Published in

BioMed Central, Human Genomics, 2(3), p. 191

DOI: 10.1186/1479-7364-3-2-191

Links

Tools

Export citation

Search in Google Scholar

Association tests and software for copy number variant data

Journal article published in 2009 by Vincent Plagnol ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Abstract Recent studies have suggested that copy number variation (CNV) significantly contributes to genetic predisposition to several common disorders. These findings, combined with the imperfect tagging of CNVs by single nucleotide polymorphisms (SNPs), have motivated the development of association studies directly targeting CNVs. Several assays, including comparative genomic hybridisation arrays, SNP genotyping arrays, or DNA quantification through real-time polymerase chain reaction analysis, allow direct assessment of CNV status in cohorts sufficiently large to provide adequate statistical power for association studies. When analysing data provided by these assays, association tests for CNV data are not fundamentally different from SNP-based association tests. The main difference arises when the quality of the CNV assay is not sufficient to convert unequivocally the raw measurement into discrete calls -- a common issue, given the technological limitations of current CNV assays. When this is the case, association tests are more appropriately based on the raw continuous measurement provided by the CNV assay, instead of potentially inaccurate discrete calls, thus motivating the development of new statistical methods. Here, the programs available for CNV association testing for case control or family data are reviewed, using either discrete calls or raw continuous data.