Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies

References

• Akaike, H. (1998), “Information Theory and an Extension of the Maximum Likelihood Principle,” in Selected Papers of Hirotugu Akaike, eds. E. Parzen, K. Tanabe, and G. Kitagawa, New York: Springer, pp. 199–213.
   Google Scholar
• Basu, S., and Pan, W. (2011), “Comparison of Statistical Tests for Disease Association With Rare Variants,” Genetic Epidemiology, 35, 606–619.
   PubMed Web of Science ®Google Scholar
• Bild, D. E., Bluemke, D. A., Burke, G. L., Detrano, R., Roux, A. V. D., Folsom, A. R., Greenland, P., JacobsJr, D. R., Kronmal, R., Liu, K., and Nelson, J. C. (2002), “Multi-Ethnic Study of Atherosclerosis: Objectives and Design,” American Journal of Epidemiology, 156, 871–881.
   PubMed Web of Science ®Google Scholar
• Boonstra, P. S., Mukherjee, B., Gruber, S. B., Ahn, J., Schmit, S. L., and Chatterjee, N. (2016), “Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification,” American Journal of Epidemiology, 183, 237--247.
   PubMed Web of Science ®Google Scholar
• Boulesteix, A.-L., and Strimmer, K. (2007), “Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data,” Briefings in Bioinformatics, 8, 32–44.
   PubMed Web of Science ®Google Scholar
• Chen, H., Meigs, J. B., and Dupuis, J. (2014), “Incorporating Gene-Environment Interaction in Testing for Association With Rare Genetic Variants,” Human Heredity, 78, 81–90.
   PubMed Web of Science ®Google Scholar
• Christine, P. J., Auchincloss, A. H., Bertoni, A. G., Carnethon, M. R., Sánchez, B. N., Moore, K., Adar, S. D., Horwich, T. B., Watson, K. E., and Roux, A. V. D. (2015), “Longitudinal Associations Between Neighborhood Physical and Social Environments and Incident Type 2 Diabetes Mellitus: The Multi-Ethnic Study of Atherosclerosis (MESA),” JAMA Internal Medicine, 175, 1311–1320.
   PubMed Web of Science ®Google Scholar
• Cornelis, M. C., Tchetgen, E. J. T., Liang, L., Qi, L., Chatterjee, N., Hu, F. B., and Kraft, P. (2012), “Gene-Environment Interactions in Genome-Wide Association Studies: A Comparative Study of Tests Applied to Empirical Studies of Type 2 Diabetes,” American Journal of Epidemiology, 175, 191–202.
   PubMed Web of Science ®Google Scholar
• Cui, J. S., Hopper, J. L., and Harrap, S. B. (2003), “Antihypertensive Treatments Obscure Familial Contributions to Blood Pressure Variation,” Hypertension, 41, 207–210.
   PubMed Web of Science ®Google Scholar
• Derkach, A., Lawless, J. F., and Sun, L. (2014), “Pooled Association Tests for Rare Genetic Variants: A Review and Some New Results,” Statistical Science, 29, 302–321.
   Web of Science ®Google Scholar
• Fisher, R. A. (1992), “Statistical Methods for Research Workers,” in Breakthroughs in Statistics, New York: Springer, pp. 66--70.
   Google Scholar
• Gao, X., Starmer, J. and Martin, E. R. (2008), “A Multiple Testing Correction Method for Genetic Association Studies Using Correlated Single Nucleotide Polymorphisms,” Genetic Epidemiology, 32, 361–369.
   Google Scholar
• Goeman, J. J., Van De Geer, S. A., and Van Houwelingen, H. C. (2006), “Testing Against a High-Dimensional Alternative,” Journal of the Royal Statistical Society, Series B, 68, 477–493.
   Google Scholar
• Grenander, U. (1981), Abstract Inference, New York: Wiley.
   Google Scholar
• He, Z., Zhang, M., Lee, S., Smith, J. A., Guo, X., Palmas, W., Kardia, S. L., Roux, A. V. D., and Mukherjee, B. (2015), “Set-Based Tests for Genetic Association in Longitudinal Studies,” Biometrics, 71, 606–615.
   PubMed Web of Science ®Google Scholar
• He, Z., Zhang, M., Zhan, X., and Lu, Q. (2014), “Modeling and Testing for Joint Association Using a Genetic Random Field Model,” Biometrics, 70, 471–479.
