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Disease model distortion in association studies
Author(s) -
Vukcevic Damjan,
Hechter Eliana,
Spencer Chris,
Donnelly Peter
Publication year - 2011
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20576
Subject(s) - multiplicative function , linkage disequilibrium , genetic association , genome wide association study , single nucleotide polymorphism , genetics , allele , statistics , dominance (genetics) , biology , econometrics , mathematics , genotype , gene , mathematical analysis
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 r 4 , where r 2 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 r 2 . 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. Genet. Epidemiol . 35: 278‐290, 2011. © 2011 Wiley‐Liss, Inc.