Online Program Home
My Program

Abstract Details

Activity Number: 55
Type: Invited
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #318494
Title: Detecting Signal Regions in Whole-Genome Association Studies
Author(s): Xihong Lin* and Zilin Li
Companies: Harvard T.H. Chan School of Public Health and Harvard T.H. Chan School of Public Health
Keywords: Correlated test statistics; ; Scan Statistics
Abstract:

We consider in this paper detecting signal regions associated with human diseases in whole genome association studies. While common gene- or region-based procedures only test for SNPs in pre-specified regions, we propose a chi-squared based scan and segmentation algorithm to detect the existence and location of signal segments. The asymptotic property of the proposed scan statistic allows us to derive an asymptotic threshold to control the familywise error rate. We also show that, under regularity conditions, the proposed procedure consistently selects the true signal regions. Our simulation studies indicate that the proposed procedure has a better finite sample performance over several existing methods, especially in presence of weak or non-signal variants and strong correlation within signal regions. We apply the proposed procedure to analyze a lung cancer genetic dataset to identify the regions of SNPs which are associated with lung cancer risk.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association