NGS IN THE CLINIC
A review of its impact on test development, operations
and result reporting
Qiagen Webinar – January 19, 2016
Birgit Funke, PhD, FACMG
Associate Professor of Pathology, Harvard Medical School
Director Clinical R&D; Laboratory for Molecular Medicine
3
http://www.nature.com/nrg/journal/v11/n1/images/nrg2626-f2.jpg
WHAT WILL BE COVERED
1. How is NGS used in the clinic today
• Focus on inherited disorders
2. Bringing NGS in house:
• Clinical development and validation
• The role of bioinformatics
3. What NGS does not do well
• Technical limitations
1. Time permitting: Real world challenges (case example)
THE DISRUPTIVE NATURE OF NGS
NGS
(research)
•
NGS
(clinical)
Clinical NGS is being implemented in an increasing number of labs
• Majority focus on gene panels
• Implementation of exome/genome sequencing quickly increasing
DETECTION RATE OVER TIME
DCM
2011: ~37%
2007: ~10%
NGS
WHICH DISORDERS
BENEFIT FROM
NGS TESTING?
EXAMPLE: INHERITED CARDIOMYOPATHIES
HCM
DCM
ARVC
OTHER (RARE)
LVNC
RCM
1:500
•
•
•
•
> 1:2,500
1:1,000 - 1:5,000
Collective incidence: > 1/500
Can lead to SCD
Substantial genetic component
Incentive for predictive testing
DISEASE CHARACTERISTICS
• Locus heterogeneity - 1 disease / many genes
• Allelic heterogeneity - many disease causing variants/gene
• Spectrum of pathogenic variation - not yet well understood
• Phenotypic overlap - can complicate testing process
LOCUS HETEROGENEITY
DCM
TNNT2
HCM
MYBPC3
50%
TTN
DSP
MYH7
34%
MYH7
LMNA
Melissa Kelly
Ahmed Alfares
ALLELIC HETEROGENEITY
# of Variants
Hypertrophic Cardiomyopathy: 10 years – 11 genes tested
63% of variants have been seen only once
Need to sequence entire coding sequence
of many genes for maximum clinical
sensitivity
MYBPC3
E258K
MYBPC3
927-9G>A
MYH7
R663H
MYBPC3
W792fs
R502W
# of Probands
Courtesy of Ahmed Alfares and Heidi Rehm
CLINICAL HETEROGENEITY
Traditional genetic testing : 1 gene panel for each diagnosis
• Proband with clinical Dx +
family history of DCM
• DCM gene panel detects a
variant of uncertain
significance
+
+
+
+
3
• Variant did not segregate
3
PREVENTING DIAGNOSTIC ODYSSEYS
_
• Patient was seen again ,
diagnosis was revised to
ARVC
+
+
• ARVC panel identified a
likely pathogenic variant
+
+
+
+
+
3
3
Traditional testing (disease centric) does not make sense for
disorders with clinical and genetic overlap
+
MULTI-DISEASE GENE PANELS
HCM test
DCM test
ARVC test
MULTI-CARDIOMYOPATHY TEST
HCM
DCM
ARVC
• ~3% of DCM patients carry a pathogenic variant in an ARVC gene
MULTI DISEASE GENE PANELS IMPROVE
CLINCAL DIAGNOSIS
GeneReviews (2012): “80-90% of patients with Costello syndrome carry a
mutation in HRAS.”
LMM broad referral population data:
(n=23)
Most patients
would have
received a
NEGATIVE
report if only
HRAS had
been tested
MULTI DISEASE GENE PANELS IMPROVE
CLINCAL DIAGNOSIS
Phenotypic expansion
• Original clinical definition based on most severe cases
• Often too narrow, full range of clinical variability emerges over time
Phenotypic overlap
• Disorders present the same -> diagnostic “error”
• Happens more often as genetic testing is moving out of specialty
clinics to more general (genetics) care
Now widely recognized
• Many physicians are beginning to change workflow
THE CHANGING GENOMIC TESTING WORKFLOW
“SEQUENCE FIRST”
Patient
Clinical
“sequence first”
Hypothesis
Diagnosis
Genetic
Test
Sequence
Variants
negative
Result
Clinical
Diagnosis
Inter
pretation
• Fill in failed
• Confirm variants
THE CURRENT DEBATE: PANELS OR EXOME?
GENE PANELS
EXOME
GENOME
10s – 100s of
genes
22,000
genes
Exome +
Intergenic
•Med-Low coverage
•Completeness <100%
•High coverage
•Completeness
Clinically well
defined cases
?
