Design considerations for association studies of candidate genes in families

In genetic epidemiologic studies, investigators often use generalized linea r models to evaluate the relationships between a disease trait and covariat es, such as one or more candidate genes or an environmental exposure.-Recen tly, attention has turned to study designs that mandate the inclusion of fa mily members in addition to a proband. Standard models for analysis assume independent observations, which is unlikely to be true for family data, and the usual standard errors for the regression parameter estimates may be to o large or too small, depending on the distribution of the covariates withi n and between families. The consequences of familial correlation on the stu dy efficiency can be measured by a design effect that is equivalent to the relative information in a sample of unrelated individuals compared to a sam ple of families with the same number of individuals. We examine design effe cts for studies in association, and illustrate how the design effect is inf luenced by the intra-familial distribution of covariate values such as woul d be expected for a candidate gene. Typical design effects for a candidate gene range between 1.1 and 2.4, depending on the size of the family and the amount of unexplained familial correlation. These values correspond to a m odest 10% increase in the required sample size up to more than doubling the requirements. Design effect values are useful in study design to compare t he efficiency of studies that sample families versus independent individual s and to determine sample size requirements that account for familial corre lation. (C) 2001 Wiley-Liss, Inc.