A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures
dc.contributor.author | Sánchez, Brisa N. | en_US |
dc.contributor.author | Kang, Shan | en_US |
dc.contributor.author | Mukherjee, Bhramar | en_US |
dc.date.accessioned | 2012-07-12T17:25:26Z | |
dc.date.available | 2013-08-01T14:04:40Z | en_US |
dc.date.issued | 2012-06 | en_US |
dc.identifier.citation | Sánchez, Brisa N. ; Kang, Shan; Mukherjee, Bhramar (2012). "A Latent Variable Approach to Study Geneâ Environment Interactions in the Presence of Multiple Correlated Exposures." Biometrics 68(2). <http://hdl.handle.net/2027.42/92103> | en_US |
dc.identifier.issn | 0006-341X | en_US |
dc.identifier.issn | 1541-0420 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/92103 | |
dc.publisher | Blackwell Publishing Inc | en_US |
dc.publisher | Wiley Periodicals, Inc. | en_US |
dc.subject.other | Shrinkage Estimation | en_US |
dc.subject.other | Gene–Environment Independence | en_US |
dc.subject.other | Principal Components | en_US |
dc.subject.other | Structural Equation Models | en_US |
dc.title | A Latent Variable Approach to Study Gene–Environment Interactions in the Presence of Multiple Correlated Exposures | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/92103/1/j.1541-0420.2011.01677.x.pdf | |
dc.identifier.doi | 10.1111/j.1541-0420.2011.01677.x | en_US |
dc.identifier.source | Biometrics | en_US |
dc.identifier.citedreference | Proust‐Lima, C., Joly, P., Dartigues, J. F., and Jacqmin‐Gadda, H. ( 2009 ). Joint modelling of multivariate longitudinal outcomes and a time‐to‐event: A nonlinear latent class approach. Computational Statistics & Data Analysis 53, 1142 – 1154. | en_US |
dc.identifier.citedreference | Dhungana, P., Eskridge, K. M., Baenziger, P. S., Campbell, B. T., Gill, K. S., and Dweikat, I. ( 2007 ). Analysis of genotype‐by‐environment interaction in wheat using a structural equation model and chromosome substitution lines. Crop Science 47, 477 – 484. | en_US |
dc.identifier.citedreference | Gonzalez‐Cossio, T., Peterson, K. E., Sanin, L. H., Fishbein, E., Palazuelos, E., Aro, A., Hernandez‐Avila, M., and Hu, H. ( 1997 ). Decrease in birth weight in relation to maternal bone‐lead burden. Pediatrics 100, 856 – 862. | en_US |
dc.identifier.citedreference | Hjort, N. L. and Claeskens, G. ( 2003 ). Frequentist model average estimators. Journal of the American Statistical Association 98, 879 – 899. | en_US |
dc.identifier.citedreference | Hopkins, M. R., Ettinger, A. S., Hernandez‐Avila, M., Schwartz, J., Tellez‐Rojo, M. M., Lamadrid‐Figueroa, H., Bellinger, D., Hu, H., and Wright, R. O. ( 2008 ). Variants in iron metabolism genes predict higher blood lead levels in young children. Environmental Health Perspectives 116, 1261 – 1266. | en_US |
dc.identifier.citedreference | Huang, G. H. and Bandeen‐Roche, K. ( 2004 ). Building an identifiable latent class model with covariate effects on underlying and measured variables. Psychometrika 69, 5 – 32. | en_US |
dc.identifier.citedreference | Huang, L. S., Wang, H. K., and Cox, C. ( 2005 ). Assessing interaction effects in linear measurement error models. Journal of the Royal Statistical Society Series C—Applied Statistics 54, 21 – 30. | en_US |
dc.identifier.citedreference | Javaras, K. N., Hudson, J. I., and Laird, N. M. ( 2010 ). Fitting ACE structural equation models to case‐control family data. Genetic Epidemiology 34, 238 – 245. | en_US |
dc.identifier.citedreference | Jeannie‐Marie S. Leoutsakos, J. M. S., Bandeen‐Roche, K., Garrett‐Mayer, E., and Zandi, P. P. ( 2010 ). Incorporating scientific knowledge into phenotype development: Penalized latent class regression. Statistics in Medicine 30, 784 – 798. | en_US |
dc.identifier.citedreference | Khoury, M. J. and Wacholder, S. ( 2009 ). Invited commentary: From genome‐wide association studies to gene‐environment‐wide interaction studies–challenges and opportunities. American Journal of Epidemiology 169, 227 – 230; discussion 234–235. | en_US |
dc.identifier.citedreference | Li, D. L. and Conti, D. V. ( 2009 ). Detecting gene‐environment interactions using a combined case‐only and case‐control approach. American Journal of Epidemiology 169, 497 – 504. | en_US |
