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Abstract Details

Activity Number: 468
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #304641
Title: Simultaneous Identification of Rare and Common Segment Variants
Author(s): X. Jessie Jeng*+ and Tony Cai and Hongzhe Li
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Address: 3131 Walnut Treet, Philadelphia, PA, 19104-3423, United States
Keywords: Recurrent genetic signals ; Optimal adaptivity ; CNV analysis

The identification of recurrent signals based on a large set of samples is one of the central topics in population-scale genomic data analysis. A bottleneck of recurrent signal identification is the lack of an adaptive information pooling procedure which can automatically adjust to the unknown signal carrier's proportions and efficiently identify both rare and common recurrent signals. We specifically consider the identification of recurrent DNA copy number variants (CNVs) based on a large set of samples from Neuroblastoma patients. It is likely that both rare and common CNVs cooperate to increase the risk of the disease. We developed the Proportion Adaptive Segment Selection (PASS) procedure, which can optimally and simultaneously identify both rare and common CNVs. The proposed method is statistically rigorous and computationally efficient. Theory, simulation, and real applications will be presented to demonstrate the performance of the method.

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