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

  • Kwangmi Ahn , Derek Gordon and Stephen J Finch
Published/Copyright: May 7, 2009
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 8 Issue 1

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.

Keywords: misclassification; genotyping error
Published Online: 2009-5-7

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

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https://doi.org/10.2202/1544-6115.1429
Keywords for this article
misclassification; genotyping error

Articles in the same Issue

  1. Article
  2. Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
  3. Orthology-Based Multilevel Modeling of Differentially Expressed Mouse and Human Gene Pairs
  4. Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis
  5. Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study
  6. A Nonlinear Mixed-Effects Model for Estimating Calibration Intervals for Unknown Concentrations in Two-Color Microarray Data with Spike-Ins
  7. Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates
  8. A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms
  9. Hypothesis Tests for Point-Mass Mixture Data with Application to `Omics Data with Many Zero Values
  10. Inferring Dynamic Genetic Networks with Low Order Independencies
  11. Normalization Method for Transcriptional Studies of Heterogeneous Samples - Simultaneous Array Normalization and Identification of Equivalent Expression
  12. A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers
  13. Modified FDR Controlling Procedure for Multi-Stage Analyses
  14. Detecting Outlier Samples in Microarray Data
  15. Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies
  16. Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis
  17. Score Statistics for Mapping Quantitative Trait Loci
  18. Impact of Population Stratification on Family-Based Association Tests with Longitudinal Measurements
  19. A Multilocus Model for Constructing a Linkage Disequilibrium Map in Human Populations
  20. Testing of Chromosomal Clumping of Gene Properties
  21. Balanced Gradient Boosting from Imbalanced Data for Clinical Outcome Prediction
  22. Univariate Shrinkage in the Cox Model for High Dimensional Data
  23. Multilevel Comparison of Dendrograms: A New Method with an Application for Genetic Classifications
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  25. Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness
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  27. A Parametric Model for Analyzing Anticipation in Genetically Predisposed Families
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