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

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

SUBMITTER: Lian IeB 

PROVIDER: S-EPMC2430975 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature