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Activity Number: 74 - Invited E-Poster Session I
Type: Invited
Date/Time: Sunday, August 7, 2022 : 8:30 PM to 9:25 PM
Sponsor: IMS
Abstract #322806
Title: Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics with Applications to Whole Genome Association Studies
Author(s): Zilin Li* and Yaowu Liu and Xihong Lin
Companies: Harvard T.H. Chan School of Public Health and School of Statistics, Southwestern University of Finance and Economics and Harvard University
Keywords: Asymptotics; Family-wise error rate; Multiple hypotheses; Scan statistics; Signal detection; Whole genome sequencing association studies
Abstract:

We consider in this paper detection of signal regions associated with disease outcomes in whole-genome association studies. Gene- or region-based methods have become increasingly popular in whole-genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a pre-specified region, e.g., a gene. In view of massive intergenic regions in whole-genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic-based method to detect the existence and the locations of signal regions by scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both causal and neutral variants, and the effects of signal variants to be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an empirical threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions.


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