A Genome-Wide Association Study of Lung Cancer and Copy Number Variation

• Activity Number: 516
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Gen Nowak*+ and Xihong Lin
• Companies: Australian National University and Harvard University
• Keywords: GWAS ; Lung Cancer ; CNV ; Elastic Net

Abstract

Genome-wide association studies (GWAS) have been used very successfully to identify single nucleotide polymorphisms (SNP) that are associated with certain disease outcomes. More recently, there has been interest in genome-wide association studies of copy number variation (CNV), as CNVs are known to account for a large proportion of genetic variability in humans. CNVs encompass larger regions of DNA than SNPs and further, exhibit particular characteristics that, in the context of a GWAS, necessitate a different method of analysis than that used for SNPs. Here we propose a method of identifying associations between CNVs and non-small cell lung cancer. Our method involves first applying a CNV calling algorithm to define a common set of regions among the samples. We then use a functional data analytic approach, combined with an elastic net penalty, to model and identify CNVs associated with lung cancer. We apply our method to a GWAS data set consisting of approximately 2,000 patient samples and 567,130 markers.