Year: 2011
Type: article
Abstract: BACKGROUND/AIM: Copy number variations (CNVs) are a major source of alterations among individuals and are a potential risk factor in many diseases. Numerous diseases have been linked to deletions and duplications of these chromosomal segments. Data from genome-wide association studies and other microarrays may be used to identify CNVs by several different computer programs, but the reliability of the results has been questioned. METHODS: To help researchers reduce the number of false-positive CNVs that need to be followed up with laboratory testing, we evaluated the relative performance of CNVPartition, PennCNV and QuantiSNP, and developed a statistical method for estimating sensitivity and positive predictive values of CNV calls and tested it on 96 duplicate samples in our dataset. RESULTS: We found that the positive predictive rate increases with the number of probes in the CNV and the size of the CNV, with the highest positive predicted rates in CNVs of at least 500 kb and at least 100 probes. Our analysis also indicates that identifying CNVs reported by multiple programs can greatly improve the reproducibility rate and the positive predicted rate. CONCLUSION: Our methods can be used by investigators to identify CNVs in genome-wide data with greater reliability.
Source: Human Heredity
DOI: 10.1159/000324683
Authors: Peng Lin, Sarah M. Hartz, Jen-Chyong Wang, Robert F. Krueger, Tatiana M. Foroud, Howard J. Edenberg, John I. Nurnberger, Jr, Andrew I. Brooks, Jay A. Tischfield, Laura Almasy, Bradley T. Webb, Victor M. Hesselbrock, Bernice Porjesz, Alison M. Goate, Laura J. Bierut, John P. Rice, COGA Collaborators, COGEND Collaborators, GENEVA Investigators
Institutions: Washington University in St. Louis, University of Minnesota, Indiana University Bloomington, Indiana University Indianapolis, Indiana University School of Medicine, Indiana University – Purdue University Indianapolis, Rutgers, The State University of New Jersey, Texas Biomedical Research Institute, Virginia Commonwealth University, University of Connecticut, UConn Health, SUNY Downstate Health Sciences University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Awards: G1083756078, Study of Addiction: Genetics and Environment, Washington University Institute of Clinical and Translational Sciences, G363841575, G3799582765, G407586344, Genome Wide Association Coordinating Center, JH/CIDR Genotyping for Genome-Wide Association Studies, FAMILY STUDY OF COCAINE DEPENDENCE, G8267674335, G858805679
OpenAlex ID: W2093595758