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Bioinformatics Advance Access originally published online on December 15, 2008
Bioinformatics 2009 25(4):497-503; doi:10.1093/bioinformatics/btn641
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© 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, 2Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 and 3Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

*To whom correspondence should be addressed.


  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 with existing approaches, our method has several distinct advantages, including (i) the ability to borrow information from an external database to increase power, (ii) the theoretical derivation of optimal marker weights and (iii) 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 with several commonly used multi-marker association tests.

Contact: mingyao{at}mail.med.upenn.edu; chun.li{at}vanderbilt.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

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

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