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Original Paper

Free Access

A Statistical Method for Identifying Trait-Associated Copy Number Variants

Jeng J.a · Wu Q.b · Li H.b

Author affiliations

aDepartment of Statistics, North Carolina State University, Raleigh, N.C., and bDepartment of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa., USA

Corresponding Author

Prof. Hongzhe Li, PhD

Department of Biostatistics and Epidemiology

University of Pennsylvania Perelman, School of Medicine

423 Guardian Drive, Philadelphia, PA 19104 (USA)

E-Mail hongzhe@upenn.edu

Related Articles for ""

Hum Hered 2015;79:147-156

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Abstract

Copy number variants (CNVs), ranging in size from about one kilobase to several megabases, are DNA alterations of a genome that result in the cell having less or more than two copies of segments of the DNA. Such CNVs have been shown to be associated with many complex phenotypes, ranging from diseases to gene expressions. Novel methods have been developed for identifying CNVs both at the individual and at the population level. However, methods for testing CNV association are limited. Most available methods employ a two-step approach, where CNVs carried by the samples are identified first and then tested for association. However, the results of such tests depend on the threshold used for CNV identification and also the number of CNVs to be tested. We developed a method, CNVtest, to directly identify the trait-associated CNVs without the need of identifying sample-specific CNVs. We show that CNVtest asymptotically controls the type I error rate and identifies true trait-associated CNVs with a high probability. We demonstrate the methods using simulations and an application to identify the CNVs that are associated with population differentiation.

© 2015 S. Karger AG, Basel


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Published online: July 28, 2015
Issue release date: July 2015

Number of Print Pages: 10
Number of Figures: 3
Number of Tables: 1

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


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