Complex phenotypes /
diagnostic odysseys
INCENTIVES
• A large fraction of panels are NEGATIVE (often >50%)
• Growing appreciation of “phenotypic expansion” – argument
for hypothesis fee testing
• Additional tests often end up being more expensive than WES
• Always up to date (accelerating pace of gene discovery)
• Easier to maintain for labs than growing # of gene panels
BARRIERS
BARRIERS
• Cost (though gap is closing)
• Incomplete coverage (suboptimal design)
RISK
• Loss of intimate /a priori knowledge on tested genes
COMMUNITY EFFORTS TO DEVELOP STANDARDS
ACMG + AMP (2015)
New guideline for clinical grade variant
classification (Mendelian disorders)
ClinGen
Unite medical genetics community by
developing approaches to
curate, centralize and share genetic data
NEW!
ASSESSMENT OF GENE-DISEASE RELATIONSHIPS
• Traditionally not needed because old tests
contained only well established disease genes
• Many published claims for a gene-disease
relationship do not withstand the rigor of clinical
grade curation
• Most novel variants in genes that are not strongly
linked with disease cannot be interpreted
USING CLINICAL VALIDITY TO CONFIGURE TESTS
Definitive
evidence
Strong evidence
Moderate
evidence
Limited evidence
Disputed /
no evidence
Predictive Tests,
Incidental Findings
Diagnostic Panels
Exome/Genome
Research
Courtesy of ClinGen (Heidi Rehm)
CLINICAL TEST
DEVELOPMENT AND
VALIDATION
(NGS)
DEVELOPMENT AND VALIDATION OF AN (NGS) TEST
DESIGN
TEST
DESIGN
LIBRARY
PREP
http://www.cdc.gov/genomics/gtesti
ng/ACCE/
VALIDATION
OPTIMIZATION
•
•
•
•
CAPTURE
SEQ
ANALYSIS
Test each step
String together
Optimize
Lock down protocol
Test analytical
performance using
locked down
protocol
DESIGN - A THREE DIMENSIONAL EXERCISE
Technology
Content
Gene selection
• Clinical validity
Gene characteristics
• Homology, High GC/AT?
• Pathogenic variant types
• Mosaicism?
X
Sequencer
• capacity/ln
• Read length
Bioinformatics pipeline
• SNV
• Indel
• CNV
• SV
Techn. Reality Check
=
Operational
• Target size
• # samples/lane
• Sample volume received
• Test cost, TAT
Diagnostic
• % gene unsequenceable
• Difficult variants prevalent
• Ability to detect low AF
Development + validation plan
Clinical Reality Check
Supplemental assay/s when:
• NGS suboptimal but gene or variant
type essential
Validation (reference) samples
• Specimen types
• Variant Types
• Reference material sources
Fill-in + confirmation strategy (Sanger)
Consult domain experts
• Difficult gene critical?
• Alternate assays offered
elsewhere
• Required analytical
sensitivity*?
• Required completeness
* TP/TP+FN
THERE IS NO “ONE SIZE FITS ALL”
Two difficult to sequence genes – two solutions*
(Laboratory for Molecular Medicine)
Renal Panel
(PKD1)
Both present similar challenge
Hearing Loss Panel
(STRC)
Essential gene (detection rate)
YES (85%)
YES (10%)
Sequenceable via NGS
Highly homologous (partial)
Highly homologous (entire gene)
Pathogenic variant spectrum
SNVs, indels >>CNVs
CNVs >>SNVs, indels
Well sequenced gene
YES
NO
Clinical specificity
HIGH
LOW
Keep on panel
NO - alternate test for ADPKD
YES
Supplemental assay
N/A
LR-PCR x 2
Operational factors
N/A
High failure LR-PCR #1 – DROP
Impact on performance metric
N/A
Reduced analytical sensitivity OK
Two very different solutions
TEST DEVELOPMENT/OPTIMIZATION
DESIGN
VALIDATION
OPTIMIZATION
• Select test contents
• Characterize contents
• Clinical needs
LIBRARY
PREP
CAPTURE
SEQ
ANALYSIS
• Technical limitations
• Supplemental assays?
• Fill-in and variant
confirmation ?
• Establish development
+ validation plan
•
•
•
•
•
Test each step
String together
Optimize if need be
Lock down protocol
Develop Q/C stops
Test analytical
performance using
locked down
protocol
NGS RUN
PRIMARY
ANALYSIS
BASE CALLING
FASTQ
Sometimes on the
Sequencer (newer
machines, e.g.
