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Valid Monte Carlo permutation tests for genetic case-control studies with missing genotypes.

Daniel D Kinnamon1, Eden R Martin

  • 1Division of Human Genetics, Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America; Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, Florida, United States of America.

Genetic Epidemiology
|April 12, 2014
PubMed
Summary
This summary is machine-generated.

Monte Carlo permutation tests for genetic association studies are valid with missing genotypes if permutations form a group and genotype distribution is invariant. Stratified permutations ensure validity when missing data depends on recorded covariates.

Keywords:
Monte Carlo permutation testscase-controlmissing genotypestype I error

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Monte Carlo permutation tests are widely used in genetic association studies.
  • Handling missing genotype data is a critical challenge for test validity.
  • Existing methods may not guarantee accurate results with missing data.

Purpose of the Study:

  • To develop a theoretical framework for validating Monte Carlo permutation tests with missing genotypes.
  • To identify conditions under which these tests maintain exact level α.
  • To propose valid testing strategies for scenarios with missing data.

Main Methods:

  • Specified a nonparametric probability model for observed genotype data in case-control studies.
  • Defined conditions for exact level α Monte Carlo permutation tests.
  • Analyzed the impact of missing data processes and covariate dependence.
  • Utilized simulations to verify theoretical findings.

Main Results:

  • Established that Monte Carlo permutation tests are exact level α if permutation sets form a group and genotype distribution is invariant under permutations.
  • Demonstrated that commonly used tests are only valid under restrictive missing data assumptions.
  • Showed that stratified permutation tests are exact level α when missing data depends on recorded covariates.

Conclusions:

  • The validity of Monte Carlo permutation tests with missing genotypes depends on the permutation set and missing data mechanism.
  • Stratified permutation tests offer a valid approach when missingness is linked to observed covariates.
  • Theoretical findings and simulations provide guidance for robust genetic association testing.