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Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs This article is a US government work, and, as such, is in the public domain in the United States of America.

dc.contributor.authorMukherjee, Bhramaren_US
dc.contributor.authorAhn, Jaeilen_US
dc.contributor.authorGruber, Stephen B.en_US
dc.contributor.authorRennert, Gaden_US
dc.contributor.authorMoreno, Victoren_US
dc.contributor.authorChatterjee, Nilanjanen_US
dc.date.accessioned2008-11-03T18:53:12Z
dc.date.available2010-01-05T16:59:14Zen_US
dc.date.issued2008-11en_US
dc.identifier.citationMukherjee, Bhramar; Ahn, Jaeil; Gruber, Stephen B.; Rennert, Gad; Moreno, Victor; Chatterjee, Nilanjan (2008). "Tests for gene-environment interaction from case-control data: a novel study of type I error, power and designs This article is a US government work, and, as such, is in the public domain in the United States of America. ." Genetic Epidemiology 32(7): 615-626. <http://hdl.handle.net/2027.42/61219>en_US
dc.identifier.issn0741-0395en_US
dc.identifier.issn1098-2272en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/61219
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18473390&dopt=citationen_US
dc.description.abstractTo evaluate the risk of a disease associated with the joint effects of genetic susceptibility and environmental exposures, epidemiologic researchers often test for non-multiplicative gene-environment effects from case-control studies. In this article, we present a comparative study of four alternative tests for interactions: (i) the standard case-control method; (ii) the case-only method, which requires an assumption of gene-environment independence for the underlying population; (iii) a two-step method that decides between the case-only and case-control estimators depending on a statistical test for the gene-environment independence assumption and (iv) a novel empirical-Bayes (EB) method that combines the case-control and case-only estimators depending on the sample size and strength of the gene-environment association in the data. We evaluate the methods in terms of integrated Type I error and power, averaged with respect to varying scenarios for gene-environment association that are likely to appear in practice. These unique studies suggest that the novel EB procedure overall is a promising approach for detection of gene-environment interactions from case-control studies. In particular, the EB procedure, unlike the case-only or two-step methods, can closely maintain a desired Type I error under realistic scenarios of gene-environment dependence and yet can be substantially more powerful than the traditional case-control analysis when the gene-environment independence assumption is satisfied, exactly or approximately. Our studies also reveal potential utility of some non-traditional case-control designs that samples controls at a smaller rate than the cases. Apart from the simulation studies, we also illustrate the different methods by analyzing interactions of two commonly studied genes, N -acetyl transferase type 2 and glutathione s -transferase M1, with smoking and dietary exposures, in a large case-control study of colorectal cancer. Genet. Epidemiol . 2008. Published 2008 Wiley-Liss, Inc.en_US
dc.format.extent181034 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherLife and Medical Sciencesen_US
dc.subject.otherGeneticsen_US
dc.titleTests for gene-environment interaction from case-control data: a novel study of type I error, power and designs This article is a US government work, and, as such, is in the public domain in the United States of America.en_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelBiological Chemistryen_US
dc.subject.hlbsecondlevelGeneticsen_US
dc.subject.hlbsecondlevelMolecular, Cellular and Developmental Biologyen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michiganen_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michiganen_US
dc.contributor.affiliationumDepartment of Internal Medicine, Epidemiology and Human Genetics, University of Michigan, Ann Arbor, Michiganen_US
dc.contributor.affiliationotherDepartment of Community Medicine and Epidemiology, Carmel Medical Center and Technion Faculty of Medicine, CHS National Israeli Cancer Control Center, Haifa, Israelen_US
dc.contributor.affiliationotherIDIBELL, Catalan Institute of Oncology, L'Hospitalet, Barcelona, Spainen_US
dc.contributor.affiliationotherDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, Maryland ; Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, Marylanden_US
dc.identifier.pmid18473390en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/61219/1/20337_ftp.pdf
dc.identifier.doihttp://dx.doi.org/10.1002/gepi.20337en_US
dc.identifier.sourceGenetic Epidemiologyen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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