This document discusses Golden Helix's software for clinical variant analysis and summarizing copy number variants using American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP) guidelines. It acknowledges funding support from several National Institutes of Health grants. It also lists upcoming discussion sessions on applying ACMG/AMP guidelines in clinical practice and analyzing copy number variants from next-generation sequencing data.

1. Clinical Validation of Copy Number Variants Using the AMP Guidelines
Dr. Eli Sward – Field Application Scientist
sward@goldenhelix.com
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3. NIH Grant Funding Acknowledgments
• Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National
Institutes of Health under:
• Award Number R43GM128485-01
• Award Number R43GM128485-02
• Award Number 2R44 GM125432-01
• Award Number 2R44 GM125432-02
• Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005
• PI is Dr. Andreas Scherer, CEO Golden Helix.
• The content is solely the responsibility of the authors and does not necessarily represent the official views of the National
Institutes of Health.

4. Filtering and Annotation
ACMG & AMP Guidelines
Clinical Reports
CNV Analysis
Pipeline: Run Workflows
Variant Warehouse
Centralized Annotations
Hosted Reports
Sharing and Integration
CNV Analysis
GWAS | Genomic Prediction
Large-N Population Studies
RNA-Seq
Large-N CNV-Analysis
Who Are We?
Golden Helix is a global bioinformatics company
founded in 1998

5. Cited in 1,000s of Peer-Reviewed Publications

6. Over 400 Customers Globally

7. SIMPLE, SUBSCRIPTION-
BASED BUSINESS MODEL
o Yearly fee
o Unlimited training & support
SOFTWARE IS VETTED
o 20,000+ users at 400+ organizations
o Quality & feedback
DEEPLY ENGRAINED IN
SCIENTIFIC COMMUNITY
o Give back to the community
o Contribute content and support
INNOVATIVE SOFTWARE SOLUTIONS
o Cited in 1,000s of publications
When you choose Golden Helix,
you receive more than just the software

9. Power of NGS CNV Detection
Small:1
50b+
Medium:
1 – 10Kb
Large:
10Kb+
Gene
panel
Whole
exome
Whole
genome
MLPA
✓ ✓
CMA
✓ ✓
VS-CNV
✓ ✓ ✓ ✓ ✓ ✓
Detectable events Supported Data types
▪ One single testing paradigm
▪ True simplification of clinical workflow
▪ Saves time and money – all on site

10. Addressing Issues - CNV Detection via NGS
▪ CNVs detected from coverage data in BAM
▪ Challenges
• Coverage varies between samples
• Coverage fluctuates between targets
• *Systematic biases impact coverage
▪ Solutions
• Data Normalization
• Reference Sample Comparison
• Algorithm works without case/control data
▪ Requirements
• ≥ 30 ref samples
• From same library prep method
• Ideally ≥100X coverage

11. CNV Detection
▪ Metrics
• Ratio: normalized sample coverage divided
by the average normalized reference
sample coverage
• Z-score: standard deviations from reference
sample mean
• Confidence
• P-value is produced for all called events and
can be customized to determine the
probability that the CNV event is true
• Annotations
• Population catalogs
• SuperDups
• Classification

12. 225+ Users and 15+ Publications
• Journals
• Atherosclerosis
• Journal of lipid research
• Journal of Clinical Lipidology
• American Journal of Medical Genetics
• BMC Medical Genomics
• Medicine
• Analysis topics
• Hypercholesterolemia
• Retinal dystrophy
• Pituitary hormone deficiency
• Rare Mendelian disorders
• Lacocca et al.
• Whole exome CNV detection comparison
• 100% concordance MLP&CMA with VSCNV
• Improves resources, cost, analysis time

13. • Automates both ACMG and AMP Guidelines all in one suite
• Single nucleotide variants, insertions and deletions, copy number variants, gene fusions
• Creates consistency in evaluations / reduces workflow fatigue
• Educational purposes
• Word-based reporting capabilities
Germline
Somatic

