Original Paper

Prioritized Subset Analysis: Improving Power in Genome-wide Association Studies
Chun Li^{a, b}, Mingyao Li^c, Ethan M. Lange^{d, e}, Richard M. Watanabe<sup>f, g

a</sup>Department of Biostatistics,
^bCenter for Human Genetics Research, Vanderbilt University School of Medicine, Nashville, Tenn.,
^cDepartment of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pa., Departments of
^dGenetics and
^eBiostatistics, University of North Carolina, Chapel Hill, N.C., Departments of
^fPreventive Medicine and
^gPhysiology & Biophysics, Keck School of Medicine of USC, Los Angeles, Calif., USA

Address of Corresponding Author

Hum Hered 2008;65:129-141 (DOI: 10.1159/000109730)

  Key Words

• Association analysis
• False discovery rate
• HapMap

  Abstract

Background: Genome-wide association studies (GWAS) are now feasible for studying the genetics underlying complex diseases. For many diseases, a list of candidate genes or regions exists and incorporation of such information into data analyses can potentially improve the power to detect disease variants. Traditional approaches for assessing the overall statistical significance of GWAS results ignore such information by inherently treating all markers equally. Methods: We propose the prioritized subset analysis (PSA), in which a prioritized subset of markers is pre-selected from candidate regions, and the false discovery rate (FDR) procedure is carried out in the prioritized subset and its complementary subset, respectively. Results: The PSA is more powerful than the whole-genome single-step FDR adjustment for a range of alternative models. The degree of power improvement depends on the fraction of associated SNPs in the prioritized subset and their nominal power, with higher fraction of associated SNPs and higher nominal power leading to more power improvement. The power improvement can be substantial; for disease loci not included in the prioritized subset, the power loss is almost negligible. Conclusion: The PSA has the flexibility of allowing investigators to combine prior information from a variety of sources, and will be a useful tool for GWAS.

Copyright © 2007 S. Karger AG, Basel

  Author Contacts

Chun Li
Center for Human Genetics Research
Vanderbilt University School of Medicine, 519 Light Hall
Nashville, TN 37232 (USA)
Tel. +1 615 322 2884, Fax +1 615 343 8619, E-Mail chun.li@vanderbilt.edu

  Article Information

C. Li and M. Li made equal contributions.

Received: March 22, 2007
Accepted after revsion: June 14, 2007
Published online: October 12, 2007
Number of Print Pages : 13
Number of Figures : 6, Number of Tables : 0, Number of References : 17