In this webcast on February 19th, Gabe Rudy, Vice President of Product Development, will showcase publicly available databases and resources available for interpreting rare and novel mutations in the context of his own personal exome obtained through a limited 23andMe pilot in 2012. The last couple years have seen many changes in well-established resources such as OMIM and dbSNP, while motivating new efforts such as ClinVar and PhenoDB to bring NGS interpretation to clinical grade through a global data sharing effort. In this webcast, Gabe will cover: The changing landscape of public annotations: Then, Now, and Soon. Will the new human reference (GRCh38) released in December be a game changer? Specific examples of improvements in annotation and algorithms that result in more accurate analysis of his own exome. The utility and progress of NGS to different clinical applications in terms of public resources: carrier screening, hereditary cancer risk, pharmacogenomics, oncology care, and genetic disorder diagnosis. Sharing of new clinical data: How both variation and phenotype level data is currently being shared and what will be the way forward to match rare and undiagnosed cases at a global scale.

1. Using Public Access
Clinical Databases to
Interpret NGS
Variants
February 19, 2014
Gabe Rudy
VP Product Development
Golden Helix

2. Use the Questions pane in
your GoToWebinar window
Questions during
the presentation

3. My Background
 Golden Helix
- Founded in 1998
- Genetic association software
- Analytic services
- Hundreds of users worldwide
- Over 800 customer citations in scientific
journals
 Products I Build with My Team
- SNP & Variation Suite (SVS)
- SNP, CNV, NGS tertiary analysis
- Import and deal with all flavors of upstream data
- GenomeBrowse
- Visualization of everything with genomic coordinates.
All standardized file formats.
- RNA-Seq Pipeline
- Expression profiling bioinformatics

4. Agenda
Getting High Quality Variant Calls
Data Sharing and the Maturing of Public Resources
2
3
4
Clinical Grade Candidate Variant Identification
How I Met My Exomes1
NGS Clinical Utopia: Are We There Yet?5

5. Exome Sequencing in Consumer Genomics
 Exomes done as part of Pilot
Program
 80x coverage
 Raw data with no interpretation
Erin
JIA
Gabe
(me)
Ethan

6. Research or clinical grade?
Total Reads 140M
Unique Align 87%
Mean Target 105x
% Target at 2x 97%
% Target at 10x 94%
% Target at 20x 89%
% Target at 30x 83%

7. Agenda
Getting High Quality Variant Calls
Data Sharing and the Maturing of Public Resources
2
3
4
Clinical Grade Candidate Variant Identification
How I Met My Exomes1
NGS Clinical Utopia: Are We There Yet?5

8. Alignment and Variant Calling Broken Down
 2012 2 VCFs from
23andMe
- BWA 0.6.1
- GATK (early & late 2012)
 2013 Real Time Genomics
- v3.1.2 2013-05-02
- Called on Trio
 2014 Rerun
- BWA 0.7.6 (2014-01-31)
- FreeBayes
2014 -
BWA/
FreeBayes

9. PSPH mis-alignment

10. Splice Mutation

14. GRCh38 – Here Now, but still Waiting
 A better human reference
- Revised Cambridge Reference
Sequence (rCRS) MT
- Has centromere models
- ~2000 incorrect alleles fixed
- ~100 assembly gaps updated
 No Gene Annotations
- RefSeqGene - Feb 2014
- Ensembl Q4 2014
 No Variant Annotations
- Re-align 1000 Genomes and
NHLBI 6500?
- dbSNP?
GRCh37 GRCh38
Ts/Tv 2.06558 2.10171
snps
snps
mnps
mnps
indels
indels
complex
complex
270000
280000
290000
300000
310000
320000
330000
340000
GRCh37 GRCh38
My Exome
331,824
319,442

15. Blog Post

16. Agenda
Getting High Quality Variant Calls
Data Sharing and the Maturing of Public Resources
2
3
4
Clinical Grade Candidate Variant Identification
How I Met My Exomes1
NGS Clinical Utopia: Are We There Yet?5

17. Baylor Workflow - Clinical Exomes Paper
Disease gene related
Medically actionable
deleterious variants
Deleterious variants in ACMG
gene list
Deleterious variants
VUS in dominant gene or
homozygous in recessive
gene
Deleterious variant in gene
with no known disease

18. Data Sources to Replicate Workflow
 1000 Genomes (Phase 1)
 “ESP” (NHLBI 6500 Exomes v2)
 HGMD (Public vs Professional)
 Variant’s Protein Coding Effect
 RNA Splicing Effect
 Genes Lists:
- Single-Gene Disorder (OMIM with Inheritance)
- Medically Actionable (114 genes NHLBI study)
- Dominant Inheritance (MedGen)
- ACMG Carrier Panel (ACMG Incidental
Findings guidelines)

