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Power and sample size calculations for case-control genetic association tests when errors are present: application to

Derek Gordon1, Stephen J Finch, Michael Nothnagel

  • 1Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. gordon@linkage.rockefeller.edu

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Summary

Genotyping errors significantly increase the required sample size for genetic association studies. Even small error increases necessitate larger sample sizes to maintain study power and accuracy.

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Area of Science:

  • Population genetics
  • Statistical genetics
  • Genetic epidemiology

Background:

  • Accurate genetic association studies are crucial for understanding disease etiology.
  • Genotyping errors can compromise the reliability of case-control study findings.
  • Quantifying the impact of such errors is essential for robust study design.

Purpose of the Study:

  • To quantify the impact of genotyping errors on statistical power and sample size.
  • To evaluate these effects for case-control genetic association studies using di-allelic markers like SNPs.
  • To assess the influence of different genotyping error models on study outcomes.

Main Methods:

  • Utilized three published models of genotyping errors applied to the chi-square test for independence.
  • Specified genotype frequencies conditional on disease status and error models within genetic and model-free frameworks.
  • Computed asymptotic power and sample size necessary (SSN) using the non-centrality parameter.

Main Results:

  • Increased genotyping error rates necessitate a larger sample size necessary (SSN).
  • A 1% increase in total genotyping error rates requires a 2-8% increase in SSN for both cases and controls.
  • SSN is a nonlinear function of linkage disequilibrium (LD) and genotyping error rates, with higher SSN observed for lower LD and higher error rates.

Conclusions:

  • Genotyping errors substantially inflate the required sample size for genetic association studies.
  • The combined effects of low LD and high genotyping error rates disproportionately increase SSN.
  • Accurate genotyping is critical for efficient and reliable genetic association study design.