A Statistical Method for Identifying Trait-Associated Copy Number Variants

• Activity Number: 34
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Jessie Jeng and Qian Wu*+ and Hongzhe Li+
• Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
• Keywords: Copy number variants ; CNVtest ; Two-step ; Association testing

Abstract

Copy number variants (CNVs) are a form of structural variation, with fewer or more than two copies of segments of the DNA, which corresponds to deletion or duplication, respectively. CNVs have been shown to be associated with many complex phenotypes, ranging from diseases to gene expressions. Identifying the CNVs that are associated with complex traits is an important problem in human genetic research. However, methods for testing the CNV association are limited. Most available methods employ a two-step approach, where the CNVs are identified first and are then tested for association, which is not clear how to control for the genome-wide error rates. We develop a method CNVtest for identifying the copy number variants that are associated with phenotypes without first calling all these CNVs. In order to illustrate the CNVtest method, simulations and an application to a case-control neuroblastoma study are conducted to identify the CNV associations. Comparing to the two-step approach, the simulation results show that the CNVtest simultaneously studying data from multiple samples could gain more power in identifying the common CNVs.