Multiple Hypothesis Testing in Large-scale Association Studies

• Main Authors: Shu-Hui Wen, 溫淑惠
• Other Authors: Chu-Hsing Hsiao
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/72944892001447586215

博士 === 國立臺灣大學 === 流行病學研究所 === 92 === Abstract Multiple hypothesis testing is a commonly occurred problem in genome-wide association studies. As the number of markers increases, the overall false positive rate inflates. The traditional Bonferroni correction is so stringent that the overall power is usually small. This may not meet the primary interest of finding the markers of even mild effect. In this thesis, we propose a two-stage selection method to address this problem. The main idea is to maintain a substantial power in the first stage and control the incurred false positives in the second stage. The implementation of the proposed procedure will be provided. Its statistical properties, including the rate of diminishing non-associated SNPs, overall false positive rate, and overall true positive rate, will be derived. In addition, we will recommend the determination of the sample size under each stage. We also illustrate the proposed method with a simulation study, and compare it with Bonferroni method. The two-stage procedure performs better than Bonferroni method even when the difference in marker allele frequency between case and control group is moderate.