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Multiple imputation to correct for measurement error in admixture estimates in genetic structured association
Miguel A Padilla1, Jasmin Divers, Laura K Vaughan
1Department of Psychology, Old Dominion University, Norfolk, VA 23505, USA. mapadill@odu.edu
Human Heredity
|April 3, 2009
Summary
Multiple imputation (MI) corrects measurement error in admixture estimates for structured association tests (SAT). This method improves the reliability of genetic analyses, provided the data quality is sufficient for accurate corrections.
Area of Science:
- Genetics
- Statistical Genetics
- Bioinformatics
Background:
- Structured association tests (SAT) assume error-free variable measurement, but measurement error can bias estimates and confound variance.
- Admixture estimates are susceptible to measurement error, impacting the accuracy of SAT models.
- Existing statistical models face challenges in addressing measurement error in genetic analyses.
Purpose of the Study:
- To present multiple imputation (MI) as a method for correcting measurement error in SAT linear models.
- To specifically address the correction of measurement error in admixture estimates within SAT frameworks.
- To evaluate the efficacy of MI in mitigating bias caused by measurement error in genetic association studies.
Main Methods:
- Several multiple imputation (MI) techniques were investigated and compared.
- Simulations were conducted to assess the performance of MI methods.
- Type I error rates were evaluated for both additive and non-additive genotype coding schemes.
Main Results:
- Multiple imputation (MI) effectively corrects measurement error in admixture estimates within SAT linear models.
- The Rubin and Cole methods of MI demonstrated success in addressing measurement error.
- The simulations confirmed the utility of MI in enhancing the accuracy of genetic association analyses.
Conclusions:
- Multiple imputation (MI) offers a viable solution for correcting admixture measurement error in SAT linear models.
- The effectiveness of MI is contingent upon the quality and informativeness of the genetic marker data.
- High-quality data is crucial for MI to effectively borrow information and perform accurate measurement error corrections.
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