Tradeoff between no-call reduction in genotyping error rate and loss of sample size for genetic case/control association studies.

S. J. Kang, D. Gordon, A. M. Brown, J. Ott, S. J. Finch

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Single nucleotide polymorphisms (SNP) may be genotyped for use in case-control designs to test for association between a SNP marker and a disease using a 2 x 3 chi-squared test of independence. Genotyping is often based on underlying continuous measurements, which are classified into genotypes. A "no-call" procedure is sometimes used in which borderline observations are not classified. This procedure has the simultaneous effect of reducing the genotype error rate and the expected number of genotypes observed. Both quantities affect the power of the statistic. We develop methods for calculating the genotype error rate, the expected number of genotypes observed, and the expected power of the resulting test as a function of the no-call procedure. We examine the statistical properties of the chi-squared test using a no-call procedure when the underlying continuous measure of genotype classification is a three-component mixture of univariate normal distributions under a range of parameter specifications. The genotype error rate decreases as the no-call region is increased. The expected number of observations genotyped also decreases. Our key finding is that the expected power of the chi-squared test is not sensitive to the no-call procedure. That is, the benefits of reduced genotype error rate are almost exactly balanced by the losses due to reduced genotype observations. For an underlying univariate normal mixture of genotype classification to be analyzed with a 2 x 3 chi-squared test, there is little, if any, increase in power using a no-call procedure.

Original languageEnglish (US)
Pages (from-to)116-127
Number of pages12
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
StatePublished - 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computational Theory and Mathematics

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