VS-CNV; Golden Helix’s solution to replace traditional methods

         December 17, 2019

Copy Number Variation (CNV) is a type of structural variation in which sections of the genome are duplicated or deleted. Although CNV events are rare in the human population, constituting approximately 10% of the human genome, they are also associated with being causal mutations for disease phenotypes. Because of this, it is important for clinical and research settings to identify CNV events in their samples or datasets. Traditionally, CNVs are detected using Multiplex Ligation-Dependent Probe Amplification (MLPA) and Chromosomal Microarray (CMA). MLPA is useful for detecting small events from assays such as targeted gene panels, whereas CMA is used to detect larger chromosomal aberrations from whole-genome sequencing data. The caveat to these methods, however, is multifaceted. In summary, these methods require outsourcing and are limited to the events that can be detected and they can be very expensive. VS-CNV, our CNV algorithm integrated into VarSeq, is Golden Helix’s solution to this.

Figure 1: The traditional methods, MLPA and CMA, in comparison to VS-CNV regarding the detectable events and supported data types. VS-CNV does it all.

VS-CNV counters the main caveats of traditional methods because it provides a true simplification of a clinical workflow using one testing paradigm which can save time and money as it is performed all on-site. To elaborate, VS-CNV is an innovative feature that has received multiple grants from the NIH as it utilizes the existing coverage data stored in your BAM files. What this means is that if you want to detect CNV events, you can use your existing NGS data (VCF and BAM) without having to outsource to other companies. This tends to be a big money saver. Furthermore, the VS-CNV caller is extremely accurate and well-validated in the clinical setting with over 225 users and over 15 publications. Below I have highlighted some of the main publications we frequently reference.

The first relevant study was carried out by the Robarts Research Institute studying familial hypercholesterolemia (FH) (1). FH is an inherited disorder characterized by elevated levels of LDL cholesterol, which can lead to cardiovascular diseases including heart attacks. This disease impacts ~34 million individuals globally and of FH cases, ~10% are attributed to CNVs in the LDLR receptor. Identifying these CNVs is valuable because it can lead to genotype-directed treatment strategies and can support insurance coverage of certain medications. In this study of FH, they compared the VS-CNV caller to MLPA and among 388 patient samples, identified 100% concordance in CNV detection between the two callers (1). Another nice touch was the conclusion which referred to the possibility of using VS-CNV to significantly reduce associated costs, resources and analysis time. I recommend reading this to any user interested in the validity of the CNV caller in comparison to the traditional methods.

Another publication that is an interesting read is a letter to the editor of Clinical Genetics focusing on a CNV event correlated with causing maturity-onset diabetes of the young (MODY)(2). MODY is known to be under-recognized with standard genetic testing as the tests do not search for CNV events. This article highlights the use of VS-CNV to identify causal mutations in a patient who initially tested negative for MODY by DNA sequencing. Thirteen years later after the original testing, VS-CNV was employed and identified a whole-gene deletion of HNF4A (MODY1), which leads to a moderately severe progressive form of diabetes. With this identification, a low dose oral treatment can be supplemented until insulin is required. Together, this publication shows that VS-CNV can be used in a research setting and that it can improve the diagnostic yield of next-generation sequencing data.

The last noteworthy publication is the all-encompassing eBook written by Andreas Scherer, Ph.D. titled, “Secondary Analysis – Calling SNVs and CNVs in Next-Gen Sequencing Data”.  This eBook is an informative resource into how VS-CNV works to extend the abilities of your next-generation sequencing data. It always tends to be the first eBook to be snatched up at the multiple conferences that we attend and for good reason. Simply put, in the words of the author, “Golden Helix spearheaded the development of commercial-grade algorithms and methods that allow the analysis of CNVs in gene panels, clinical exomes, and whole genomes”. VS-CNV is a top-shelf application and through reading this eBook you will find out why.

Although these above choices are my favorites, here is a list of some of the other publications referencing the VS-CNV caller:

  1. Rosettia Ho et al. The Identification of Novel CNVs in Patients with Inherited Lipodystrophy. Atherosclerosis; 32(10): 2018
  2. Jacqueline Dron et al. Large-Scale Deletions of the ABCA1 gene in Patients with Hypoalphalipoproteinemia. Journal of Lipid Research; 60 (12): 2018
  3. Michael Lacocca et al. Whole-Genome Duplication of PCSK9 as a Novel Genetic Mechanism for Severe Familial Hypercholesterolemia. Canadian Journal of Cardiology; 34(10): 2018
  4. Pablo Corral et al. Unusual Genetic Variants Associated with Hypercholesterolemia in Argentina. Atherosclerosis; 277(1): 2018
  5. Andrew Geller et al. Genetic and Secondary Causes of Severe HDL Deficiency and Cardiovascular Disease. Journal of Lipid Research; 2018
  6. Jacqueline Dron et al. Severe Hypertriglyceridemia is Primarily Polygenic. Journal of Clinical Lipidology; 13(1): 2019
  7. Francisco Contreras et al. Loss of Function BMP4 Mutation Supports the Implication of the BMP/TGF-B Pathway in the Etiology of Combined Pituitary Hormone Deficiency. ImmunoMedicine; 2019
  8. Cathrine Jespersgaard et al. Molecular Genetic Analysis Using Targeted NGS Analysis of 677 Individuals with Retinal Dystrophy. Scientific Reports; 1219: 2019

In summary, VS-CNV is an innovative, market-leading application that is well received in the clinical and research setting. This feature will continue to lead the way in CNV detection with the support and feedback of our customers as well as the NIH grant funding, which we are extremely grateful for. I hope this blog provided you with some additional resources to help better understand the CNV caller and how other customers are using it to greatly reduce analysis time, cost and outsourcing for their existing pipelines.

If you would like to see VS-CNV in action, I would recommend watching our start to finish webcast on CNV calling from targeted gene panel data below.

Alternatively, you can contact info@goldenhelix.com to get a one-on-one demonstration and we would be happy to answer any of your questions.

  1. Michael Iacocca et al. Use of Next Generation Sequencing to Detect LDLR Gene Copy Number Variation in Familial Hypercholesterolemia. Journal of Lipid Research. Volume 58, 2017.
  2. Amanda Berberich et al. Bioinformatic Detection of Copy Number Variation in HNF4A Causing Maturity Onset Diabetes of the Young. Letter to the Editor; Clinical Genetics, 2019

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