A versatile gene-based test for genome-wide association studies

Liu, Jimmy Z., McRae, Allan F., Nyholt, Dale R., Medland, Sarah E., Wray, Naomi R., Brown, Kevin M., AMFS Investigators, Hayward, Nicholas K., Montgomery, Grant W., Visscher, Peter M., Martin, Nicholas G. and Macgregor, Stuart (2010) A versatile gene-based test for genome-wide association studies. American Journal of Human Genetics, 87 1: 139-145. doi:10.1016/j.ajhg.2010.06.009

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Author Liu, Jimmy Z.
McRae, Allan F.
Nyholt, Dale R.
Medland, Sarah E.
Wray, Naomi R.
Brown, Kevin M.
AMFS Investigators
Hayward, Nicholas K.
Montgomery, Grant W.
Visscher, Peter M.
Martin, Nicholas G.
Macgregor, Stuart
Title A versatile gene-based test for genome-wide association studies
Journal name American Journal of Human Genetics   Check publisher's open access policy
ISSN 0002-9297
Publication date 2010-07-09
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2010.06.009
Volume 87
Issue 1
Start page 139
End page 145
Total pages 7
Place of publication United States
Publisher Cell Press
Collection year 2011
Language eng
Abstract We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in ∼1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application. © 2010 The American Society of Human Genetics. All rights reserved.
Keyword Sequence variants
Linkage
Pathway
Height
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Medicine Publications
Official 2011 Collection
 
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Created: Wed, 23 Mar 2011, 12:52:17 EST by Lisa Hennell on behalf of School of Medicine