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Multiple Imputation to Correct for Measurement Error in Admixture Estimates in Genetic Structured Association TestingPadilla M.A.a, b · Divers J.c · Vaughan L.K.d · Allison D.B.d, e · Tiwari H.K.d
Departments of aPsychology and bMathematics and Statistics, Old Dominion University, Norfolk, Va., cDepartment of Biostatistical Sciences, Section on Statistical Genetics, Wake Forest University Health Sciences, Winston-Salem, N.C., dDepartment of Biostatistics, Section on Statistical Genetics and eClinical Nutrition Research Center, University of Alabama at Birmingham, Birmingham, Ala., USA Corresponding Author
Miguel A. Padilla, PhD
Department of Psychology, Old Dominion University
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Objectives: Structured association tests (SAT), like any statistical model, assumes that all variables are measured without error. Measurement error can bias parameter estimates and confound residual variance in linear models. It has been shown that admixture estimates can be contaminated with measurement error causing SAT models to suffer from the same afflictions. Multiple imputation (MI) is presented as a viable tool for correcting measurement error problems in SAT linear models with emphasis on correcting measurement error contaminated admixture estimates. Methods: Several MI methods are presented and compared, via simulation, in terms of controlling Type I error rates for both non-additive and additive genotype coding. Results: Results indicate that MI using the Rubin or Cole method can be used to correct for measurement error in admixture estimates in SAT linear models. Conclusion: Although MI can be used to correct for admixture measurement error in SAT linear models, the data should be of reasonable quality, in terms of marker informativeness, because the method uses the existing data to borrow information in which to make the measurement error corrections. If the data are of poor quality there is little information to borrow to make measurement error corrections.
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