14. Tier I: Variants of
Strong Clinical
Significance
Therapeutic, prognostic &
diagnostic
Level A Evidence
FDA-approved therapy
Included in professional
guidelines
Level B Evidence
Well-powered studies with
consensus from experts in
the field
Tier II: Variants of
Potential Clinical
Significance
Therapeutic, prognostic &
diagnostic
Level C Evidence
FDA-approved therapy for
different tumor types or
investigational therapies
Multiple small published
studies with some consensus
Level D Evidence
Preclinical trials or a few
case reports without
consensus
Tier III: Variants of
Unknown Clinical
Significance
Not observed at a significant
allele frequency in the
general or specific
subpopulation database, or
pan-cancer or tumor-specific
variant database
No convincing published
evidence of cancer
association
Tier IV: Benign or
Likely Benign Variants
Observed at a significant
allele frequency in the
general or specific
subpopulation databases
No existing published
evidence of cancer
association
(2017) Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer:
VSClinical - AMP Guidelines: Biomarkers
▪ Biomarkers:
- Biological states with indications for
treatments, prognostic, or diagnostic
outcomes
▪ AMP Guidelines: 4 Tiers
- Tier 1: FDA-approved therapy
- Tier 2: FDA-approved for different tumor
type / Preclinical
- Tier 3: No evidence of cancer association
- Tier 4: High allele frequency variants

15. AMP Guidelines – Annotations
Population Database to exclude common variants
Cancer Specific Databases
Sequence Repositories
Clinical, Drug, and Prediction annotations
Splice Site and Functional Prediction Algorithms

16. VSClinical AMP: Golden Helix CancerKB
Haroche J. et al. Dramatic efficacy of vemurafenib in both
multisystemic and refractory Erdheim-Chester disease and
Langerhans cell histiocytosis harboring the BRAF V600E mutation.
Blood 2013 121
▪ Interpretations for Cancer Biomarkers
- Biomarker and Gene interpretations
- Assess drug sensitivity, resistance, prognostic, and diagnostic information
- Build biomarker classification and interpretation
▪ Updated by users and reviewed by expert panel
▪ Jumpstarts interpretation enhances knowledge base and productivity
to final report

17. VSClinical – Clinical Report
• VSClinical conducts the clinical variant analysis based
on ACMG and AMP guidelines
- Automated population of the clinical report-based workflow
outcome
- Standardizing of variant level interpretation based on
customizable assessment catalogs
- For somatic variants, GHI provides predefined clinical
assessments via our CancerKB catalog
• Rendering of clinical reports within seconds
• Supported output formats
- Word
- PDF

18. VSClinical – AMP Guidelines: Project Demonstration
• CNV background
- ~ 50 Reference samples
- Predefined workflow
- Quality/Confidence filtering
- Remove common CNVs
- Targeted phenotype
• Evaluate CNV in VSClinical – AMP hub
- Patient info
- Mutation selection
- Biomarker interpretation + treatment options
- Clinical report

19. Project Demonstration

20. NIH Grant Funding Acknowledgments
• Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National
Institutes of Health under:
• Award Number R43GM128485-01
• Award Number R43GM128485-02
• Award Number 2R44 GM125432-01
• Award Number 2R44 GM125432-02
• Montana SMIR/STTR Matching Funds Program Grant Agreement Number 19-51-RCSBIR-005
• PI is Dr. Andreas Scherer, CEO Golden Helix.
• The content is solely the responsibility of the authors and does not necessarily represent the official views of the National
Institutes of Health.

22. CoLab Discussions:
Wednesday, Oct 16th - 12:45-1:30pm | Clinical Variant Analysis: Applying the AMP & ACMG
Guidelines in the Clinical Practice
Thursday, Oct 17th - 12:45-1:30pm | State of the Art Clinical Copy Number Variant Analysis in Next-
Gen Sequencing Data: Gene Panels, Whole Exome, Whole Genome

24. Additional Questions?
info@goldenhelix.com | goldenhelix.com