Marchenko & Raymond K. Carroll & Danyu Y. Lin & Christopher I. Amos & Roberto G. Gutierrez, 2008. "Semiparametric analysis of case–control genetic data in the presence of environmental factors," Stata Journal, StataCorp LP, vol. 8(3), pages 305-333, September.
    13. Eric J. Tchetgen Tchetgen & James Robins, 2010. "The Semiparametric Case-Only Estimator," Biometrics, The International Biometric Society, vol. 66(4), pages 1138-1144, December.
    14. Hua Yun Chen & Daniel E. Rader & Mingyao Li, 2015. "Likelihood Inferences on Semiparametric Odds Ratio Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1125-1135, September.
    15. Gustafson Paul, 2010. "Bayesian Inference for Partially Identified Models," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-20, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:107:y:2012:i:500:p:1441-1452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.