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

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
Hayward_authaffil_staffdata.pdf Hayward_authaffil_staffdata.pdf application/pdf 181.76KB 0
UQ238343_authaffil_staffdata_Martin.pdf HERDC evidence - not publicly available application/pdf 491.94KB 0
UQ238343_staffdata_Hayward.pdf HERDC evidence - not publicly available application/pdf 173.64KB 0

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
Year available 2010
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2010.06.009
Open Access Status Not yet assessed
Volume 87
Issue 1
Start page 139
End page 145
Total pages 7
Place of publication United States
Publisher Cell Press
Language eng
Subject 1311 Genetics
2716 Genetics (clinical)
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
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 496675
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Medicine Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 435 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 439 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 23 Mar 2011, 22:52:17 EST by Lisa Hennell on behalf of School of Medicine