The ClinGen Sequence Variant Interpretation Working Group aims to refine criteria for classifying genetic variants by standardizing how different types of evidence are integrated. In the short term, it will refine and modify current American College of Medical Genetics guidelines. It will work with disease-specific groups to evaluate criteria like population frequency thresholds and computational methods. The long term goal is to develop a quantitative Bayesian framework to classify variants. The working group will analyze ClinVar to identify disease genes with many reported variants to help evaluate criteria.

1. The ClinGen Sequence Variant Interpretation
(SVI) Working Group-
Refining Criteria for Interpreting the
Pathogenicity of Genetic Variants
Marc Greenblatt, Leslie Biesecker, Danielle
Azzariti, Jonathan Berg, Sharon Plon, Heidi Rehm,
and the ClinGen SVI Working Group
3 June 2016

2. ClinGen SVI Work Group
• Co-Chairs
• Les Biesecker (NHGRI)
• Marc Greenblatt (U of Vermont)
• Danielle Azzariti (Harvard/Partners)
• Jonathan Berg (UNC)
• Steven Brenner (UC Berkeley)
• Fergus Couch (Mayo Clinic)
• Selina S. Dwight (Stanford)
• Raj Ghosh (Baylor)
• Steven Harrison (Harvard/Partners)
• Chris Heinen (UConn)
• Alison Homstad (UNC)
• Matt Hurles (Sanger)
• Peter Kang (Counsyl)
• Rachel Karchin (Johns Hopkins)
• Annie Niehaus (NHGRI)
• Robert Nussbaum (Invitae)
• Sharon Plon (Baylor)
• Erin Ramos (NHGRI)
• Heidi Rehm (Harvard/Partners)
• Tasha Strande (UNC)
• Sean Tavtigian (U of Utah)
• Kira Wong (NHGRI)
• Matt Wright (Stanford)

3. Case Study of
Confusion in Variant Classification
• Clinical hx
– Breast CA, 50 yo woman, Ashkenazi Jewish
– Mother Breast CA 62, Maternal GM Panc CA 71, Maternal
GM’s sister Breast CA 48
• Genetic Workup
– 2008: Mother (-) for the 3 common Ashk J BRCA variants
– 2015: Proband, 61 gene panel: “Negative for mutations”
– 2016: Mother’s unaffected sister, 25 gene panel (different
testing company):
• VUS in ATM
• VUS in CHEK2

4. Case Study of
Confusion in Variant Classification
• Reconciling the Genetic Workup
– Affected individual “Negative”
– Unaffected second degree relative with two VUS’s
• Query the first company regarding the VUS’s
– ATM VUS was present, classified as “Benign”, not reported
– CHEK2 VUS not present- “but if it had been present, we
would have called it ‘likely pathogenic’”
– CHEK2 VUS in ClinVar: Four labs have reported variant, all
say “Pathogenic”
• One family, 2 cancer genes, 2 discordant variants

5. Background: Need to Standardize
Variant Classification
• Conflicting systems among labs, researchers
• American College of Genetics and Genomics (ACMG)
panel developed guidelines for evaluating different
types of data (Richards et al, Genetics in Medicine, 2015)
• Qualitative evaluation of different data types
• Evidence assessed as “Very Strong”, “Strong”,
“Moderate”, “Supportive”
• Goal: to develop a system that labs can use, reduce
discordance, instill confidence in evaluating evidence

6. ACMG Framework for Classifying Variants

7. Background: ClinGen
• US NIH is funding a coordinated effort to
create a Clinically Relevant Variants Resource
• ClinVar: “public archive of reports of the
relationships among human variations and
phenotypes, with supporting evidence”
• ClinGen: “build an authoritative central
resource that defines the clinical relevance of
genes and variants for use in precision
medicine and research”