   PubMed Web of Science ®Google Scholar
• ICBP (2011), “Genetic Variants in Novel Pathways Influence Blood Pressure and Cardiovascular Disease Risk,” Nature, 478, 103–109.
   PubMed Web of Science ®Google Scholar
• Kaiser, P., Diez Roux, A. V., Mujahid, M., Carnethon, M., Bertoni, A., Adar, S. D., Shea, S., McClelland, R., and Lisabeth, L. (2016), “Neighborhood Environments and Incident Hypertension in the Multi-Ethnic Study of Atherosclerosis,” American Journal of Epidemiology, 183, 988--997.
   PubMed Web of Science ®Google Scholar
• Karolchik, D., Baertsch, R., Diekhans, M., Furey, T. S., Hinrichs, A., Lu, Y., Roskin, K. M., Schwartz, M., Sugnet, C. W., Thomas, D. J., and Weber, R. J. (2003), “The UCSC Genome Browser Database,” Nucleic Acids Research, 31, 51–54.
   PubMed Web of Science ®Google Scholar
• Lee, S., Wright, F. A., and Zou, F. (2011), “Control of Population Stratification by Correlation-Selected Principal Components,” Biometrics, 67, 967–974.
   PubMed Web of Science ®Google Scholar
• Li, S., and Cui, Y. (2012), “Gene-Centric Gene–Gene Interaction: A Model-Based Kernel Machine Method,” The Annals of Applied Statistics, 6, 1134–1161.
   Web of Science ®Google Scholar
• Liang, K.-Y., and Zeger, S. L. (1986), “Longitudinal Data Analysis Using Generalized Linear Models,” Biometrika, 73, 13–22.
   Web of Science ®Google Scholar
• Lin, X., Lee, S., Christiani, D. C., and Lin, X. (2013), “Test for Interactions Between a Genetic Marker Set and Environment in Generalized Linear Models,” Biostatistics, 14, 667–681.
   PubMed Web of Science ®Google Scholar
• Lin, X., Lee, S., Wu, M. C., Wang, C., Chen, H., Li, Z., and Lin, X. (2016), “Test for Rare Variants by Environment Interactions in Sequencing Association Studies,” Biometrics, 72, 156–164.
   PubMed Web of Science ®Google Scholar
• Mallows, C. L. (1973), “Some Comments on Cp,” Technometrics, 15, 661–675.
   Web of Science ®Google Scholar
• Marceau, R., Lu, W., Holloway, S., Sale, M. M., Worrall, B. B., Williams, S. R., Hsu, F.-C., and Tzeng, J.-Y. (2015), “A Fast Multiple-Kernel Method With Applications to Detect Gene–Environment Interaction,” Genetic Epidemiology, 39, 456–468.
   PubMed Web of Science ®Google Scholar
• Marchini, J., Howie, B., Myers, S., McVean, G., and Donnelly, P. (2007), “A New Multipoint Method for Genome-Wide Association Studies by Imputation of Genotypes,” Nature Genetics, 39, 906–913.
   PubMed Web of Science ®Google Scholar
• McKusick, V. A. (1998), Mendelian Inheritance in Man: A Catalog of Human Genes and Genetic Disorders (Vol. 1), Baltimore, MD: JHU Press.
   Google Scholar
• Moore, L. V., Roux, A. V. D., Nettleton, J. A., and Jacobs, D. R. (2008), “Associations of the Local Food Environment With Diet Quality a Comparison of Assessments Based on Surveys and Geographic Information Systems the Multi-Ethnic Study of Atherosclerosis,” American Journal of Epidemiology, 167, 917–924.
   PubMed Web of Science ®Google Scholar
• Mujahid, M. S., Roux, A. V. D., Morenoff, J. D., Raghunathan, T. E., Cooper, R. S., Ni, H., and Shea, S. (2008), “Neighborhood Characteristics and Hypertension,” Epidemiology, 19, 590–598.
   PubMed Web of Science ®Google Scholar
• Mukherjee, B., Ahn, J., Gruber, S. B., and Chatterjee, N. (2012), “Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons,” American Journal of Epidemiology, 175, 177–190.
   PubMed Web of Science ®Google Scholar
• Newey, W. K. (1997), “Convergence Rates and Asymptotic Normality for Series Estimators,” Journal of Econometrics, 79, 147–168.
   Web of Science ®Google Scholar
• Pan, W. (2009), “Asymptotic Tests of Association With Multiple snps in Linkage Disequilibrium,” Genetic Epidemiology, 33, 497–507.
