One of our main focuses in 2017 was VS-CNV which allows clinicians to directly call CNVs in target regions quicker, easier and more affordably than CMA or MLPA testing. Our clients at Robarts Research Institute shared their recent publication with me which confirms that our time and dedication to our CNV capabilities was well worth it. I am delighted to share their discoveries with our community.

Michael Iacocca and colleagues analyzed 388 samples of patients with familial hypercholesterolemia, a disease caused predominantly by autosomal codominant mutations in the LDL receptor gene (LDRL). The standard method is to use next-gen sequencing to detect SNVs followed by an MLPA test to identify CNVs. However, their study was interested in seeing if the MLPA portion of the test can be entirely replaced by our NGS-based CNV detection method. It turned out that thirty-eight (9.8%) of the 388 FH patients were positive for CNVs in LDRL according to Golden Helix CNV method. This was 100% in concordance with those detected by MLPA.

The authors concluded the following, “Transitioning to CNV detection from targeted NGS data has many benefits. Our cost for MLPA analysis in LDRL – including reagents, controls, duplicate analysis and labor – was approximately $80 per patient sample, which totaled approximately $31,000 USD for this cohort of 388 FH individuals. These costs would be essentially eliminated when applying a bioinformatics method to NGS data as such data are already generated for small-scale variant analysis that precedes the CNV assessment.”

The calculated savings are based on applying MLPA to only one gene. Most gene panel tests look at a much high number of genes. I encourage you to read the entire study which can be found here.

This finding is in line with similar unpublished findings of other customers and it is supported by our own internal benchmarks. If you have any questions or are interested in learning more about our CNV capabilities, please do not hesitate to reach out. I hope you all have a great start to 2018!