Multistage analysis strategies for genome-wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16
Version of Record online: 18 NOV 2009
© 2009 Wiley-Liss, Inc.
Supplement: Genetic Analysis Workshop 16: Approaches to Analysis of Genome-Wide Data
Volume 33, Issue Supplement 1, pages S19–S23, 2009
How to Cite
Neuman, R. J. and Sung, Y. J. (2009), Multistage analysis strategies for genome-wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16. Genet. Epidemiol., 33: S19–S23. doi: 10.1002/gepi.20467
- Issue online: 18 NOV 2009
- Version of Record online: 18 NOV 2009
- NIH. Grant Number: R01 GM031575
- National Institute of General Medical Sciences. Grant Number: GM28719
- elastic net;
- two-locus models;
- sliding window
This contribution summarizes the work done by six independent teams of investigators to identify the genetic and non-genetic variants that work together or independently to predispose to disease. The theme addressed in these studies is multistage strategies in the context of genome-wide association studies (GWAS). The work performed comes from Group 3 of the Genetic Analysis Workshop 16 held in St. Louis, Missouri in September 2008. These six studies represent a diversity of multistage methods of which five are applied to the North American Rheumatoid Arthritis Consortium rheumatoid arthritis case-control data, and one method is applied to the low-density lipoprotein phenotype in the Framingham Heart Study simulated data. In the first stage of analyses, the majority of studies used a variety of screening techniques to reduce the noise of single-nucleotide polymorphisms purportedly not involved in the phenotype of interest. Three studies analyzed the data using penalized regression models, either LASSO or the elastic net. The main result was a reconfirmation of the involvement of variants in the HLA region on chromosome 6 with rheumatoid arthritis. The hope is that the intense computational methods highlighted in this group of papers will become useful tools in future GWAS. Genet. Epidemiol. 33 (Suppl. 1):S19–S23, 2009. © 2009 Wiley-Liss, Inc.