|
Activity Number:
|
136
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 4, 2008 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Statistics in Epidemiology
|
| Abstract - #300425 |
|
Title:
|
A Multiple Testing Correction Method for Genetic Association Studies Using Correlated SNPs
|
|
Author(s):
|
Xiaoyi Gao*+ and Joshua Starmer and Eden R. Martin
|
|
Companies:
|
Miami Institute for Human Genomics and The University of North Carolina at Chapel Hill and Miami Institute for Human Genomics
|
|
Address:
|
1120 NW 14th Street, Miami, FL, 33136,
|
|
Keywords:
|
single nucleotide polymorphism ; composite linkage disequilibrium ; multiple testing correction ; eigenvalues ; principal component analysis
|
|
Abstract:
|
Correcting for multiple testing is a challenging and critical issue in genetic association studies using large numbers of single nucleotide polymorphisms (SNPs), many of which exhibit linkage disequilibrium (LD). The Bonferroni method is well known to be conservative in the presence of LD. Permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using principal components of SNP markers. We show that it is simple, fast and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it can be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing of SNPs in LD with each other.
|