19. My Exome Analyzed
Start: 235,689
847
234,842
224,914
9,928
9,069
807
859
40
242 13
59 565
0
624
624
255
20
20
20
0
0
598
644

20. Pathogenic by RSID match

21. Agenda
Getting High Quality Variant Calls
Data Sharing and the Maturing of Public Resources
2
3
4
Clinical Grade Candidate Variant Identification
How I Met My Exomes1
NGS Clinical Utopia: Are We There Yet?5

22. Applications of NGS Data in the Clinic
Carrier screening –
prenatal and standard
Lifetime risk prediction
Genetic disorder
diagnostics
Oncology care
PGx – dosage and
care

23. ClinVar
 Submitters:
- OMIM: Johns Hopkins
- Samuels
- Lab for Molecular Medicine
- Invitae
- Emory Genetics Lab
Star rating system
- 0-4 stars – level of review
ClinVar is designed to provide a freely accessible,
public archive of reports of the relationships
among human variations and phenotypes, with
supporting evidence.

24. HGMD
 Data mines academic
papers for reported
functional variants
 Also takes
submissions,
corrections reviewed by
team
 First available in 1996
- Originally 10k variants
- 105k in Public (2014)
- 148k in “Pro” (2014)

25. Example: CFTR
 Different Variant Sources
- CFTR2 (John Hopkins)
- UMD-CFTR
- ACMG
 ClinVar
- 1632 Variants
- 442 Marked Pathogenic
 ClinVitae
- 446 Variants
- 325 Marked Pathogenic
 Caution Needed – Delta F508 Alignment

27. CFTR delta F508

28. BRCA: The back door to Myriad’s database
1995 – Patent issued
to Myriad Genetics
June 2013 – Patents
invalidated by ruling
Lab setting up Dx
has a lot of catch up
“Free the Data” and
other ways in which
Mryiad’s data is in
ClinVar, etc.
Sharing Clinical Reports Project

29. ClinVitae: ClinVar and Friends by Invitae
Sources:
- ClinVar (62,913)
- Emory (13,365)
- ARUP (2,850)
- Carver Mut (199)
- K Cunningham (581)
79,907 V, 9,189 G
- 32,523 Pathogenic
- 38,796 Likely Pathogenic
Provided in HGVS
- 59,878 after mapping to genomic space

30. BRCA: In my wife

31. Agenda
Getting High Quality Variant Calls
Data Sharing and the Maturing of Public Resources
2
3
4
Clinical Grade Candidate Variant Identification
How I Met My Exomes1
NGS Clinical Utopia: Are We There Yet?5

32. Training
 Most variants are rare or novel
- Training to interpret these is
extensive
 MD/Pathology background is
insufficient
 Need a PhD in molecular
genetics
 There’s only 500 board certified
Clinical Molecular Geneticists
since started
 Let’s share in the learning
process
Baylor Exome Sign-Out

33. Thank you
 Heidi Rehm – Chief Laboratory Director at
Laboratory for Molecular Medicine,
PCPGM
 Joel Parker – Cancer Genetics, UNC
Chapel Hill
 Gerry Higgins – VP, Pharmacogenomic
Science, Assure Rx Health
 Frank Schacherer – Chief Technical
Officer, BIOBASE
 Reece Hart – Computational Biologist,
Invitae
 Greta Linse Peterson – Director of Product
Management and Quality, Golden Helix

34. Use the Questions pane in
your GoToWebinar window
Questions?

35. [cut slides after this]

36. Phenotypeing and Matchmaking Portals
 PhenoDB
 PhenomeCentral.org
 Orphanet – Resources on over
6000 rare diseases and orphan
drugs.
 European centric:
- GEN2PHEN (G2P)

37. Updated VCF and report at end of October
GATK is a Research Tool. Clinics Beware.

38. Rare Disease Resources
 Rare defined as affecting fewer
than 200k people.
- Most affect fewer than 6000
- 25M Americans have a rare disease
 NIH Genetic and Rare Diseases
Information Center (GARD)
 ClinicalTrials.gov
 Orphanet – Resources on over
6000 rare diseases and orphan
drugs.

39. Cancer Resources
 Behind germline because:
- Sharing cancer data is more wholesale.
You don’t just post a variant + a
phenotype, you have to have whole
variant sets
- Cohorts are not covering enough ethnic
groups. African americans under-
represented
- Not a lot of incentive for large cancer
centers to share their internal
databases
 What do we do with the data?
- 70% of tumors can find driver genes.
But not many have actionable drugs.
- Need much more evidence based trials
to find more examples like BRAF
V600E
 Pic of BRAF V600E and drug