title: Interval estimation of genetic susceptibility for retrospective case-control studies.
abstract: BACKGROUND
  This article describes classical and Bayesian interval estimation of genetic susceptibility based on random samples with pre-specified numbers of unrelated cases and controls.
  
  
  RESULTS
  Frequencies of genotypes in cases and controls can be estimated directly from retrospective case-control data. On the other hand, genetic susceptibility defined as the expected proportion of cases among individuals with a particular genotype depends on the population proportion of cases (prevalence). Given this design, prevalence is an external parameter and hence the susceptibility cannot be estimated based on only the observed data. Interval estimation of susceptibility that can incorporate uncertainty in prevalence values is explored from both classical and Bayesian perspective. Similarity between classical and Bayesian interval estimates in terms of frequentist coverage probabilities for this problem allows an appealing interpretation of classical intervals as bounds for genetic susceptibility. In addition, it is observed that both the asymptotic classical and Bayesian interval estimates have comparable average length. These interval estimates serve as a very good approximation to the "exact" (finite sample) Bayesian interval estimates. Extension from genotypic to allelic susceptibility intervals shows dependency on phenotype-induced deviations from Hardy-Weinberg equilibrium.
  
  
  CONCLUSIONS
  The suggested classical and Bayesian interval estimates appear to perform reasonably well. Generally, the use of exact Bayesian interval estimation method is recommended for genetic susceptibility, however the asymptotic classical and approximate Bayesian methods are adequate for sample sizes of at least 50 cases and controls.
authors.affiliation: Department of Population Genetics, GlaxoSmithKline Inc, Research Triangle Park NC 27709, USA. dmitri.v.zaykin@gsk.com
authors.forename: Dmitri V
authors.initials: DV
authors.lastname: Zaykin
authors.forename: Zhaoling
authors.initials: Z
authors.lastname: Meng
authors.forename: Sujit K
authors.initials: SK
authors.lastname: Ghosh
mesh_terms: D000483:Alleles; D001499:Bayes Theorem; D016022:Case-Control Studies; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D017720:Molecular Epidemiology / Q000379:methods / Q000706:statistics & numerical data; D010597:Pharmacogenetics / Q000379:methods / Q000706:statistics & numerical data; D015995:Prevalence; D012189:Retrospective Studies; D018401:Sample Size