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Increase of Rejection Rate in Case-Control Studies with the Differential Genotyping Error Rates

Author

Listed:
  • Ahn Kwangmi

    (Penn State School of Medicine)

  • Gordon Derek

    (Rutgers University)

  • Finch Stephen J

    (Stony Brook University)

Abstract

Genotyping error adversely affects the statistical power of case-control association studies and introduces bias in the estimated parameters when the same error mechanism and probabilities apply to both affected and unaffected individuals; that is, when there is non-differential genotype misclassification. Simulation studies have shown that differential genotype misclassification leads to a rejection rate that is higher than the nominal significance level (type I error rate) for some tests of association.This study extends previous work by examining this issue analytically using the non-centrality parameter of the asymptotic distribution of the chi-squared test and linear trend test (LTT) when there is no difference between case and control genotype frequencies, but there is differential misclassification with SNP data. The parameters examined are the minor allele frequency (MAF) and sample size.When MAF is less than 0.2, differential genotyping errors lead to a rejection rate much larger than the nominal significance level. As the MAF decreases to zero, the increase in the rejection rate becomes larger. The errors that most increase the rejection rate are differential recording of the more common homozygote as the other homozygote and differential recording of the more common homozygote as the heterozygote. The rejection rate increases as the sample size increases for fixed differential genotyping error rates and nominal significance level for each test.

Suggested Citation

  • Ahn Kwangmi & Gordon Derek & Finch Stephen J, 2009. "Increase of Rejection Rate in Case-Control Studies with the Differential Genotyping Error Rates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-11, May.
    • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:25
    DOI: 10.2202/1544-6115.1429
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    References listed on IDEAS

    as
    1. Wonkuk Kim & Derek Gordon & Jonathan Sebat & Kenny Q Ye & Stephen J Finch, 2008. "Computing Power and Sample Size for Case-Control Association Studies with Copy Number Polymorphism: Application of Mixture-Based Likelihood Ratio Test," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-9, October.
    2. Vincent Plagnol & Jason D Cooper & John A Todd & David G Clayton, 2007. "A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies," PLOS Genetics, Public Library of Science, vol. 3(5), pages 1-9, May.
    3. Tintle Nathan L & Gordon Derek & McMahon Francis J & Finch Stephen J, 2007. "Using Duplicate Genotyped Data in Genetic Analyses: Testing Association and Estimating Error Rates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-29, February.
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    Cited by:

    1. Attia John & Thakkinstian Ammarin & McElduff Patrick & Milne Elizabeth & Dawson Somer & Scott Rodney J & Klerk Nicholas de & Armstrong Bruce & Thompson John, 2010. "Detecting Genotyping Error Using Measures of Degree of Hardy-Weinberg Disequilibrium," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-17, January.
    2. Andrew McDavid & Paul K Crane & Katherine M Newton & David R Crosslin & Wayne McCormick & Noah Weston & Kelly Ehrlich & Eugene Hart & Robert Harrison & Walter A Kukull & Carla Rottscheit & Peggy Peiss, 2013. "Enhancing the Power of Genetic Association Studies through the Use of Silver Standard Cases Derived from Electronic Medical Records," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.

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    Keywords

    misclassification; genotyping error;

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