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Related Experiment Videos

Quantifying the percent increase in minimum sample size for SNP genotyping errors in genetic model-based association

Sun Jung Kang1, Stephen J Finch, Chad Haynes

  • 1Duke University Medical Center, Durham, NC, USA.

Human Heredity
|April 7, 2005
PubMed
Summary

Genotyping errors in genetic association studies significantly increase required sample size, especially for SNPs with low minor allele frequency. Careful attention to error rates is crucial for maintaining study power.

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Area of Science:

  • Genetics
  • Epidemiology
  • Biostatistics

Background:

  • Genotype misclassification errors can impact the power of genetic association studies.
  • Previous work by Kang et al. quantified these costs for single nucleotide polymorphisms (SNPs) in a model-free setting.

Purpose of the Study:

  • To evaluate the cost of genotype misclassification errors within a genetic model-based framework.
  • To determine the minimum percentage increase in sample size necessary (%MSSN) to maintain study power and significance levels under various genetic models.

Main Methods:

  • Utilized a genetic model-based framework considering disease models (dominant, recessive), genotypic relative risk, allele frequencies, and linkage disequilibrium.
  • Employed a linear Taylor series expansion of the non-centrality parameter of the 2 x 3 chi2 test to approximate %MSSN.

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  • Assumed zero misclassification errors between homozygotes.

    • Main Results:

    • Identified specific genetic model parameter settings that result in substantial %MSSN for both dominant and recessive models.
    • Demonstrated that %MSSN increases without bound as SNP minor allele frequency approaches zero.
    • Established %MSSN as a complex function of genetic model parameters.

    Conclusions:

    • SNPs with small minor allele frequencies necessitate rigorous control of genotyping error rates to achieve desired study power.
    • The findings underscore the importance of considering genotype error costs in study design, particularly for low-frequency variants.
    • Available software can assist in calculating these costs for study design.