Multiple Imputation to Correct for Measurement Error in Admixture Estimates in Genetic Structured Association Testing
Year: 2009
Type: article
Abstract: 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.
Source: Human Heredity
DOI: 10.1159/000210450
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases, U.S. National Library of Medicine
Awards: G1474999166, Multiple Imputation in Genetic Studies, G2137000402, Training Program in Rheumatic Diseases Research, UAB Statistical Genetics Post-Doctoral Training Program, G453018386, Center for Nutrient-Gene Interaction in Cancer Prevention, G5501604117, Genome-wide Structured Association Testing & Regional Admixture Mapping, Program Project in the Genetics of SLE, Predictors of Treatment Response in Early Aggressive RA, Positional Gene Identification of Complex Traits, G8404489359, G9000001851, G9000196840, UAB Nutrition Obesity Research Center (NORC)
OpenAlex ID: W1985498568