Abstract 174: The Use of Next-Generation Sequencing to Detect Copy Number Variation in the Molecular Diagnosis of Familial Hypercholesterolemia

Abstract

Background: Familial hypercholesterolemia (FH) is a common monogenic disorder of lipoprotein metabolism, characterized by elevated LDL cholesterol and increased risk for premature cardiovascular disease. Efforts to provide a molecular diagnosis of FH are trending towards the use of targeted next-generation sequencing (NGS) panels to interrogate canonical FH-associated genes— LDLR , APOB , and PCSK9 —for clinically relevant small-scale variants. However, large-scale copy number variants (CNVs) or deldup variants constitute 10-15% of LDLR variants in many cohorts. Because these are not routinely accessible by NGS, a second assay, namely multiplex-ligation dependent probe amplification (MLPA), is currently required to screen for them. To increase efficiency and decrease costs associated with identifying the genetic causes for FH, use of a single platform to detect both small and large-scale variants would be extremely beneficial in a clinical setting. Objective: Here we determine the accuracy of NGS bioinformatic tools in identifying CNVs. Methods: In 313 clinically ascertained, unrelated patients with at least possible FH per the Dutch Lipid Clinic Network (DLCN) criteria, we sequenced canonical FH-associated genes using our targeted NGS panel (LipidSeq TM ). These patients were also assayed using MLPA. The CNV analysis tool (VarSeq® Golden Helix, Inc.) was run using the NGS data generated for each patient. Concordance between the NGS tool and standard MLPA was subsequently determined. Results: We evaluated a subset of 99 FH individuals: 19 were positive while 80 were negative for a LDLR mutation using MLPA. Our CNV analysis using bioinformatically processed NGS data compared to MLPA yielded 2 out of 19 false negatives and 0 out of 80 false positives. This translates to a sensitivity of 89% and a specificity of 100% for our NGS approach, considering MLPA as the current ’gold standard’. Conclusions: Analysis of deeply resequenced targeted NGS data for the identification of CNVs in FH shows excellent potential to become a standard diagnostic test for those with suspected FH, potentially eliminating the need for secondary MLPA analysis. Future applications of this NGS tool may also allow for novel CNV screening in additional FH-associated genes.