Increased power for case-control studies of single nucleotide polymorphisms through incorporation of family history and genetic constraints.
Association studies assessing the relationship between a common polymorphism and disease generally compare allele frequencies in cases and controls. In such studies, a limited amount of information is often available about disease incidence in relatives. We hypothesised that more power could be obtained by incorporating the constraints imposed by the properties of a genetic polymorphism, and that power could be further increased by using family history (FH) information. We have developed a simple method for incorporating basic FH information from cases and controls into a genetic association study, assuming Hardy-Weinberg equilibrium (HWE) in the general population. We model the likelihood of the data in terms of the allele frequency and its relative risk (RR) of disease and perform likelihood ratio tests. Using simulations, we compared the power to detect an association using this approach with that of a 2 x 2 chi-squared test, for a range of disease models. The sample size required to detect an association is consistently lower for tests including the HWE constraint, with the largest reduction for more common alleles. The required sample size is reduced further by stratifying by FH. Stratifying by FH also improves the precision of the RR estimates. In situations where basic FH data are already available, this study shows that efficiency can be improved by the inclusion of even this small amount of extra information.
Thompson
Deborah
D
Unit of Genetic Epidemiology, International Agency for Research on Cancer, Lyon, France.
Witte
John S
JS
Slattery
Martha
M
Goldgar
David
D
D000483:Alleles; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005838:Genotype; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*