Journal
GENETIC EPIDEMIOLOGY
Volume 28, Issue 2, Pages 138-156Publisher
WILEY
DOI: 10.1002/gepi.20049
Keywords
case-control studies; case-only designs; conditional likelihood; conditional logistic regression; family-based studies; gene-environment independence; gene-environment interactions; genetic epidemiology; matched case-control studies; population stratification
Funding
- NCI NIH HHS [CA-57030] Funding Source: Medline
- NIEHS NIH HHS [P30-ES09106] Funding Source: Medline
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Family-based case-control studies are popularly used to study the effect of genes and gene-environment interactions in the etiology of rare complex diseases. We consider methods for the analysis of such studies under the assumption that genetic susceptibility (G) and environmental exposures (E) are independently distributed of each other within families in the source population. Conditional logistic regression, the traditional method of analysis of the data, fails to exploit the independence assumption and hence can be inefficient. Alternatively, one can estimate the multiplicative interaction between G and E more efficiently using cases only, but the required population-based G-E independence assumption is very stringent. In this article, we propose a novel conditional likelihood framework for exploiting the within-family G-E independence assumption. This approach leads to a simple and yet highly efficient method of estimating interaction and various other risk parameters of scientific interest. Moreover, we show that the same paradigm also leads to a number of alternative and even more efficient methods for analysis of family-based case-control studies when parental genotype information is available on the case-control study participants. Based on these methods, we evaluate different family-based study designs by examining their relative efficiencies to each other and their efficiencies compared to a population-based case-control design of unrelated subjects. These comparisons reveal important design implications. Extensions of the methodologies for dealing with complex family studies are also discussed. (C) 2004 Wiley-Liss, Inc.
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