MiSeq)
DEMULTIPLEXING
SECONDARY
ANALYSIS
ALIGNMENT
BAM
output
files
VARIANT CALLING
“bioinformatics pipeline”
VCF
ANNOTATION
TERTIARY
ANALYSIS
PRIORITIZATION
FILTRATION
CLASSIFICATION
CLINICAL REPORT
Trend is to add more
functionality to the
sequencer
THE ROLE OF BIOINFORMATICS
(…not just for NGS)
Research
Clinical
Dev
Bioinformatics
IT
Clinical
Ops
BIOINFORMATICS STAFF
(Partners Personalized Medicine)
Molecular Dx Lab + Translational Genomics Core
2008
2009
2010
2011
7
1st NGS
panel
Bioinformatics FTEs
6
5
4
3
2
1
NGS work
started
2012
2013
2014
COMMONLY USED DATA ANALYSIS FILTERS
•
•
Secondary Analysis
•
Coverage (read depth)
•
Strand bias
•
Mapping quality (confidence in location of read)
•
Allelic fraction (% reads containing variant)
•
Quality by depth
Tertiary Analysis
•
Variant frequency in control cohorts
COVERAGE : HOW MUCH IS ENOUGH?
Red = reads
containing a
variant call
Courtesy of Karl Voelkerding
• In theory, a heterozygous variant is present in 50% of the reads
• In reality, this is not always the case (range can be broad)
• To be confident to identify variants with a range of allelic fractions (e.g. >20%):
Need ~20x coverage to be 99% confident to detect variant allele
For details see: Schrijver 2012, J. Mol. Diagn. 14
REALITY CHECK
Need ~20x coverage to be 99% confident to detect heterozygous variant *
Example LMM (germline) –
46 genes (~250 kb)
• Very high average coverage
(300-400x)
• >97% of bases >20x
• 3% <20x = 7.5 kb
• Reducing avg. coverage
increases the % of the test with
insufficient coverage!
Need to sequence at high coverage to maximize completeness
* Schrijver 2012, J. Mol. Diagn. 14
ANALYSIS THRESHOLDS – ALLELIC FRACTION
203 samples; n=1455 variants: NGS + Sanger
TRUE POS
FALSE POS
HOM
TAZ_EX10
c.718G>C
TTN_EX37
[SB= -3536.45]
c.8887A>C
TTN_EX37
[SB= 17.08]
c.8887A>C
[SB= 14.07]
TTN_EX37
c.8887A>C
[SB= 8.04]
•
Need large “truth sets”
•
Until recently there was none labs could obtain pre-test development
•
Data shown here generated from launching NGS pipeline w/o any
threshold (2011)
•
very high overhead for confirmatory testing till truth set established
HET
via Sanger confirmation
•
Now have some community resources: NIST’s NA12878 high
confidence regions
LMNA_EX10
c.*4C>T
[SB= -106.6]
TTN_EX157
c.31861A>G
[SB= -855.52]
TTN_EX45A
c.15318T>C TTN_EX155
[SB= -146.03] c.31709T>C
[SB= -10.01]
ALLELIC FRACTION THRESHOLDS SNVs + IN/DELS
STRAND BIAS
READ 1
READ 2
A T C C G T G G A T T T C G T T A C A C G T T
T
T
T
T
T
See also: Allhoff et al. BMC Bioinformatics 2013 14 (Suppl 5):S1 doi:10.1186/1471-2105-14-S5-S1
ANALYSIS THRESHOLDS – QUALITY BY DEPTH
QUALITY CONTROL STEPS
DNA Extraction
• OD 260/280
Library
Generation
• Fragment size (each step)
Sequencing
• Cluster Density, Error rate
• Total reads, % reads pass filter
Alignment,
Variant Calling
• % reads on target, avg coverage
• Min cov/base, library complexity,
• MQ, Strand Bias, GC/AT bias, etc
Filtration,
Prioritization
Variant
confirmation
• Concordance w/ NGS result
Variant + Gene
assessment
• Double review
Interpretation+
Report
• Double review of variants
and interpretation
• Q/C Approach?
• Individual steps?
• End-to-end?
Both is best!
