A new multimarker test for family-based association studies
Article first published online: 3 NOV 2006
© 2006 Wiley-Liss, Inc.
Volume 31, Issue 1, pages 9–17, January 2007
How to Cite
Rakovski, C. S., Xu, X., Lazarus, R., Blacker, D. and Laird, N. M. (2007), A new multimarker test for family-based association studies. Genet. Epidemiol., 31: 9–17. doi: 10.1002/gepi.20186
- Issue published online: 11 DEC 2006
- Article first published online: 3 NOV 2006
- Manuscript Accepted: 18 JUL 2006
- Manuscript Revised: 22 MAY 2006
- Manuscript Received: 2 DEC 2005
- National Institute of Health(NIH). Grant Numbers: ES007142, MH059532, MH60009-06
- tag SNP method;
- multiple SNPs;
- best strategy
We propose a new multimarker test for family-based studies in candidate genes. We use simulations under different genetic models to assess the performance of competing testing strategies, characterized in this study as combinations of the following important factors: genes, statistical tests, tag single nucleotide polymorphisms (SNP) methods, number of tag SNPs and family designs. An ANOVA model is employed to provide descriptive summaries of the effects on power of the above-mentioned factors. We find that tag SNP methods, gene characteristics and family designs have minimal impact on the best testing strategy. The familywise error rate (FWER) controlling multiple comparison procedure and the new multimarker test offer the highest power followed by the asymptotic global haplotype test.Both the FWER and the multimarker test are invariant to family designs and gain power as we increase the number of tag SNPs. However, the performance of the global haplotype test is slightly degraded when analyzing larger numbers of tag SNPs. Within the framework of our study, the best strategy for family-based studies in candidate genes that emerged from our analysis is to use the FWER or the multimarker test and select 6–10 tag SNPs using any of the tag SNP methods considered. We confirm the conclusions of our study with an application to Alzheimer's disease data. Genet. Epidemiol. © 2006 Wiley-Liss, Inc.