Comparative Power of Family-Based Association Strategies to Detect Disease-Causing Variants Under Two-Locus Models
Article first published online: 9 AUG 2012
© 2012 Wiley Periodicals, Inc.
Volume 36, Issue 8, pages 848–855, December 2012
How to Cite
Babron, M.-C., Guilloud-Bataille, M., Sahbatou, M., Demenais, F., Génin, E. and Dizier, M.-H. (2012), Comparative Power of Family-Based Association Strategies to Detect Disease-Causing Variants Under Two-Locus Models. Genet. Epidemiol., 36: 848–855. doi: 10.1002/gepi.21672
- Issue published online: 14 NOV 2012
- Article first published online: 9 AUG 2012
- Manuscript Accepted: 2 JUL 2012
- Manuscript Revised: 15 JUN 2012
- Manuscript Received: 24 NOV 2011
- Paris Diderot University
- French National Agency for Research. Grant Number: ANR-11 BSV1-027-01
- family-based association study;
- gene-gene interaction;
- study design;
- two-locus models;
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.