• End-to-end critical measures
• Completeness
• Accuracy
EXAMPLE Q/C STEPS - CLUSTER DENSITY (HiSeq)
Difficult to prescribe standard – reduced quality at this step can be acceptable
depending on the overall test design (fill in, confirmation)
DETERMINE NEED FOR ORTHOGONAL
CONFIRMATION OF VARIANTS
Substitutions *
In/dels
FN
0/542
3/70
% FN
0%
4.29%
SENS
100%
95.70%
95% CI
99.3-100
89.8-99.2
FP
1/1321
9/60
% FP
0.08%
15.00%
When initial performance assessment reveals high FP rate one needs to
plan for orthogonal confirmation
Today, NGS is deemed of high enough accuracy for SNVs such that
confirmation may not needed
Laboratory for Molecular Medicine 2013, unpublished data (HiSeq2000, 50b PE)
TEST VALIDATION
DESIGN
VALIDATION
DEVELOPMENT
• Select test contents
• Characterize contents
• Clinical needs
LIBRARY
PREP
CAPTURE
SEQ
ANALYSIS
• Technical limitations
• Supplemental assays?
• Fill-in and variant
confirmation ?
• Establish development
+ validation plan
•
•
•
•
Test each step
String together
Optimize
Lock down protocol
Test analytical
performance using
locked down
protocol
WHY IS NGS DIFFERENT?
• Genetic testing traditionally synonymous with genotyping (assessing
whether known pathogenic variants are present)
• Sequencing based tests predominantly used to screen the entire coding
sequence enables novel variant detection
• Core issues
• Cannot validate every theoretically possible variant
• The NGS testing process is highly complex (many steps)
• Increased requirement for bioinformatic processing
• Knowledge required for interpretation does not scale proportionally
• These issues are not entirely new – but are an order of magnitude
worse compared to traditional methods (e.g. Sanger sequencing)
• NGS “pushed us over the edge” - catalyzed awareness and a push for
novel approaches and standards
ANALYTICAL PERFORMANCE METRICS
DESCRIPTION
EXAMPLE
Quantitative Test
Result can have any value between 2
limits)
Heteroplasmy of
mitochondrial variants
Qualitative Test
True quantitative signal can only have
one of two possible values
Sequencing (WT or
variant allele)
• See also:
• Mattocks 2010: Eur J Hum Genet 18:1276
• CDC MMWR 2009 Vol 58 (www.cdc.gov/mmwr)
• Jennings 2009 Arch Pathol Lab Med.133:743–755
SENS
SPEC
ACC
PREC
LOD
Different approaches
depending on type of test
Sens:
Spec:
Acc:
Prec:
LOD:
Sensitivity
Specificity
Accuracy
Precision
Limit of Detection
ANALYTICAL SENSITIVITY
Base line (“methods-based approach”)
• Maximize # of variants tested to achieve high statistical confidence
• Pathogenicity doesn’t matter
• Determine separately by variant type (SNV, indel, CNV)
• Critical importance of confidence interval!!
VARIANT TYPE
#
FN SENSITIVITY
All variants
415
2
99.52%
Substitutions
350
0
100.00%
Indels (all)
65
2
96.92%
>10 bp 14
1
92.86%
95%CI
98.2-100
98.9-100
89.8-99.2
70.2-98.8
Disease/gene specific add-ons
• Test samples with common disease causing mutations
• Example: validate the mutations present on the ACMG 23 panel
for CF if NGS test covers CFTR
ANALYTICAL SPECIFICITY
Definition
• Ability to return a negative result when no variant is present
• Reflects frequency of false positives: TN / TN + FP
Common practice – count WT bases correctly identified as WT
• 20 kb sequenced: 5 FP 19995 true negatives
• Specificity = 19995 / 20000 = 99.98%
Not very useful – other metrics are more meaningful
• Technical Positive Predictive Value (likelihood that a variant
call is FP): TP/ TP + FP
• Number of FPs per TEST
• Particularly important if lab doesn’t confirm variants!
VALIDATION LAYERS
EXAMPLE: ANALYTICAL SENSITIVITY
TIER 1: BASE LINE (METHODS BASED, CUMULATIVE)
NA12878
Test 1
(100 genes)
Variants
600
Test 2
(70 genes)
300
Test 3
(30 genes)
100
Test 4
(200genes)
800
Analytical sensitivity (+CI)
(=TP/TP+FN)
TIER 2: DISEASE/GENE SPECIFIC CONSIDERATIONS
Well character.
gene
+ n samples (e.g. frequent pathogenic variants)
In/del rich gene + n samples
CNV rich gene
+ n samples
Mosaicism
+ n samples
Need for more
reference materials!
