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Ann Hum Genet. 2007 Nov;71(Pt 6):828-42. Epub 2007 May 22.

Sample size needed to detect gene-gene interactions using linkage analysis.

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  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA. sw2206@columbia.edu

Abstract

Gene-gene interactions have received much attention recently because most human traits may be under the control of several genetic factors, as well as environmental factors, and these factors likely interact among each other to influence these traits. Gauderman (2002) and Wang & Zhao (2003) have reported systematic studies on the statistical power to detect gene-gene interactions through association studies. In this article we investigated the power of the affected sib pair (ASP) design to detect gene-gene interaction at two disease loci. Different definitions of gene-gene interaction were considered and different disease models (including both logistic models considered in previous studies and several two-locus models with fixed penetrances) were examined. Our results indicate that comparisons between power to detect gene-gene interaction using ASP designs and association designs heavily depend on the definition of gene-gene interaction. Under the definition of gene-gene interaction with departure from independence between two marginal IBD sharings, the association design is much more powerful than the ASP design, and the additive model is more powerful than dominant and recessive models for rare diseases, while for common diseases for example with a population prevalence of 10%, the recessive model is more powerful than the additive and dominant models. Under the definitions of departure from a multiplicative model, additive model, and heterogeneity model (Risch, 1990), the ASP design is as powerful as, or more powerful than, both family-based and population-based association designs for rare disease,, but less powerful for more common diseases. Under the definition of correlation between two marginal IBD sharings, the association design is much more powerful than the ASP design.

PMID:
17521308
[PubMed - indexed for MEDLINE]
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