Skip Navigation



Bioinformatics Advance Access published online on December 15, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn641
This Article
Right arrow Advance Access manuscript (PDF)
Right arrow All Versions of this Article:
25/4/497    most recent
btn641v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Li, M.
Right arrow Articles by Li, C.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Li, M.
Right arrow Articles by Li, C.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

ATOM: A Powerful Gene-Based Association Test by Combining Optimally Weighted Markers

Mingyao Li 1,*, Kai Wang 2, Struan F.A. Grant 2, Hakon Hakonarson 2 and Chun Li 3,*

1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104.
2Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104.
3Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232.

*To whom correspondence should be addressed. Dr. Mingyao Li, E-mail: mingyao{at}mail.med.upenn.edu


  Abstract

Background: Large-scale candidate-gene and genome-wide association studies genotype multiple SNPs within or surrounding a gene, including both tag and functional SNPs. The immense amount of data generated in these studies poses new challenges to analysis. One particularly challenging yet important question is how to best use all genetic information to test whether a gene or a region is associated with the trait of interest.

Methods: Here we propose a powerful gene-based Association Test by combining Optimally Weighted Markers (ATOM) within a genomic region. Due to variation in linkage disequilibrium, different markers often associate with the trait of interest at different levels. To appropriately apportion their contributions, we assign a weight to each marker that is proportional to the amount of information it captures about the trait locus. We analytically derive the optimal weights for both quantitative and binary traits, and describe a procedure for estimating the weights from a reference database such as the HapMap. Compared to existing approaches, our method has several distinct advantages, including 1) the ability to borrow information from an external database to increase power, 2) the theoretical derivation of optimal marker weights, and 3) the scalability to simultaneous analysis of all SNPs in candidate genes and pathways.

Results: Through extensive simulations and analysis of the FTO gene in our ongoing genome-wide association study on childhood obesity, we demonstrate that ATOM increases the power to detect genetic association as compared to several commonly used multi-marker association tests.

Contact: mingyao{at}mail.med.upenn.edu

Associate Editor: Prof. Martin Bishop

Received on August 20, 2008; revised on December 11, 2008; accepted on December 11, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.