25 bp deletion (intron 32, MYBPC3)
Dhandapany et al. Nat Genet. 2009. 41(2):187-91
• c.3628-41_3628-17del (outside
region interpreted by many labs)
• MAF = 4-8% in SE Asia
• Het: Mild/late onset HCM (high
lifetime penetrance) – OR ~6
• Hom: Severe and early onset
disease, often Heart Failure
WHAT NGS
DOES NOT DO WELL
CURRENT NGS
LIMIATIONS
THE DEVIL IS IN THE DETAILS
NGS HAS PROBLEMS WITH
• Regions of high homology
• Repeat expansions
• Large in/dels, CNVs and other
structural variants
http://www.trizic.com/the-devilis-in-the-details-how-preparedare-you-for-a-presence-exam/
LESS PROBLEMATIC FOR GENE PANELS
• Testing labs typically have clinical domain expertise
• Contents typically highly curated + well understood
• Add-on tests typically available for difficult genes
KNOWLEDGE DOES NOT SCALE EASILY
• Sequencing ≠ understanding
SEQUENCING GENES WITH HOMOLOGY
STRC (nonsyndromic hearing loss)
• 99.6% identical across cds (98.9% incl. introns)
• First half of gene 100% identical (incl. introns)
29
1
100% identical incl. introns
maps to pseudogene
Mandelker 2014, J. Mol. Diagn.
#
%
#
%
#
HOMOL. HOMOL.
#
HOMOL. HOMOL.
GENE
EXONS EXONS
EXONS
GENE
EXONS EXONS
EXONS
HYDIN
86
78
91
RPS17
5
5
100
TTN
363
26
7
OPN1LW
6
5
83
NEB
183
24
13
OCLN
9
5
56
STRC
29
23
79
GBA
12
5
42
DDX11
27 recessive
18
67
PMS2 hearing15
33
STRC: 10% of
nonsyndromic
loss 5
RANBP2
29
15
52
FANCD2
44
5
11
DPY19L2
22
13 of congenital
59
FCGR3B
6
4
67
CYP21A2: Nearly
all
adrenal hyperplasia
TNXB
44
12
27
RHD
10
4
40
NCF1
11
11
100
CEL
11
4
36
VWF: Von Willebrandt’s
disease
(common coagulopathy)
ADAMTSL2
19
10
53
CATSPER2
13
4
31
SMN1
9
9
100
CHEK2
16
4
25
SMN2
9
9
100
CLCNKB
20
4
20
PMS2:
Colorectal
CA
(on
ACMG
Incidental
Findings
gene
list)
ABCC6
31
9
29
NOTCH2
34
4
12
CYP21A2
10
8
80
TBX20
8
3
38
IKBKG
10
8
80
KRT81
9
3
33
OTOA
28
8
29
KRT86
9
3
33
Exome
wide clinical
grade
shared via Get-RM
CFC1
6
6 resource
100 to beSTAT5B
19 (in progress)
3
16
OPN1MW
6
6
100
PIK3CA
21
3
14
CHRNA7
10
6
60
HS6ST1
2
2
100
VWF
52
6
12
HBA1
3
2
67
Diana Mandelker, Himanshu Sharma, Mark Bowser
NGS IN THE CLINIC
REAL WORLD
CHALLENGES
A WORD OF CAUTION
•
THE NGS PROCESS IS HIGHLY COMPLEX
•
EVEN THE BEST INFORMATICS AND Q/C MEASURES
DO NOT REPLACE A CRITICAL MIND
•
WATCH OUT FOR RESULTS THAT DON’T MAKE SENSE
CASE EXAMPLE
9 year old boy with suspected Noonan syndrome
TEST ORDERED
Noonan Spectrum Panel (12 genes tested via NGS)
NEGATIVE
FAMILY HISTORY REVISITED
• Physician ordered whole exome
• Pathogenic missense variant in SOS1
• Gene WAS covered by our test…
• What went wrong?
some features
• Variant in untested region of SOS1 or
variant type not detectable?
• Technical false negative?
….at this point the lab director received
an unpleasant email
Accession ID
PM13-00632A
Index/Barcode
GCAT
Amplicons for Sequencing Follow-up (1)
PTPN11 01
DIGGING IN THE TRASH….
