The impact of genotype misclassification errors on the power to detect a gene-environment interaction using cox proportional hazards modeling

Lin Tung, Derek Gordon, Stephen J. Finch

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

This paper extends gene-environment (G x E) interaction study designs in which the gene (G) is known and the environmental variable (E) is specified to the analysis of 'time-to-event' data, using Cox proportional hazards (PH) modeling. The objectives are to assess whether a random sample of subjects can be used to detect a specific G x E interaction and to study the sensitivity of the power of PH modeling to genotype misclassification. We find that a random sample of 2,100 is sufficient to detect a moderate G x E interaction. The increase in sample size necessary (SSN) to maintain Type I and Type II error rates is calculated for each of the 6 genotyping errors for both dominant and recessive modes of inheritance (MOI). The increase in SSN required is relatively small when each genotyping error rate is less than 1% and the disease allele frequency is between 0.2 and 0.5. The genotyping errors that require the greatest increase in SSN are any misclassification of a subject without the at-risk genotype as having the at-risk genotype. Such errors require an indefinitely large increase in SSN as the disease allele frequency approaches 0, suggesting that it is especially important that subjects recorded as having the at-risk genotype be correctly genotyped. Additionally, for a dominant MOI, large increases in SSN can occur with large disease allele frequency.

Original languageEnglish (US)
Pages (from-to)101-110
Number of pages10
JournalHuman Heredity
Volume63
Issue number2
DOIs
StatePublished - Feb 1 2007

All Science Journal Classification (ASJC) codes

  • Medicine(all)
  • Genetics
  • Genetics(clinical)

Fingerprint Dive into the research topics of 'The impact of genotype misclassification errors on the power to detect a gene-environment interaction using cox proportional hazards modeling'. Together they form a unique fingerprint.

Cite this