8. ClinGen Sequence Variant
Interpretation (SVI) Work Group
• Refine standards for variant interpretation
– Assess each data type in the ACMG guidelines
– Establish standards for how to integrate data
• Short term goal: Refine, clarify, and modify
current ACMG/AMP criteria
• Long term goal: Move to quantitative
Bayesian framework

9. SVI WG Process
• Start with ACMG grid
• Standardize current interpretation processes
for each cell in the grid
– Sub-groups of 3-5 people address each category
– Currently ~20 people involved in the WG
– Work with other groups, eg disease-specific WGs

10. ACMG Framework for Classifying Variants

11. ACMG Framework for Classifying Variants
First data types
to address:
-Population
Frequencies
-In silico
algorithms

12. First ACMG Criterion for SVI: “BA1”
Using Population Allele Frequency
• “BA1”- Benign, criterion can stand Alone
• Use allele frequency in control population as a
diagnostic criterion for “Benign”
– Rare that allele frequency >5% is associated with disease
• Harmonize approach of SVI with disease related
groups (Cardiomyopathy, RASopathy) so that ClinGen
presents a single coherent message

13. First ACMG Criterion for SVI: “BA1”
Using Population Allele Frequency
• Current wording: “Allele frequency is >5% in
Exome Sequencing Project, 1000 Genomes
Project, or Exome Aggregation Consortium”
• Proposed wording: “Allele frequency is >.05 in
any general continental population data set of
at least 2,000 alleles for a gene without a
gene-specific recommendation.”

14. Refining Computational Methods
• Algorithms based on sequence, structure
• To standardize, issues to address include:
– How to assess sequence alignments, algorithms
– How to validate/calibrate outputs
• Criteria for Genes to use to assess algorithms
– Variants classified as Pathogenic and as Neutral
– Disease phenotype defined clearly
– Expert group to assist in classification

15. Using ClinVar to Identify Genes
• Search ClinVar for genes with large numbers
of variants reported
• Apply filters to get missense variants
• Use ClinVar “Star” system
– One star- pathogenicity assertion from one source
– Two stars- concordant assertions from 2+ sources

16. ClinVar Genes, N>50 “2-Star Variants”
• Only 5 genes (BRCA1/2, MLH1, MSH2, CFTR) have
N>50 missense variants reported in ClinVar with
assertions from >1 source
• Exact figures are likely not accurate
• Disease focused groups (InSiGHT, ENIGMA, CFTR2)
note more missense variants than ClinVar
• Need to engage disease-specific groups

17. ClinVar Genes, N>50 “1-Star Variants”
Neutral v Path Variants N Genes
>20 Neutral >20 Path 10
10-19 Neutral >20 Path 5
<10 Neutral >20 Path 15
>20 Neutral <20 Path 6
Total >50 36
• Disease-specific groups likely would revise numbers
(e.g., CFTR includes only 9 Neutral)
• Include genes from many disease categories (cancer,
cardio, hearing loss, myopathies, dysmorph, neuro)

18. SUMMARY
• ACMG framework is a starting point for
refining criteria to classify variants
• ClinGen SVI has short and long term goals
– Add precision to individual criteria
– Create framework for integrating data
– Convert to quantitative system
• Work with disease-focused groups

20. ClinVar Genes, N>50 “2-Star Variants”
Gene Total Pathogenic Neutral
BRCA1 115 25 90
BRCA2 110 9 101
MLH1 109 82 27
MSH2 60 43 17
CFTR 54 52 2
• From Raj Ghosh extracting ClinVar summary data
• Exact figures are likely not accurate
• E.g., InSiGHT notes >350 Pathogenic, >250 Neutral
missense variants; BRCA2 has >9 Pathogenic
• Need to engage disease-specific groups

21. Short versus Long-Term Goals
• Move from qualitative system toward more
quantitative system
– Overlay probabilities onto current qualitative
terms (e.g., what is “Strong” versus “Moderate”
versus “Supporting”)
– Develop quantitative schema for the future