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 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.

1. Use of next-generation sequencing to detect copy
number variants in the molecular diagnosis of
familial hypercholesterolemia
Michael Iacocca
Research Trainee
Blackburn Cardiovascular Genetics Laboratory
Robarts Research Institute, Western University, Canada
Supervisor: Dr. Robert Hegele, MD

2. Overview
• What is familial hypercholesterolemia (FH) and why is it important?
• What are the causes of FH and how is it being currently diagnosed at the
molecular level?
• Method: How can the molecular diagnosis be potentially improved?
• What are the implications of this method?
• How can this method be further applied?

3. Familial Hypercholesterolemia (FH)
• Genetically determined extreme LDL cholesterol
(LDL-C plasma concentration >95th percentile for age/sex)
• Autosomal dominant inheritance
• Heterozygous FH: Prevalence of ~1 in 250 (Akioyamen LE et al. BMJ Open. 2017)
-most common monogenic disorder worldwide
• < 10 % diagnosed
globally
• Early onset atherosclerosis
causing CVD
- ↑ risk of MI, stroke
• Effectively lowered LDL-C,
~ normal life expectancy

4. Familial Hypercholesterolemia (FH)
• DNA testing a central part of
diagnosis worldwide
(ClinVar at NCBI, accessed Dec 2017)
Current method:
1) Targeted NGS panel
- small-scale variants
- LDLR, APOB, PCSK9
2) MLPA
- large-scale CNVs (deletion/duplications of one or more whole exons)
- LDLR
• LDLR: loss-of-function
variants
• APOB: specific protein-
altering variants
• PCSK9: gain-of-function
variants

5. Iacocca MA and Hegele RA. Curr Opin Lipidol. 2018
Unique LDLR CNVs identified in FH patients worldwide

6. Objective
• To determine the potential of applying bioinformatics
to existing NGS data to accurately detect CNVs in LDLR,
thus removing the need for secondary MLPA analysis

7. Methods
Study subjects
• 388 individuals from Canada with a clinical diagnosis of at least
‘probable’ FH per the DLCN criteria
Next-generation sequencing (NGS)
• LipidSeq
• 73 genes, including LDLR, APOB, PCSK9 and LDLRAP1, APOE, STAP1,
ABCG5, ABCG8, LIPA
CNV analysis by MLPA
• Multiplex PCR method
• Assay of promoter and all 18 exons in LDLR
CNV analysis by NGS data
• Bioinformatics applied to existing NGS data
• VarSeq CNV Caller: Depth of coverage analysis

8. Methods: NGS Panel
LipidSeq Panel NGS
• 73 lipid metabolism-related genes, including all FH-associated genes LDLR,
APOB, PCSK9 and LDLRAP1, APOE, STAP1, ABCG5, ABCG8, LIPA
- All exons, 150 bp at intron/exon boundaries, ~250 bp of 5’UTR
- 178 SNP loci
• Library prep: Nextera Rapid Capture Custom Enrichment kit (Illumina)
• Platform: MiSeq (Illumina) – 2 x 150 bp paired-end chemistry
• Avg. 300-fold coverage per base
Johansen CT et al. J Lipid Research. 2014
Hegele RA et al. Curr Opin Lipidol. 2015

9. Dilliott AA et al. J Vis Exp. 2018
Methods: CLC Genomics Workbench
.FASTQ file
.BED file
NGS MiSeq Output

10. VarSeq CNV Caller Requirements
• .BAM file
• .VCF file
1) Patient sample
2) Matched reference controls (N= 30 to 50)
• .BAM file
• .VCF file
3) .BED file

11. Results
MLPA NGS
Type Region Detection Avg. Ratio Avg. Z-score
Het. Deletion Promoter-Exon 1 (n=22) Yes 0.51 -6.2
Het. Deletion Promoter-Exon 2 (n=2) Yes 0.57 -5.6
Het. Deletion Promoter-Exon 6 Yes 0.54 -7.4
Het. Deletion Exons 2-3 Yes 0.56 -6.7
Het. Deletion Exons 2-6 Yes 0.54 -9.7
Duplication Exons 2-6 Yes 1.38 11.8
Het. Deletion Exons 3-6 Yes 0.53 -9.7
Het. Deletion Exons 5-6 Yes 0.54 -14.7
Duplication Exon 7 Yes 1.47 7.3
Duplication Exons 11-12 Yes 1.86 12.7
Het. Deletion Exons 11-12 Yes 0.54 -7.8
Het. Deletion Exons 13-14 Yes 0.51 -15.9
Het. Deletion Exons 13-15 Yes 0.65 -8.7
Het. Deletion Exons 16-18 Yes 0.53 -9.9
Het. Deletion Exons 17-18 (n=2) Yes 0.53 -9.3
CNVs in LDLR detected by MLPA
• 38 of 388 (9.8%) FH patients
were CNV positive
Iacocca MA, Wang J, et al. J Lipid Res. 2017

12. Ex) VarSeq NGS data output: LDLR Exons 2- 6 heterozygous deletion

13. Ex) VarSeq NGS data output: LDLR Exons 11-12 heterozygous deletion

14. Ex) VarSeq NGS data output: LDLR Exon 7 duplication

15. Ex) VarSeq NGS data output: LDLR Exons 2-6 duplication

16. Results
Sensitivity:
100%
Specificity:
100%
MLPA Result
NGS + VarSeq
Result
Concordance
True Positives
38
False Positives
0
False Negatives
0
True Negatives
350
Positive
Negative
DiploidCNV
Iacocca MA, Wang J, et al. J Lipid Res. 2017

17. Implications
• Use of a single platform (NGS) for detection of both small and large-scale
DNA variants
• Reduced costs, resources, analysis time associated with the routine
molecular diagnosis of FH
- MLPA: $80 USD per sample - $31,000 USD for this cohort of 388 samples
• Expanding CNV screening to all FH-associated genes on a given NGS panel
at no extra cost
LipidSeq: APOB, PCSK9 and LDLRAP1, APOE, STAP1, ABCG5/8, LIPA
further accounting for all genetic abnormalities capable of defining
FH cases

18. Future Directions
• Novel CNV screening in additional FH-associated genes

19. Conclusion
• FH is the most prevalent monogenic disorder worldwide affecting ~1 in 250
individuals
• DNA testing increasingly becoming a central part of diagnosis; current procedure
often includes targeted NGS followed by MLPA
• In analysis of 388 FH patient samples, there was 100% concordance in LDLR CNV
detection between MLPA and NGS method
• Suggests MLPA is dispensable, significantly reducing associated costs, resources,
analysis time
• All genes on a given NGS panel assessed for CNVs concurrently; allows for novel
CNV screening in additional FH genes at no extra cost
- promoting more widespread assessment of CNVs across diagnostic
laboratories
- potential for discovery of novel genetic mechanisms for FH
- increasing molecular diagnostic yield

20. Acknowledgements
Dr. Robert Hegele
Dr. Jian Wang
Dr. Henian Cao
Jacqueline Dron
Adam McIntyre
John Robinson
David Carter
Jenn Biltcliffe
Rosettia Ho
Allison Dilliott
Ericka Simon
Brooke Kennedy
Matthew Ban
Blackburn Cardiovascular
Research Laboratory