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Most findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a single nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically. We show that imperfect linkage disequilibrium (LD) between causal and marker loci distorts disease models, with the power to detect such departures dropping off very quickly: decaying as a function of r4, where r2 is the usual correlation between the causal and marker loci, in contrast to the well-known result that power to detect a multiplicative effect decays as a function of r2. We perform a simulation study with empirical patterns of LD to assess how this disease model distortion is likely to impact GWAS results. Among loci where association is detected, we observe that there is reasonable power to detect substantial deviations from the multiplicative model, such as for dominant and recessive models. Thus, it is worth explicitly testing for such deviations routinely.

Original publication




Journal article


Genet Epidemiol

Publication Date





278 - 290


case-control, epistasis, genome-wide association study (GWAS), interaction, linkage disequilibrium (LD), nonadditive, nonmultiplicative, tag SNP, Alleles, Case-Control Studies, Computer Simulation, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Models, Genetic, Polymorphism, Single Nucleotide