Using the longest significance run to estimate region-specific p-values in genetic association mapping studies

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 commonl...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Ie-Bin Lian, Yi-Hsien Lin, Ying-Chao Lin, Hsin-Chou Yang, Chee-Jang Chang, Cathy S. J. Fann
• Keywords: BMCBI 2008, False Discovery Rate, Locus-specific Multiple Tests, LSR Method