期刊
HUMAN HEREDITY
卷 65, 期 3, 页码 129-141出版社
KARGER
DOI: 10.1159/000109730
关键词
association analysis; false discovery rate; HapMap
资金
- NCRR NIH HHS [P20 RR020751, P20 RR020751-01-02] Funding Source: Medline
- NIDDK NIH HHS [R01 DK066368-03, R01 DK066368] Funding Source: Medline
- NATIONAL CENTER FOR RESEARCH RESOURCES [P20RR020751] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R01DK066368] Funding Source: NIH RePORTER
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 (c) 2007 S. Karger AG, Basel.
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