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Related Experiment Videos

Errors and linkage disequilibrium interact multiplicatively when computing sample sizes for genetic case-control

D Gordon1, M A Levenstien, S J Finch

  • 1Laboratory of Statistical Genetics, Rockefeller University, 1230 York Avenue, New York, NY 10021-6399, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2003
PubMed
Summary
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Genotype errors and linkage disequilibrium (LD) significantly impact sample size in genetic association studies. Higher error rates reduce statistical power, necessitating larger sample sizes, especially with imperfect LD.

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Case-control studies commonly use single nucleotide polymorphisms (SNPs) to identify disease associations.
  • These studies often assume error-free genotype data, which may not reflect real-world scenarios.
  • Linkage disequilibrium (LD) between disease and marker loci is crucial for association studies.

Purpose of the Study:

  • To quantify the impact of genotype errors on sample size requirements in case-control studies.
  • To investigate the combined effects of LD and genotyping errors on study power.
  • To develop a model for estimating necessary sample sizes under various error and LD conditions.

Main Methods:

  • Utilized a 2x3 chi-square analysis framework incorporating a published error model.

Related Experiment Videos

  • Examined the joint influence of LD (D') and error rates (S) on sample size for six distinct genetic disease models and allele frequency settings.
  • Employed backward stepwise regression to estimate minimal sample size as a 4th-degree polynomial function of S and D'.

    • Main Results:

    • Increased genotype error rates were found to decrease statistical power.
    • LD and genotyping errors exhibit a significant non-linear interaction affecting sample size.
    • Higher-order interaction terms between LD and errors were statistically significant, explaining over 99.99% of the variance in sample size.
    • The required sample size increase to maintain power was smallest under perfect LD (D'=1) and increased monotonically as LD decreased.

    Conclusions:

    • Genotype errors are a critical factor that must be accounted for in sample size calculations for genetic association studies.
    • The interplay between LD and genotyping errors necessitates careful consideration to ensure adequate statistical power.
    • Accurate sample size estimation requires models that incorporate both LD measures and anticipated error rates.