- Multiple imputation
- Measurement error
- Structured association testing
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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Miguel A. Padilla, PhD
Department of Psychology, Old Dominion University
250 Mills Godwin Building
Norfolk, VA 23505 (USA)
Tel. +1 757 683 4448, Fax +1 757 683 5087, E-Mail firstname.lastname@example.org
Received: June 30, 2008
Accepted after revision: November 6, 2008
Published online: April 1, 2009
Number of Print Pages : 8
Number of Figures : 0, Number of Tables : 5, Number of References : 35
Human Heredity (International Journal of Human and Medical Genetics)
Vol. 68, No. 1, Year 2009 (Cover Date: April 2009)
Journal Editor: Devoto M. (Philadelphia, Pa.)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)
For additional information: http://www.karger.com/HHE
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