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Despite tremendous efforts, few genes involved in the susceptibility for complex disorders have been identified. One explanation is that these disorders are a result of an interaction between genes and environment, and under such conditions, it may be difficult to measure the true genetic effect without accounting for the interaction. Umbach and Weinberg ([2000] Am. J. Hum. Genet. 66:251–261) proposed an association test which looks at the joint effects of genotype and environment, using case-parent trios. In this study, we explore under which conditions accounting for G×E interaction enhances one's ability to detect the role of genetic factors in complex diseases. Using asymptotic power calculations, we investigate the power to detect the gene effect over varying exposure frequencies and different scenarios of G×E interaction. We show that for a given sample size, interaction scenario, and allele frequency, the actual gain in power while accounting for the interaction depends on the magnitude of the exposure frequency: the largest gains are seen for relatively low exposure frequencies. Moreover, a loss of power can be observed when the exposure is frequent and/or the exposure effect is strong. If we consider a gene with a disease allele frequency of 0.2, with no effect in the absence of exposure, an exposure with a 10-fold increase risk and a G×E relative risk of 2, then when the exposure frequency is 0.1, accounting for G×E interaction increases the power to detect the gene effect in 200 trios by 10%; alternatively, when the exposure frequency is 0.9, it decreases the power by 15%. Genet Epidemiol 24:200–207, 2003. © 2003 Wiley-Liss, Inc.

Not accounting for interaction in association analyses may reduce the power to detect the variants involved. We investigate the powers of different designs to detect under two-locus models the effect of disease-causing variants among several hundreds of markers using family-based association tests by simulation. This setting reflects realistic situations of exploration of linkage regions or of biological pathways. We define four strategies: (S1) single-marker analysis of all Single Nucleotide Polymorphisms (SNPs), (S2) two-marker analysis of all possible SNPs pairs, (S3) lax preliminary selection of SNPs followed by a two-marker analysis of all selected SNP pairs, (S4) stringent preliminary selection of SNPs, each being later paired with all the SNPs for two-marker analysis. Strategy S2 is never the best design, except when there is an inversion of the gene effect (flip-flop model). Testing individual SNPs (S1) is the most efficient when the two genes act multiplicatively. Designs S3 and S4 are the most powerful for nonmultiplicative models. Their respective powers depend on the level of symmetry of the model. Because the true genetic model is unknown, we cannot conclude that one design outperforms another. The optimal approach would be the two-step strategy (S3 or S4) as it is often the most powerful, or the second best. Genet.

The mean identity-by-descent (IBD) specification used in the Generalized Estimating Equations (GEE) methodology for linkage is only valid, strictly speaking, under the assumption of fully polymorphic markers. In practice, markers often provide only partial IBD information, which can potentially result in inconsistency of the locus location and gene effect estimates obtained by the GEE method. Using both simulations and theory, we identify some realistic conditions about marker information under which the validity of the GEE linkage methods may be arguable. Namely, researchers should not trust the GEE parameters' estimates and their associated confidence intervals in areas of the genome where IBD information is sparse or when this information changes abruptly. We show that properly standardized statistics based on IBD sharing provide a valid alternative.