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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A cost-effective statistical method to correct for differential genotype misclassification when performing

Douglas Londono1, Chad Haynes, Francisco M De La Vega

  • 1Department of Genetics, Rutgers University, Piscataway, N.J., USA.

Human Heredity
|July 8, 2010
PubMed
Summary

The likelihood ratio test allowing for differential errors (LRT(D)A(M)E) maintains accurate type I error rates in genome-wide association studies even with differential genotyping errors. This method requires minimal double-sampled data to preserve statistical power.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Differential misclassification of genotypes can impact the accuracy of genome-wide association studies (GWAS).
  • Existing methods like the likelihood ratio test allowing for errors (LRTAE) address nondifferential misclassification.
  • There is a need for statistical approaches that account for differential misclassification in GWAS.

Purpose of the Study:

  • To extend the LRTAE statistic to accommodate differential genotype misclassification between cases and controls.
  • To evaluate the performance of the new LRT(D)A(M)E statistic in GWAS simulations under differential error conditions.
  • To determine the amount of double-sampled data required to maintain statistical power and control type I error rates.

Main Methods:

  • Developed the LRT(D)A(M)E statistic, an extension of LRTAE, to handle differential genotype misclassification.
  • Simulated genetic data under null and power models with varying degrees of differential misclassification.
  • Assessed type I error rates and statistical power using permutation-based p-values for the LRT(D)A(M)E statistic.
  • Investigated different modes of inheritance (dominant, multiplicative, recessive).

Main Results:

  • The LRT(D)A(M)E statistic successfully maintained a correct type I error rate under the null model, even with differential genotyping errors.
  • As little as 10-15% double-sampled genotype data was sufficient to achieve this error rate control.
  • Approximately 15-20% double sampling was needed to maintain 80% power at a 0.05 significance level, and around 20% for a 0.01 significance level, with some variation across inheritance modes.

Conclusions:

  • The LRT(D)A(M)E statistic is a robust tool for GWAS, effectively managing differential genotype misclassification.
  • The proposed method requires a relatively small proportion of double-sampled data to ensure reliable genetic association findings.
  • This approach enhances the power and accuracy of GWAS in the presence of complex genotyping error scenarios.