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Activity Number: 658 - Recent Statistical Advances in Genomic and Genetic Data Analysis
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #329976
Title: Detection of Signal Regions in Whole Genome Genotyping and Sequencing Association Studies Using Scan Statistics
Author(s): ZILIN LI* and Xihong Lin
Companies: Harvard T.H. School of Public Health and Harvard University
Keywords: Multiple hypotheses; Scan statistics; Signal detection; Whole genome sequencing study

We consider in this paper detection of signal regions associated with disease outcomes in whole genome association studies. The existing gene- or region-based methods test for the association of an outcome and the genetic variants in a pre-specified region, e.g., a gene. In view of massive inter-genetic regions in whole genome association studies, we propose a p-value scan statistic based method to detect the location and size 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. We performed simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results showed that the proposed procedure outperforms the existing methods, especially when signal regions have causal variants whose effects are in different directions, or are contaminated with neutral variants, or the variants in signal regions are correlated. We applied the proposed method to analyze a whole genome sequencing data to identify the genetic regions that are associated with heart- and blood-related traits.

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

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