Copy number variation accuracy in genome-wide association studies

Peng Lin, Sarah M. Hartz, Jen Chyong Wang, Robert F. Krueger, Tatiana M. Foroud, Howard J. Edenberg, John I. Nurnberger, 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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)141-147
Number of pages7
JournalHuman heredity
Volume71
Issue number3
DOIs
StatePublished - Jul 2011

Keywords

  • Accuracy
  • Copy number variations
  • False positives
  • Genome-wide association studies

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