1st Place Abstract Competition Winner

Our abstract competition is one of my favorite events because of the learning opportunity it provides our team. Each applicant’s submission tells us a unique story about how our software is helping users conduct their research. This year’s competition didn’t disappoint bringing a new round of fascinating studies to our attention. However, with all these great abstracts came the difficult task of narrowing down three prize winners! We appreciate each one of you who submitted your research.

Today, I would like to announce our first-place winner, Michael Iacocca, research trainee in Dr. Robert Hegele’s laboratory at Robarts Research Institute. His abstract coincides with our one of our main goals to create powerful CNV calling capabilities for users to rely on to detect copy number variants from their NGS data. Michael will be presenting his research in our upcoming webcast, Using NGS to detect CNVs in familial hypercholesterolemia, next Wednesday, February 14th at 12:00 PM Eastern.

Abstract:

Familial hypercholesterolemia (FH) is a heritable condition of severely elevated LDL cholesterol, characterized by premature atherosclerotic cardiovascular disease. FH affects an estimated 1 in 250 individuals worldwide, and is considered to be the most frequent monogenic disorder encountered in clinical practice. Although FH has multiple genetic etiologies, the large majority (>90%) of defined cases result from autosomal codominant mutations in the LDL receptor gene (LDLR).

In providing a molecular diagnosis for FH, the current procedure often includes targeted next-generation sequencing (NGS) panels for the detection of small-scale DNA variants, followed by multiplex ligation-dependent probe amplification (MLPA) in LDLR for the detection of whole-exon copy number variants (CNVs). The latter is essential as ~10% of FH cases are attributed to CNVs in LDLR; accounting for them decreases false-negative findings. Here, we have determined the potential of replacing MLPA with bioinformatic analysis (VarSeq) applied to NGS data, which uses depth of coverage analysis as its principal method to identify whole-exon CNV events. In analysis of 388 FH patient samples, there was 100% concordance in LDLR CNV detection between these two methods: 38 reported CNVs identified by MLPA were also successfully detected by NGS + VarSeq, while 350 samples negative for CNVs by MLPA were also negative by NGS + VarSeq. This result suggests that MLPA is dispensable, significantly reducing costs, resources, and analysis time associated with the routine diagnostic screening for FH, while promoting more widespread assessment of this important class of mutations across diagnostic laboratories.

You can view Michael’s entire paper by visiting: http://goldenhelix.com/media/pdfs/Robarts-Research-Institute-CNV_Capabilities. I will be announcing the rest of the winners at a later date – stay tuned!

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