   PubMed Web of Science ®Google Scholar
• Papas, M. A., Alberg, A. J., Ewing, R., Helzlsouer, K. J., Gary, T. L., and Klassen, A. C. (2007), “The Built Environment and Obesity,” Epidemiologic Reviews, 29, 129–143.
   PubMed Web of Science ®Google Scholar
• Pfeufer, A., van Noord, C., Marciante, K. D., Arking, D. E., Larson, M. G., Smith, A. V., Tarasov, K. V., Müller, M., Sotoodehnia, N., Sinner, M. F., and Verwoert, G. C. (2010), “Genome-Wide Association Study of pr Interval,” Nature Genetics, 42, 153–159.
   PubMed Web of Science ®Google Scholar
• Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., and Willer, C. J. (2010), “Locuszoom: Regional Visualization of Genome-Wide Association Scan Results,” Bioinformatics, 26, 2336–2337.
   PubMed Web of Science ®Google Scholar
• Sallis, J. F., Floyd, M. F., Rodríguez, D. A., and Saelens, B. E. (2012), “Role of Built Environments in Physical Activity, Obesity, and Cardiovascular Disease,” Circulation, 125, 729–737.
   PubMed Web of Science ®Google Scholar
• Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461–464.
   Web of Science ®Google Scholar
• Sotoodehnia, N., Isaacs, A., de Bakker, P. I., Dörr, M., Newton-Cheh, C., Nolte, I. M., Van der Harst, P., Müller, M., Eijgelsheim, M., Alonso, A., and Hicks, A. A. (2010), “Common Variants in 22 Loci are Associated With QRS Duration and Cardiac Ventricular Conduction,” Nature Genetics, 42, 1068–1076.
   PubMed Web of Science ®Google Scholar
• Tchetgen Tchetgen, E. J., and Kraft, P. (2011), “On the Robustness of Tests of Genetic Associations Incorporating Gene–Environment Interaction When the Environmental Exposure is Misspecified,” Epidemiology, 22, 257–261.
   PubMed Web of Science ®Google Scholar
• Thomas, D. (2010), “Gene–Environment-Wide Association Studies: Emerging Approaches,” Nature Reviews Genetics, 11, 259–272.
   PubMed Web of Science ®Google Scholar
• Tzeng, J.-Y., Zhang, D., Pongpanich, M., Smith, C., McCarthy, M. I., Sale, M. M., Worrall, B. B., Hsu, F.-C., Thomas, D. C., and Sullivan, P. F. (2011), “Studying Gene and Gene–Environment Effects of Uncommon and Common Variants on Continuous Traits: A Marker-Set Approach Using Gene-Trait Similarity Regression,” The American Journal of Human Genetics, 89, 277–288.
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J., Ko, Y.-A., and Mukherjee, B. (2013), “Environmental Confounding in Gene–Environment Interaction Studies,” American Journal of Epidemiology, 178, 144–152.
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J., and Shpitser, I. (2013), “On the Definition of a Confounder,” The Annals of Statistics, 41, 196–220.
   PubMed Web of Science ®Google Scholar
• Vansteelandt, S., VanderWeele, T. J., Tchetgen, E. J., and Robins, J. M. (2008), “Multiply Robust Inference for Statistical Interactions,” Journal of the American Statistical Association, 103, 1693–1704.
   PubMed Web of Science ®Google Scholar
• Voorman, A., Lumley, T., McKnight, B., and Rice, K. (2011), “Behavior of qq-Plots and Genomic Control in Studies of Gene–Environment Interaction,” PloS One, 6, e19416.
   PubMed Web of Science ®Google Scholar
• Wang, L. (2011), “Gee Analysis of Clustered Binary Data With Diverging Number of Covariates,” The Annals of Statistics, 39, 389–417.
   Web of Science ®Google Scholar
• Wu, M. C., Kraft, P., Epstein, M. P., Taylor, D. M., Chanock, S. J., Hunter, D. J., and Lin, X. (2010), “Powerful snp-Set Analysis for Case–Control Genome-Wide Association Studies,” The American Journal of Human Genetics, 86, 929–942.
   PubMed Web of Science ®Google Scholar
• Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., and Lin, X. (2011), “Rare-Variant Association Testing for Sequencing Data With the Sequence Kernel Association Test,” The American Journal of Human Genetics, 89, 82–93.
   PubMed Web of Science ®Google Scholar