Using the longest significance run to estimate region-specific p-values in genetic association mapping studies |
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| Authors: | Ie-Bin Lian Yi-Hsien Lin Ying-Chao Lin Hsin-Chou Yang Chee-Jang Chang Cathy SJ Fann |
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| Affiliation: | (1) Department of Mathematics, National Changhua University of Education, Changhua, 500, Taiwan;(2) Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan;(3) Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 115, Taiwan;(4) Institute of Statistical Science, Academia Sinica, Taipei, 115, Taiwan;(5) Graduate Institute of Clinical Medical Sciences, Chang-Gung University, Taoyuan, 333, Taiwan |
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| 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. |
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