title: Linkage analysis of complex traits using affected sibpairs: effects of single-locus approximations on estimates of the required sample size.
abstract: We investigated the power of the affected sibpair method for detecting a disease locus when the disease is inherited through two bi-allelic loci. The power was computed for all possible values of the gene frequencies and penetrances that lead to a given population prevalence and a given sibling relative risk. A method to generate rapidly all possible models that give a specific population prevalence and relative risk is provided. We applied it to the case of a two-locus disease with a prevalence of 10% and a low sibling relative risk of 1.5. For this particular example, regardless of the true underlying model, a sample size (N = 450 for alpha = 0.05, N = 1,500 for alpha = 0.0001) may be determined such that one would expect enough power (0.80) to detect at least one of the two disease genes. In addition to the general case, we examined a special class of models in which the marginal penetrances at each locus are either recessive or dominant. In this instance, the gene frequencies were excellent predictors of the power afforded by a particular sample size. These methods have been implemented in a C program called SIBPOWER which is freely available from the first author. With this program, investigators can perform their own power calculations for any two-locus model of their choice thus avoiding the need to use single-locus approximations that may grossly underestimate the necessary sample size.
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110, USA. alex@wubios.wustl.edu
authors.forename: A A
authors.initials: AA
authors.lastname: Todorov
authors.forename: I B
authors.initials: IB
authors.lastname: Borecki
authors.forename: D C
authors.initials: DC
authors.lastname: Rao
mesh_terms: D005787:Gene Frequency; D008040:Genetic Linkage; D006801:Humans; D008432:Mathematical Computing; D008957:Models, Genetic; D015233:Models, Statistical; D009679:Nuclear Family; D018570:Risk Assessment; D018401:Sample Size; D012984:Software