dc.identifier.citedreference | Little, R. J. A. and Rubin, D. B. ( 2002 ). Statistical Analysis with Missing Data, 2nd edition. Hoboken, New Jersey: John Wiley & Sons. | en_US |
dc.identifier.citedreference | Mukherjee, B. and Chatterjee, N. ( 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 64, 685 – 694. | en_US |
dc.identifier.citedreference | Qi, L., Ma, J., Qi, Q., Hartiala, J., Allayee, H., and Campos, H. ( 2011 ). Genetic risk score and risk of myocardial infarction in Hispanics. Circulation 123, 374 – 380. | en_US |
dc.identifier.citedreference | Raghunathan, T. E., Solenberger, P., and Van Hoewyk, J. ( 2002 ). IVEware: Imputation and Variance Estimation Software User Guide. Ann Arbor, Michigan: Survey Methodology Program, University of Michigan. | en_US |
dc.identifier.citedreference | Rathouz, P., Van Hulle, C., Rodgers, J., Waldman, I., and Lahey, B. ( 2008 ). Specification, testing, and interpretation of gene‐by‐measured‐environment interaction models in the presence of gene‐environment correlation. Behavioral Genetics 38, 301 – 315. | en_US |
dc.identifier.citedreference | Roy, J. and Lin, X. H. ( 2000 ). Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics 56, 1047 – 1054. | en_US |
dc.identifier.citedreference | Sánchez, B. N., Budtz‐Jørgensen, E., Ryan, L. M., and Hu, H. ( 2005 ). Structural equation models: A review with applications to environmental epidemiology. Journal of the American Statistical Association 100, 1443 – 1455. | en_US |
dc.identifier.citedreference | Schumacher, F. R. and Kraft, P. ( 2007 ). A Bayesian latent class analysis for whole‐genome association analyses: An illustration using the gaw15 simulated rheumatoid arthritis dense scan data. BMC Proceedings 1, S112. Available at http://www.biomedcentral.com/1753‐6561/1/S1/S112, accessed December 10, 2010. | en_US |
dc.identifier.citedreference | Skrondal, A. and Rabe‐Hesketh, S. ( 2004 ). Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Boca Raton, Florida: Chapman & Hall. | en_US |
dc.identifier.citedreference | Westland, J. C. ( 2010 ). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications 9, 476 – 487. | en_US |
dc.identifier.citedreference | Bentler, P. M. and Hu, L. T. ( 1995 ). Evaluating model fit. In Structural Equation Modeling, R. H. Hoyle (ed.), 76 – 99. London: Sage. | en_US |
dc.identifier.citedreference | Bollen, K. A. ( 1989 ). Structural Equations with Latent Variables. New York: John Wiley & Sons. | en_US |
dc.identifier.citedreference | Budtz‐Jørgensen, E., Keiding, N., Grandjean, P., Weihe, P., and White, R. F. ( 2003a ). Consequences of exposure measurement error for confounder identification in environmental epidemiology. Statistics in Medicine 22, 3089 – 3100. | en_US |
dc.identifier.citedreference | Budtz‐Jørgensen, E., Keiding, N., Grandjean, P., Weihe, P., and White, R. F. ( 2003b ). Statistical methods for the evaluation of health effects of prenatal mercury exposure. Environmetrics 14, 105 – 120. | en_US |
dc.identifier.citedreference | Cantonwine, D., Hu, H., Tellez‐Rojo, M. M., Sánchez, B. N., Lamadrid‐Figueroa, H., Ettinger, A. S., Mercado Garcia, A., Hernandez‐Avila, M., and Wright, R. O. ( 2010 ). HFE gene variants modify the association between maternal lead burden and infant birthweight: A prospective birth cohort study in Mexico City, Mexico. Environmental Health 9, 43. Available at http://www.ehjournal.net/content/9/1/43, accessed January 10, 2010. | en_US |
dc.identifier.citedreference | Chatterjee, N. and Carroll, R. J. ( 2005 ). Semiparametric maximum likelihood estimation exploiting gene‐environment independence in case‐control studies. Biometrika 92, 399 – 418. | en_US |
dc.identifier.citedreference | Chatterjee, N., Kalaylioglu, Z., Moslehi, R., Peters, U., and Wacholder, S. ( 2006 ). Powerful multilocus tests of genetic association in the presence of gene‐gene and gene‐environment interactions. American Journal of Human Genetics 79, 1002 – 1016. | en_US |
dc.identifier.citedreference | Chen, Y. H., Chatterjee, N., and Carroll, R. J. ( 2009 ). Shrinkage estimators for robust and efficient inference in haplotype‐based case‐control studies. Journal of the American Statistical Association 104, 220 – 233. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.