English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6469/11641
Visitors : 18545383      Online Users : 339
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/14857

Title: Using the Longest Significance Run to Estimate Region-specific P-values in Genetic Association Mapping Studies
Authors: Lian, Ie-Bin;Lin, Yi-Hsien;Lin, Ying-Chao;Yang, Hsin-Chou;Chang, Chee-Jang;Fann, Cathy S. J.
Contributors: 數學系
Date: 2008
Issue Date: 2012-12-10T02:29:23Z
Publisher: BioMed Central Ltd.
Abstract: BACKGROUND: Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (LSR) method to estimate a region-specific p-value to provide an index for the most likely candidate region.

RESULTS: An advantage of the LSR method relative to procedures based on genotypic data is that only p-value data are needed and hence can be applied extensively to different study designs. In this study the proposed LSR method was compared with commonly used methods such as Bonferroni's method and FDR controlling method. We found that while all methods provide good control over false positive rate, LSR has much better power and false discovery rate. In the authentic analysis on psoriasis and asthma disease data, the LSR method successfully identified important candidate regions and replicated the results of previous association studies.

CONCLUSION: The proposed LSR method provides an efficient exploratory tool for the analysis of sequences of dense genetic markers. Our results show that the LSR method has better power and lower false discovery rate comparing with the locus-specific multiple tests.
Relation: BMC Bioinformatics, 9: 246
Appears in Collections:[數學系] 期刊論文

Files in This Item:

File SizeFormat
index.html0KbHTML415View/Open


All items in NCUEIR are protected by copyright, with all rights reserved.

 


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback