Human Molecular Genetics Advance Access originally published online on July 31, 2007
Human Molecular Genetics 2007 16(20):2494-2505; doi:10.1093/hmg/ddm205
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Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project
1 Department of Hematology/Oncology, 2 Department of Cell Therapy and Transplantation Medicine, Graduate School of Medicine and 3 Department of Information and Communication Engineering, Graduate School of Information Science, University of Tokyo, Tokyo 113-8655, Japan and 4 Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, Saitama 332-0012, Japan
* To whom correspondence should be addressed to: Department of Cell Therapy and Transplantation Medicine, The 21st Century COE Program, Graduate School of Medicine, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. Tel: +81 358008741; Fax: +81 358046261; Email: sogawa-tky{at}umin.ac.jp
Received April 7, 2007; Accepted July 22, 2007
With recent advances in high-throughput single nucleotide polymorphism (SNP) typing technologies, genome-wide association studies have become a realistic approach to identify the causative genes that are responsible for common diseases of complex genetic traits. In this strategy, a trade-off between the increased genome coverage and a chance of finding SNPs incidentally showing a large statistics becomes serious due to extreme multiple-hypothesis testing. We investigated the extent to which this trade-off limits the genome-wide power with this approach by simulating a large number of case-control panels based on the empirical data from the HapMap Project. In our simulations, statistical costs of multiple hypothesis testing were evaluated by empirically calculating distributions of the maximum value of the 
2 statistics for a series of marker sets having increasing numbers of SNPs, which were used to determine a genome-wide threshold in the following power simulations. With a practical study size, the cost of multiple testing largely offsets the potential benefits from increased genome coverage given modest genetic effects and/or low frequencies of causal alleles. In most realistic scenarios, increasing genome coverage becomes less influential on the power, while sample size is the predominant determinant of the feasibility of genome-wide association tests. Increasing genome coverage without corresponding increase in sample size will only consume resources without little gain in power. For common causal alleles with relatively large effect sizes [genotype relative risk 
1.7], we can expect satisfactory power with currently available large-scale genotyping platforms using realistic sample size (
1000 per arm).