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Activity Number: 254
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #316107
Title: Rare-Variant Association Analysis Using Kullback-Leibler Distance Methods
Author(s): Asuman Turkmen* and Zhifei Yan and Yue-Qing Hu and Shili Lin
Companies: The Ohio State University and The Ohio State University and Fudan University and The Ohio State University
Keywords: Single nucleotide based testsmulti-locus based tests ; multi-locus based tests ; next generation sequencing data ; noise and direction resistance ; Dallas Heart Study
Abstract:

The recent advances in sequencing technologies along with "missing" genetic variance not recovered in genome-wide association studies have led a tremendous interest in detecting rare genetic variants that contribute to human diseases. This study proposes Kullback-Leibler distance based statistical procedures (KLTs) for testing association between a binary trait and a set of rare and common variants. Unlike existing burden tests aggregating variant signals, the KLTs explicitly compare the distributions of genotypes and, therefore, they are still powerful in the presence of both deleterious and protective variants in a genomic region. Furthermore, KLTs are robust to the presence of non-causal variants due to their built-in noise fighting mechanism and they work well regardless of the underlying linkage disequilibrium (LD) structure. Extensive simulation studies and an application to the Dallas Heart Study data are utilized to compare our newly proposed tests with the sum of squared score test (SSU) and optimal sequence kernel association test (SKAT-O). The numerical results show that the proposed tests are attractive alternatives for association studies involving rare variants.


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

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