CNVineta: a data mining tool for large case-control copy number variation datasets

Genome-wide rare copy number variation screening in ulcerative colitis identifies potential susceptibility loci

Background

Ulcerative colitis (UC), a complex polygenic disorder, is one of the main subphenotypes of inflammatory bowel disease. A comprehensive dissection of the genetic etiology of UC needs to assess the contribution of rare genetic variants including copy number variations (CNVs) to disease risk. In this study, we performed a multi-step genome-wide case-control analysis to interrogate the presence of disease-relevant rare copy number variants.

Methods

One thousand one hundred twenty-one German UC patients and 1770 healthy controls were initially screened for rare deletions and duplications employing SNP-array data. Quantitative PCR and high density custom array-CGH were used for validation of identified CNVs and fine mapping. Two main follow-up panels consisted of an independent cohort of 451 cases and 1274 controls, in which CNVs were assayed through quantitative PCR, and a British cohort of 2396 cases versus 4886 controls with CNV genotypes based on array data. Additional sample sets were assessed for targeted and in silico replication.

Results

Twenty-four rare copy number variants (14 deletions and 10 duplications), overrepresented in UC patients were identified in the initial screening panel. Follow-up of these CNV regions in four independent case-control series as well as an additional public in silico control group (totaling 4439 UC patients and 15,961 healthy controls) revealed three copy number variants enriched in UC patients; a 15.8 kb deletion upstream of ABCC4 and CLDN10 at13q32.1 (0.43 % cases, 0.11 % controls), a 119 kb duplication at 7p22.1, overlapping RNF216, ZNF815, OCM and CCZ1 (0.13 % cases, 0.01 % controls) and a 134 kb large duplication upstream of the KCNK9 gene at 8q24.3 (0.22 % carriers among cases, 0.03 % carriers among controls). The trend of association with UC was present after the P-values were corrected for combining data from different subpopulations. Break-point mapping of the deleted region suggested non-allelic homologous recombination as the mechanism underlying its formation.

Conclusion

Our study presents a pragmatic approach for effective rare CNV screening of SNP-array data sets and implicates the potential contribution of rare structural variants in the pathogenesis of UC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12881-016-0289-z) contains supplementary material, which is available to authorized users.

CNVinspector: a web-based tool for the interactive evaluation of copy number variations in single patients and in cohorts

OBJECTIVES

Many genetic disorders are caused by copy number variations (CNVs) in the human genome. However, the large number of benign CNV polymorphisms makes it difficult to delineate causative variants for a certain disease phenotype. Hence, we set out to create software that accumulates and visualises locus-specific knowledge and enables clinicians to study their own CNVs in the context of known polymorphisms and disease variants.

METHODS

CNV data from healthy cohorts (Database of Genomic Variants) and from disease-related databases (DECIPHER) were integrated into a joint resource. Data are presented in an interactive web-based application that allows inspection, evaluation and filtering of CNVs in single individuals or in entire cohorts.

RESULTS

CNVinspector provides simple interfaces to upload CNV data, compare them with own or published control data and visualise the results in graphical interfaces. Beyond choosing control data from different public studies, platforms and methods, dedicated filter options allow the detection of CNVs that are either enriched in patients or depleted in controls. Alternatively, a search can be restricted to those CNVs that appear in individuals of similar clinical phenotype. For each gene of interest within a CNV, we provide a link to NCBI, ENSEMBL and the GeneDistiller search engine to browse for potential disease-associated genes.

CONCLUSIONS

With its user-friendly handling, the integration of control data and the filtering options, CNVinspector will facilitate the daily work of clinical geneticists and accelerate the delineation of new syndromes and gene functions. CNVinspector is freely accessible under http://www.cnvinspector.org.

Chapter 15: disease gene prioritization

Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.

Chromosomal variation in lymphoblastoid cell lines

Tens of thousands of lymphoblastoid cell lines (LCLs) have been established by the research community, providing nearly unlimited source material from samples of interest. LCLs are used to address questions in population genomics, mechanisms of disease, and pharmacogenomics. Thus, it is of fundamental importance to define the extent of chromosomal variation in LCLs. We measured variation in genotype and copy number in multiple LCLs derived from peripheral blood mononuclear cells (PBMCs) of single individuals as well as two comparison groups: (1) three types of differentiated cell lines (DCLs) and (2) triplicate HapMap samples. We then validated and extended our findings using data from a large study consisting of samples from blood or LCLs. We observed high concordances between genotypes and copy number estimates within all sample groups. While the genotypes of LCLs tended to faithfully reflect the genotypes of PBMCs, 13.7% (4 of 29) of immortalized cell lines harbored mosaic regions greater than 20 megabases, which were not present in PBMCs, DCLs, or HapMap replicate samples. We created a list of putative LCL-specific changes (affecting regions such as immunoglobulin loci) that is available as a community resource.