Failed Regions (1 entries, 1 amplicons)--> exons with 1 or more bases that are insufficiently covered (<20x)
PTPN11 PTPN11_Exon_01 44/44
Variant calls
Gene
Exon
Coverage A;B breakdown B frequency Mapping Quality Category
Classification
Predicted Zygosity
Predicted Variant Predicted AA Strand bias
All entries below are variant calls that were removed by the pipeline
Common variants meeting criteria to be classified as "BENIGN" or "LIKELY BENIGN"(7 entries, 7 amplicons)
Gene
HRAS
KRAS
MAP2K2
MAP2K2
SPRED1
SPRED1
SPRED1
Exon
Exon 02
Exon 05
Exon 06
Intron 07
Exon 03
Intron 04
Exon 07
Coverage A;B breakdown B frequency Mapping Quality Category
761 503;258
0.34
57.84 Variant
1000 0;1000
1
59.25 Variant
945 501;444
0.47
31.67 Variant
692 0;692
1
59.38 Variant
942 424;518
0.55
59.54 Variant
992 417;575
0.58
59.24 Variant
1000 410;590
0.59
59.22 Variant
Classification
Predicted Zygosity
Predicted Variant
Benign (Het)
c.81T>C
Benign (Hom)
c.483G>A
Benign (Het)
c.660C>A
Benign (Hom)
c.919+12A>G
Benign (Het)
c.291G>A
Benign (Het)
c.424-8C>A
Benign (Het)
c.1044T>C
Predicted AA Strand bias
p.His27His
-2494.88
p.Arg161Arg
-13656.42
p.Ile220Ile
-4434.96
-6098.73
p.Lys97Lys
-8898.5
-6371.93
p.Val348Val
-9031.77
FILTERED (REMOVED FROM ANALYSIS) SECTION
Common False Positives (3 entries, 3 amplicons): Strand Bias >20 or Allelic fraction <0.2
Gene
HRAS
KRAS
SOS1
•
•
•
Exon
Intron 02
Exon 06
Exon 14
Coverage A;B breakdown B frequency Mapping Quality Category
Classification
Predicted Zygosity
Predicted Variant Predicted AA Strand bias
481 424;57
0.12
58.83 Low_AF
Benign (Het)
c.111+15G>A
-222.02
961 856;105
0.11
59.34 Low_AF
Benign (Het)
c.519T>C
p.Asp173Asp
-118.12
985 631;354
0.36
59.44 Strand_bias
(Het)
c.2200A>C
p.Thr734Pro
97.48
SOS1 Thr734Pro: strand bias = 97.48
Highest strand bias observed in technical validation = 1.78
Used threshold of 20 since
TEST LIMITATIONS IN CLINICAL PRACTICE
• 100% sensitive for SNVs
• CI = 99.3-100%
Technical false
negatives will
happen
ACKNOWLEDGMENTS
LMM
Development
• Heidi Rehm
• Birgit Funke
• Matt Lebo
• Beth Duffy-Hynes
• Sami Amr
• Mark Bowser
• Heather McLaughlin • Diana Mandelker
• Heather Mason-Suares Fellows
• Jun Shen
• Ozge Birsoy
Operations
• Lisa Mahanta
• Robin Hill Cook
• Laura Hutchinson
• Shangtao Liu
• Ashley Frisella
• Timothy Fiore
• Jiyeon Kim
• Maya Miatkowski
• William Camara
• Eli Pasackow
• Yan Meng
• Ahmad A. Tayoun
• Jillian Buchan
• Fahad Hakami
• Hana Zouk
Genetic Counselors
• Amy Lovelette
• Melissa Kelly
• Mitch Dillon
• Shana White
• Andrea Muirhead
• Danielle Metterville
Marketing
• Priscilla Cepeda
Office
• Peta-Gaye Brooks
• Kim O’Brien
• Lauren Salviati
• Charlotte Mailly
• Cassandra Novick
Transl. Genomics Core
Sami Amr
• All team members
IT Team
• Sandy Aronson
• Natalie Boutin
• All team members
ClinGen leadership and cardiovascular domain working group
Bioinformatics
• Matt Lebo
• Rimma Shakhbatyan
• Jason Evans
• Himanshu Sharma
• Peter Rossetti
• Ellen Tsai
• Chet Graham
Leadership and Faculty
• Scott Weiss
• Meini Sumbada-Shin
• Robert Green
• Immaculata deVivo
• Mason Freeman
• John Iafrate
• Mira Irons
• Isaac Kohane
• Raju Kucherlapati
• Cynthia Morton
• Soumya Raychaudhuri
• Patrick Sluss
THANKS!
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