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The effect of phenotype variation on detection of linkage in the COGA data
dc.contributor.authorBirznieks, Guntheren_US
dc.contributor.authorGhosh, Soumitraen_US
dc.contributor.authorWatanabe, Richard M.en_US
dc.contributor.authorMitchell, Braxton D.en_US
dc.date.accessioned2013-12-04T18:57:35Z
dc.date.available2013-12-04T18:57:35Z
dc.date.issued1999en_US
dc.identifier.citationBirznieks, Gunther; Ghosh, Soumitra; Watanabe, Richard M.; Mitchell, Braxton D. (1999). "The effect of phenotype variation on detection of linkage in the COGA data." Genetic Epidemiology 17(S1): S61-S66. <http://hdl.handle.net/2027.42/101827>en_US
dc.identifier.issn0741-0395en_US
dc.identifier.issn1098-2272en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/101827
dc.description.abstractError in phenotypic measurement can significantly compromise ability to detect linkage. We assessed the impact of introducing phenotypic measurement error on our ability to detect a quantitative trait locus in the Collaborative Study on the Genetics of Alcoholism (COGA) data. The impact of introducing three different types of errors was evaluated: 1) errors generated by sampling from a normal distribution; 2) errors generated by permuting phenotype values between subjects; and 3) errors generated by sampling from a uniform error distribution.en_US
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherAlcoholismen_US
dc.subject.otherQuantitative Traitsen_US
dc.subject.otherSensitivity Analysisen_US
dc.subject.otherPhenotypesen_US
dc.titleThe effect of phenotype variation on detection of linkage in the COGA dataen_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.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michiganen_US
dc.contributor.affiliationotherDepartment of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texasen_US
dc.contributor.affiliationotherGenetics and Molecular Biology Branch, National Human Genome Research Institute, Bethesda, Marylanden_US
dc.identifier.pmid10597413en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/101827/1/1370170711_ftp.pdf
dc.identifier.doi10.1002/gepi.1370170711en_US
dc.identifier.sourceGenetic Epidemiologyen_US
dc.identifier.citedreferenceBegleiter H, Porjesz B, Reich T, Edenberg HJ, Goate A, Blangero J, Almasy L, Foroud T, Van Eerdewegh P, Polich J, Rohrbaugh J, Kuperman S, Bauer LO, O'Connor SJ, Chorlian DB, Li TK, Conneally PM, Hesselbrock V, Rice JP, Schuckit MA, Cloninger R, Nurnberger J Jr, Crowe R, Bloom FE ( 1998 ): Quantitative trait loci analysis of human event‐related brain potentials: P3 voltage. Electroencephalogr Clin Neurophysiol 108: 244 – 250.en_US
dc.identifier.citedreferenceAmos CI ( 1994 ): Robust variance‐components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54: 535 – 543.en_US
dc.identifier.citedreferenceAlmasy L, Blangero J ( 1998 ): Multipoint quantitative trait linkage analysis in general pedigrees. Am J Hum Genet 62: 1198 – 1211.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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