Errors in the interpretation of copy number variations due to the use of public databases as a reference

Application of the cghRA framework to the genomic characterization of Diffuse Large B-Cell Lymphoma

Motivation

Although sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyze such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task.

Results

The cghRA framework combines a user-friendly graphical interface and a powerful object-oriented command-line interface to handle a full aCGH analysis, as is illustrated in an original series of 107 Diffuse Large B-Cell Lymphomas. New algorithms for copy-number calling, polymorphism detection and minimal common region prioritization were also developed and validated. While their performances will only be demonstrated with aCGH, these algorithms could actually prove useful to any copy-number analysis, whatever the technique used.

Availability and implementation

R package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA.

Contact

mareschal@ovsa.fr or fabrice.jardin@chb.unicancer.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genetic Imbalance in Patients with Cervical Artery Dissection

Background: Genetic and environmental risk factors are assumed to contribute to the susceptibility to cervical artery dissection (CeAD). To explore the role of genetic imbalance in the etiology of CeAD, copy number variants (CNVs) were identified in high-density microarrays samples from the multicenter CADISP (Cervical Artery Dissection and Ischemic Stroke Patients) study and from control subjects from the CADISP study and the German PopGen biobank. Microarray data from 833 CeAD patients and 2040 control subjects (565 subjects with ischemic stroke due to causes different from CeAD and 1475 disease-free individuals) were analyzed. Rare genic CNVs were equally frequent in CeAD-patients (16.4%; n=137) and in control subjects (17.0%; n=346) but differed with respect to their genetic content. Compared to control subjects, CNVs from CeAD patients were enriched for genes associated with muscle organ development and cell differentiation, which suggests a possible association with arterial development. CNVs affecting cardiovascular system development were more common in CeAD patients than in control subjects (p=0.003; odds ratio (OR) =2.5; 95% confidence interval (95% CI) =1.4-4.5) and more common in patients with a familial history of CeAD than in those with sporadic CeAD (p=0.036; OR=11.2; 95% CI=1.2-107).

Conclusion: The findings suggest that rare genetic imbalance affecting cardiovascular system development may contribute to the risk of CeAD. Validation of these findings in independent study populations is warranted.