Linkage analysis with gene-environment interaction: model illustration and performance of ordered subset analysis
Article first published online: 2 MAY 2006
© 2006 Wiley-Liss, Inc.
Volume 30, Issue 5, pages 409–422, July 2006
How to Cite
Schmidt, S., Schmidt, M. A., Qin, X., Martin, E. R. and Hauser, E. R. (2006), Linkage analysis with gene-environment interaction: model illustration and performance of ordered subset analysis. Genet. Epidemiol., 30: 409–422. doi: 10.1002/gepi.20152
- Issue published online: 8 JUN 2006
- Article first published online: 2 MAY 2006
- Manuscript Accepted: 16 MAR 2006
- Manuscript Revised: 17 FEB 2006
- Manuscript Received: 7 NOV 2005
- National Institute of Health. Grant Numbers: NEI R03-EY015216, NIMH R01-MH595228, NIA R01-AG20135
- Neurosciences Education and Research Foundation.
- complex human disease;
- genetic heterogeneity;
The ordered subset analysis (OSA) method allows for the incorporation of covariates into the linkage analysis of a dichotomous disease phenotype in order to reduce genetic heterogeneity. Complex human diseases may involve gene-environment (G × E) interactions, which represent a special form of heterogeneity. Here, we present results of a simulation study to evaluate the performance of OSA when the disease-generating mechanism includes G × E interaction, in the absence of main effects of gene and environment. First, the complex simulation models are illustrated graphically. Second, we show that OSA is underpowered to detect small to moderate interaction effects, consistent with previous evaluations of other linkage analysis methods. When interaction effects are large enough to produce substantial marginal effects, standard linkage methods have sufficient power to detect significant baseline linkage evidence in the entire dataset. The power of OSA to improve upon a high baseline lod score is then strongly dependent on the underlying genetic model, especially the susceptibility allele frequency. If significant, OSA identifies family subsets that are more efficient for follow-up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E)=10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per-genotype efficiency in the subset is 20–30% greater than in the entire dataset. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc.