Download Panels vs. Exomes: An Interactive Panel Discussion

Panels vs. Exomes:
An Interactive Panel Discussion
Analytical consolidation of content
Focused
panels
Broad
panels
Exomes
Genomes
Exomes
Genomes
Interpretive consolidation of test offerings
Focused
panels
Broad
panels
Next Generation Sequencing Panels
Soma Das
University of Chicago
June 11, 2014
Next Generation Sequencing Panels
• Many different panels are clinically available
for a wide range of different disorders
• Typically ranges from ~7 genes to 200 genes
• Panels can be divided into those that are for
more defined/focused disorders and those
that are for broader categories of disease
What criteria to use to add a gene to a panel?
• How much evidence is required to implicate a
gene in the disorder
• When to draw the line with regards to the
breadth of the disease phenotype covered by
a panel
How much evidence is required to add a gene
to a panel
• Is a single publication enough
– Amount of evidence in the publication
• Some functional studies
– Contribution of the gene to the disease
• When only genomic deletions/duplications have
been described
• Susceptibility genes
When to draw the line with regards to the breadth
of the disease phenotype covered by a panel
• Phenotypic heterogeneity of genes
– e.g. SMC1A, DMD – distinct syndromic genes but also been
associated with intellectual disability
• How many patients? Single patient enough?
• Particularly challenging for panels with less defined
phenotypes, e.g. seizures, ataxia, ID
–
–
–
–
Isolated phenotype, part of a syndrome
Severity of the phenotype
Similar phenotypes
Blurring lines with exome
Next Generation Sequencing Panels
• Approximately 40 NGS panels offered in our
lab
• Smallest panel contains 5 genes while largest
panel contains 144 genes
Brain Malformation Panels
Panel
Abnormal
VOUS
Normal
Comprehensive
Lissencephaly
(16 genes)
45%
14%
41%
Cerebellar/Pont
ocerebellar
hypoplasia
(16 genes)
18%
25%
57%
Polymicrogyria
(12 genes)
7%
23%
70%
Joubert/Meckel
Gruber
syndrome
(21 genes)
35%
35%
30%
Microcephaly
(41 genes)
18%
72%
10%
Congenital Muscle Disorders Panels
Panel
Abnormal
VOUS
Normal
Congenital
Myopathy
(17 genes)
41%
52%
7%
Congenital
Muscular
Dystrophy
(21 genes)
60%
40%
Congenital
Myopathy with
Prominent
Contractures
(11 genes)
25%
75%
Limb Girdle
Muscular
Dystrophy
(24 genes)
38%
62%
Distal
Arthrogryposes
(9 genes)
10%
90%
Syndromic Panels
Panel
Abnormal
VOUS
Normal
Cornelia de
Lange
(5 genes)
26%
10%
64%
Coffin-Siris
(6 genes)
16%
21%
63%
NBIA
(9 genes)
8%
25%
67%
Rett/Angelman
(20 genes)
11%
22%
67%
Seckel
(7 genes)
16%
41%
43%
Meier-Gorlin
(5 genes)
14%
43%
43%
Diabetes/Hyperinsulinism Panels
Panel
Abnormal
VOUS
Normal
Lipodystrophy
(11 genes)
14%
14%
72%
Familial
Hyperinsulinism
(8 genes)
25%
25%
50%
Neonatal
Diabetes/MODY
(27 genes)
13%
75%
12%
Non-Specific Phenotype Panels
Panel
Abnormal
VOUS
Normal
Early Infantile
Epileptic
Encephalopathy
(21 genes)
10%
35%
55%
Infantile and
Childhood
Epilepsy
(73 genes)
9%
82%
9%
Non-Specific
Intellectual
Disability
(144 genes)
17%
69%
14%
Test Yield of NGS Panels
• The positive yield of the more focused disease panels
varies with some having a higher yield (40-50%) and
others having a lower yield (15-25%)
– Likely depends on the phenotype and genetic
heterogeneity of the disorder
• The positive yield of the broad phenotype panels
ranges from 10-20%
NGS Panels versus Exomes
• Panels for certain disease phenotypes have a high yield
and have an advantage over exomes
• Several panels have a yield in the range of that obtained
by exomes
• With panels, the genes present are fully covered and all
variants are analyzed
• Blurring of lines especially between the larger panels for
more non-specific phenotypes and exomes
Panel versus Exome Testing
Sharon E. Plon, MD, PhD
Baylor College of Medicine
BCM Wes Clinical Volume limited by insurance coverage?
•
Over 3000 samples
received, ~2500
cases analyzed
•
Continues to 85%
peds; 15% adult
•
Most common
indication is
neurologic/intellectu
al disability
•
Diagnostic rate of
25%
Germline
Cancer
Exomes are continuing to improve
• As will be discussed at this meeting, there are
many independent attempts to improve WES
testing even further:
– BCM Exome Version 2.0 (using a spike-in reagent
to improve coverage of clinically relevant genes).
– Medical exome project
– Multiple vendors introducing new exomes
reagents
– Perhaps next will be CNV calls or combined use of
array/WES
Specific WES Diagnostic Advantages
• Do not have to continually modify target:
– 100 of 500 (20%) diagnoses from 2000 BCM-WGL tests
were in disease genes reported in 2012-2013.
– Not cost effective for very rare recessive disorders with
very rapid gene discovery, e.g. bone marrow failure
• Can identify patients with more than one
Mendelian disorder yielding a blended phenotype.
• Can identify recessive disorders resulting from
recessive alleles/uncovered by UPD (6 cases).
Germline WES results reported
Cancer or Other
Patient Phenotype
Other
Medically
Actionable
PCG
Genes
Recessive
Carrier
Genes
Pathogenic
CFTR
∆F508
Pathogenic
VUS
Pathogenic
Clinically
Relevant
Variants
DICER1
nonsense
Rare WT1
missense
SCN5A
mut
CYP2A
mut
Signal
“Noise”
Incidental Findings for Cancer Patients
• One could view the reporting of incidental
findings as part of the WES signal (not noise):
– It may play a lesser role for adult cancer patients
– 26/56 genes are cancer susceptibility genes so
don’t count as incidental
– The pathogenic mutations, penetrance and
recommended surveillance for the remaining 30
genes (many cardiovascular) maybe less well
defined and thus noisier.
Cancer Panels for adults may have
more favorable Signal:Noise ratio
WES
Cancer Panels
• Do not have to
continually modify.
• Identify patients
with blended
phenotypes.
• Can identify
recessive disorders
resulting from UPD.
• Rate of cancer gene discovery not that
rapid
• Total number of disease genes much
lower than for neurologic/ID phenotypes
• Patient phenotypes are relatively
straightforward to recognize
• Recessive conditions uncommon, e.g.
MUTYH
• Tests often ordered by non-geneticist
physicians who only “want” the
pathogenic mutations
Panels VS Exomes
Madhuri Hegde, PhD, FACMG
Professor
Executive Director, Emory Genetics Laboratory
Emory University
Gene Selection for Targeted Panels
• Size of panel (~50-100 genes is moderate)
– 88 gene panels
• Level of evidence supporting inclusion of
gene
- many genes: one phenotype
- many genes: overlapping phenotypes
• Single gene reports in literature
• No risk alleles!
Exomes/Genomes
• Phenotype-driven analysis
• Data analysis by extracting data for targeted
genes
• Interrogating the entire exome/genome for
mutations in known Mendelian genes
• Carrier status analysis
• Incidental findings
Evidence based
Targeted mutation and
Sequencing panels
Clinically well defined
cases
Technically complete:
Cover all exons of a gene
Covers entire mutation
spectrum of the gene: Point
mutation, indels, CNVs,
deep intronic pathogenic
variants
Exome
(Medical exome
VS
Research exome)
Complex/overlapping phenotypes or neg. for
known genetic causes
<25% known clinically relevant genes
Technically incomplete: Does not cover all
exons
Does not cover entire mutation spectrum of
genes
Genome
Targeted Panel
CONFIRM
VARIANT CALLS
aCGH based intragenic
CNV detection
Other
Complementary/addition
al assays included in the
panel
Phenotype
Variants
XLID/Autism panel: FMR1, FMR2, Biochemical
assays
Short stature panel: Russell Silver
(H19/Lit1 methylation), UPD7
Chin et al, BMC Genetics, 2012, Askree et al, BMC Genetics, 2013,
Valencia et al, J Mol Diag, 2013, Valencia et al, PLoS One, 2013
NMD panel: FKTN insertion assay
Enhancing EmVClass
the exome………………………
Gene:
Technical
aspects
Gene:
evidence
Technically
complete
assay
Medical Exome
just "because three is better than one”
CHOP
Emory
Harvard LMM
Medical Exome
A highly curated gene resource and a technically
optimized assay to provide a stepping stone for
standardizing interpretation of genetic variation to fulfill
the promise of genomic medicine
THE MEDICAL EXOME PROJECT
FOUNDERS
•
Emory Genetics Laboratory – Madhuri Hegde
•
Children’s Hospital of Philadelphia – Avni Santani
•
Harvard/Partners Lab for Molecular Medicine – Birgit Funke
HELP STANDARDIZE MEDICAL EXOME SEQUENCING
• Develop a “medically enhanced exome” capture kit (all clinically significant genes
adequately covered) + develop ancillary assays (pseudogenes, CNV detection,
repeat regions, epigenetic assays)
•
Define medically relevant genes + develop framework for iterative curation
•
Support and integrate with evidence-based curation led community efforts
•
Ledbetter/Martin/Nussbaum/Rehm (U41)
•
Berg/Evans/Ledbetter/Watson (U01)
•
Bustamante/Plon (U01)
•
ClinVar Database (NCBI)
ClinGen
Medical EmExome
• Design provides >97% coverage of the 22,000 genes in the
exome at >20x, with a mean coverage of 100x
• Includes 100% coverage (>20X) of all exons of 3000+ diseaseassociated genes
• Recognizing difficult regions: High GC, homologous regions,
pseudogenes
Birgit Funke presentation
Samples Referred to EGL (2014)
Clinical test ordered*
Diagnostic yield (%)
CDG comprehensive panel
5.8
CMD comprehensive panel
34.6
(one incidental : RYR1)
LGMD comprehensive panel
29.5
NMD comprehensive panel
63.6
Eye disorders panel
70.8
Autism panel
14 (cyto array) +4.1
Exomes
28% (26-30%)+
Range 0-5 incidental findings (2 ACMG genes)
Range 0-2 (Pharmacogenetic and Carrier)
• *NGS/ sanger fill in/ targeted gene array for del/dup
• + Will increase with medical exome
Choosing the Right Clinical Test
Sanger
Sequencing
panels
Medical Exome
Single
gene
NextGen
~4,600 GAD
EXOME BOOST
-Enhanced exome: near
complete coverage of
medically relevant genes
-Low exon drop out
Research
Exome
Genome
~17,400 GOUS
Choosing the Right Clinical Test
Sanger
Sequencing
panels
Medical Exome
Sequencing
panels
Medical Exome
Single
gene
NextGen
Research
Exome
Genome
Medical Exome
NGS panel
- Sanger fill-in
- del/dup
Panels vs Exome
•
•
•
•
Which one to order?
Is one really better than the other?
Will the findings overlap?
Will the same findings be reported?
Turn-around time
Analytical
sensitivity
Clinical sensitivity
Gene coverage
Parental testing
Potential Results
Panel
Exome
8 weeks
99%; All coding exons of all genes on panel
are analyzed; Del/Dup included
All genes are associated with specific
phenotype of panel
Only genes included on panel are analyzed
Not required; parental follow up may be
useful
Mutations and VUS identified in genes
associated with specific phenotype
16 weeks
92%; all exons of all genes are not covered;
no del/dup
No specific phenotype needed; not all
exons/genes covered
Captures exomes indiscriminately
Recommended; can help with interpretation
and classification
Mutations, VUS, and carrier status can be
identified in any gene, including adult onset,
cancer and non-medically actionable genes
BRIDGING THE GAP BETWEEN GENE PANELS AND
EXOME SEQUENCING
ICCG Annual Meeting, 06/11/2014
Birgit Funke, PhD, FACMG
Assistant Professor of Pathology, Harvard Medical School
Director Clinical R&D; Laboratory for Molecular Medicine
http://www.nature.com/nrg/journal/v11/n1/images/nrg2626-f2.jpg
THE DILEMMA
On the one hand:
• NGS has enabled comprehensive testing
• Single gene  multi gene  multi disease
gene panels
• Multi disease gene panels improve
diagnostic accuracy (Noonan spectrum
disorders)
• GeneReviews (2012): “80-90% of patients
with Costello syndrome carry a mutation
in HRAS.”
• LMM Noonan Spectrum panel: This is not
true in a broad referral population
(n=23)
On the other hand
• The overall detection rate is only ~30%
• Consider exome sequencing?
Tom Mullen, Kat Lafferty
MANY EXOME ASSAYS HAVE HOLES
Cardiomyopathy Panel
51 genes
Gene Panel*
<1% exons not fully covered
(0.7% bp < 20x)
*PanCardiomyopathy Panel V3
Exome*
15% exons not fully covered
(3.7% bp < 20x)
*Agilent V5
ENHANCED COVERAGE
(base pair level)
ENHANCED COVERAGE
(gene level)
30 exomes, mean coverage = 127x (83-183x)
64% of genes: 100%
82% of genes: ≥99%
91% of genes: ≥95%
TOWARDS REPLACING TARGETED PANELS WITH
EXOME SEQUENCING
Pan Cardiomyopathy Panel
51 genes
Targeted capture data
Exome data (v5-plus)
0.08%
0.16%
0.08%
0.48%
=
99.19%
>99% of exons fully covered
(every base ≥ 20x)
HiSeq2000; ~400x avg coverage
99% of exons fully covered
(every base ≥ 20x)
HiSeq2500 ; ~200x avg coverage
Analytical sensitivity + specificity is equal as well
OPERATIONAL CONSIDERATIONS!
• Cost aside the exome is equal to most targeted panels
•
However- don’t forget possible operational requirements/limitations
(these may differ between laboratories)
• LMM considerations
• Can we maintain service offered for targeted NGS panels
• Sanger confirmation + fill in of missing data
• Exome pipeline needs to feed into LIMS – difficult to manage
manually above a certain volume
• Novel need
• In contrast to targeted panels, Sanger assays not pre-developed
• Need efficient process to design/test within the turnaround time
• Primer design pipeline
USING THE EXOME AS A UNIVERSAL ASSAY
EARLY EXPLORATIONS
• Small tests sometimes end up being more expensive than WES
• LMM and Brigham and Women’s Hospital have launched a process to
compare conventional tests to medically enhanced exome assay
• Are the genes of interest adequately covered in the exome assay?
• Compare price and consider exome in lieu of conventional assay
EXAMPLE
•
Ordered test: CMT Sequencing Test, Lab XXX
• 23 genes including CNV analysis
• Clin. Sens. = 65%
•
Enhanced Exome (would need additional PMP22 del/dup)
• 34 genes (99.5% bases >20x)
• Clin. Sens. = 75-80%
•
Exome turned out to be cheaper (enough to add PMP22del/dup)
LIMITATIONS OF EXOME SEQUENCING
NGS
•
Current NGS assays are not yet optimal for
• Highly homologous regions
• Triplet repeat expansions
• Structural variants
•
Why is this a bigger issue for WES than for targeted panels?
• A lab that designs a gene panel is usually familiar with gene specific
issues.
• This knowledge does not exist for every gene…
• Can result in false positive and negative variant calls, particularly if
variants are not confirmed
MEDICALLY RELEVANT GENES WITH
HOMOLOGY ISSUES
• The exome contains 1998 genes with at least one 250bp stretch of
>98% homology
• Nearly 50% of these genes have issues in >3/4 of their exons
Medical
Exome
1998
homologous
genes
286 homologous genes of
likely/possible medical
relevance
Courtesy: Diana Mandelker
SELECT GENES WITH HOMOLOGY ISSUES
Gene
Total Exons
# homologous
exons
# of homology
hits
Main disease
Onset
Prevalence
Evidence level
(3,2,1,0)
GBA
12
5
1
Gaucher Disease
pediatric
1/855 (Ashkenazi
Jews)
3
CYP21A2
10
8
1
Congenital adrenal hyperplasia
pediatric
~1/10000
3
VWF
52
6
VWF:
coagulopathy
2 Most common
von Willebrandhereditary
disease
pediatric-adult
1/100-1/1000
3
PMS2
15
5
adolescencePMS2:
On ACMG
incidental findings
list
1
Lynch Syndrome
adulthood
1/440
3
SMN1
9
9
1/10,000
3
SMN2
9
9
1/10,000
3
HBA1
3
HBA2
1
Muscular Atrophy
SMN:
ACMG Spinal
recommends
carrier pediatric
screening
1
Spinal Muscular Atrophy
2
1
Hemoglobinopathy
3
3
2
1
Hemoglobinopathy
3
STRC
29
23
CFC1
6
6
1
HYDIN
86
78
OTOA
28
IKBKG
ABCC6
recessive sensorineural
STRC:
10%Autosomal
of inherited
1
hearing loss NSHL
pediatric
pediatric
1/1000
3
Congenital heart defects
pediatric
~1/100
3
2
Ciliary dyskinesia, primary, 5
pediatric
1/16,000
3
8
1
Autosomal recessive sensorineural
hearing loss
pediatric
1/1000
3
10
8
1
Incontinentia Pigmenti
pediatric
unknown
3
31
9
2
Pseudoxanthoma elasticum
variable
1/25,000 to
1/100,000
3
THANKS!
Clinical Validation of Complex
Variants in Complex Panels
The InVitae Team
ICCG 2014
Validation: Large Panels and Exomes
Multiple variables to cover:
• Classes of Variation
• Multiple Genes and Sequence Contexts
• Lab/Sample Variability
• Interpretation (Pathogenicity, VUS Rate)
Challenges:
• Limited positive controls in public biobanks
−
•
NIST Genome in a bottle is great, but…
Practical limits to lab-lab sample exchanges
50
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|
CONFIDENTIAL
Clinical Study and CLIA Validation Samples
Group
#Samples
Type
Testing
MGH Prospective
401
Fresh Blood
BRCA1/2
Occasionally other genes
Stanford
422
Frozen Blood
or gDNA
BRCA1/2
MGH Retrospective
248
Frozen Blood
BRCA1/2
Occasionally other genes
Clinical Reference
Samples
(Coriell, NIBSC)
105
gDNA
Various
Genome Reference
Samples (Coriell)
7
gDNA
WGS, Multiple Platforms
Total
1183
~13% of MGH Prospective and Stanford Cohorts are BRCA1/2+
~35% of MGH Retrospective Cohort is BRCA1/2+
51
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CONFIDENTIAL
Variation Types Known/Observed
Type
Clinical*
Reference
Samples*
Whole
Genomes**
SNVs
97 previous
+ 926 new
64
2020
Deletions <5bp
92 + 21
21
Deletions ≥5bp
9+8
8
Insertions <5bp
24 + 6
6
Insertions ≥5bp
1+2
Delins/sub/complex
5
Deletions (CNVs)
26
Duplications (CNVs)
12
HomopolymerAssociated
23
Details
126, 40, 19, 15, 11, 9 bp
50
To 2date:
8
0 False
Positives
4
0 False
Negatives
24, 5 bp
Single exon to Entire
Chromosome
Single exon to Entire
Chromosome
3
CFTR and MSH2
* High-MAF polymorphisms excluded
** In 200-genes, post QC and Mendelian analysis of public data
52
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CONFIDENTIAL
BRCA1: c.1175_1214del40
53
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CONFIDENTIAL
BRCA2: c.9203del126
54
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CONFIDENTIAL
BBS9 exon 4 Alu insertion
55
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CONFIDENTIAL
SMAD4 – Any Guesses?
56
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CONFIDENTIAL
CNV Calling Given Coverage Variability
Take-home: %GC is not the only variable
•
•
•
•
•
•
•
•
•
•
•
Probe placement
Exon Size
Read-mapping artifacts
Enrichment/reagent lot
gDNA source/quality
Reagent handling
Hybridization temperature
Allele-specific hybridization
Reagent concentration
Wash stringency
...?
Many Copy-Number
Events are Not Simple
57
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CONFIDENTIAL
CNVitae and Related Methods
•
Model/fit every covariate you can
−
•
%GC is not the only variable
Integrated approach
−
Lab and bioinformatics
− Read-depth and sequence analysis
o Split-read,
•
discordant paired ends
Know what you don’t know
−
Ability to no-call (vs normal-ploidy call) is critical
− Partial call (“no-del” = not ploidy 0 or 1)
− Single-exon resolution is important, but occasionally
you need two
58
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CONFIDENTIAL
CNV Callability Evaluation
No single ploidy call > Q20
OR
no-del<Q35
59
Where Single-Exon and 2-Exon CNVs are
Challenging
Gene
Exons
QC Fail
DNAF2
1-3
100%
INPP5E
7-8
100%
MBL2
3-4
100%
NF1
16-24, 31-25
100%
NRAS
2-5
100%
PMS2
13-15
100%
UPF3B
2-3
4.3%
NPHP1
1-20
2%
PMS2
12
1.5%
ZFYVE26
17-18
<1%
MYBPC3
7-10
<1%
60
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CONFIDENTIAL
Further Work
Submit Clinical Variants from this Study to ClinVar
- Should be up on “Clinvitae” website sooner
Publish CNVitae
Get samples with interesting variants into GetRM?
Implications for Orthogonal Confirmation in Routine Testing
−
Objective Criteria to Identify highest confidence calls
61
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CONFIDENTIAL
Acknowledgements
Invitae
• Kevin Jacobs
• Geoff Nilsen
• Michael Anderson
• Yuya Kobayashi
• Shan Yang
• Reece Hart
• Scott Topper
• Swaroop Aradhya
• Federico Monzon
Stanford
• Allison Kurian
• Meredith Mills
• Jim Ford
Massachusetts General Hospital
• Leif Ellisen
• Andrea Desmond
• Kristen Shannon
• Michelle Gabree
62
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CONFIDENTIAL
Evaluating and Improving Clinical
Performance of NGS Based Tests
Richard Chen, MD, MS, FAAD
Chief Scientific Officer
Personalis
What Influences Accuracy of NGS Based Tests?
PIPELINE
SAMPLE
PREP
DNA
64
SEQUEN
CING
REFEREN
CE
CLINICAL
INTERPRETA
TION
Evaluating the Accuracy/Analytic Validity of NGS Tests
How are the genes being defined?
NM_003002.3 Reference Transcript
NM_001276506.1 (new RefSeq Transcript)
Are all exonic bases covered? To what minimum depth? Variability?
5’
3’
Are variants in UTRs covered?
Are intronic pathogenic
variants covered?
Sensitivity for different types of variants (SNPs, Indels, large SVs)
= Impacts Analytic Validity
65
Standard Exomes Have Gaps that Affect Accuracy & Sensitivity
Coverage over RPGR in Standard Exome
(average depth across gene = 104X)
Variability
in Coverage
(light blue = avg,
Dark blue = 1 SD)
>20X minimum
Coverage to call
heterozygous SNPs, indels
Missed Exons
Pathogenic variants
in introns and UTRs
Not covered
Standard Exomes Don’t Cover the Whole Exome
Sequencing Deeper Does Not Solve the Problem
66
Standard Whole Genomes Also Have Coverage Gaps
RPGR
Retinitis Pigmentosa
CDK11A
Neuroblastoma
67
Augmented Exome Approaches Are Designed to Improve Accuracy
Sample
Standard Exome Sequencing
Augmented Sequencing #1
Augmented Sequencing #2
High-Accuracy
Augmented
Exome
• Targets “Medical Genome”
4500-5000 Mendelian genes + 2000 genes from recent literature, cancer, pharmacogenomics
= 7000+ medical genes
+ Pathogenic intronic variants, UTRs etc.
+ Augmentation for structural variant detection
• Simply “adding more probes” isn’t sufficient.
• Need separate optimized to sample prep to address amplification and sequencing biases
• Augmented Exome results in sensitivity comparable to single gene tests & typical NGS panels
• Very important as exomes are increasingly used as a first line test
68
Augmented Exomes Improve Sensitivity, Accuracy, Finish Genes
RPGR
Augmented Exome
Standard Exome
69
Augmented Exomes Improve Sensitivity, Accuracy, Finish Genes
p.E810fs
Augmented Exome
Standard Exome
70
RPGR
Targeting Intronic Variants
CFTR: Intronic Variant Supplemented By ACE
rs75039782
c.3849+10kbC>T
Deep intronic variant recommended for carrier testing by ACMG that is missed
on a standard exome but captured with augmented exome sequencing
71
Improving Structural Variant Detection
• Structural variants can be missed with Standard Exomes
• Combined Augmented Assay + Informatics approaches can improve sensitivity to
SV’s
All 32 Reference Sample CNVs Detected
XXXX
Trisomy 9
Chromosome 14 duplication
Translocated chromosome
Partial tetrasomy chr 18
Adenosine deaminase def (large del Chr 20)
Partial monosomy
Partial trisomy chr 6
Trichorhinophalangeal Syndrome (del chr 8)
Greig Cephalopolysyndactyly Syndrome (del chr 7)
Gilles De La Tourette Syndrome (del chr 9)
XYYYY
Partial monosomy chr 10
Chromosomal Abnormality
Smith-Magenis Syndrome (del chr 17)
Trisomy-21
Tetralogy of Fallot (del chr 13)
Partial trisomy chr 22
Cri Du Chat
Partial trisomy chr 3
45,X (Turner’s Syndrome)
Autism (isodicentric chromosome 15)
Potocki-Shaffer Syndrome (del chr 11)
Copy Number Variation (CNV) Reference Panel, chosen based on relevance for cytogenetic diagnosis and is
characterized as part of a collaborative effort involving the CDC’s Genetic Testing Reference Materials Coordination
Program, clinical cytogeneticists, microarray suppliers, and the NIGMS Human Genetic Cell Repository.
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Augmented Exomes Finish 70% More Genes than Standard Exomes
# Medical Genes with 100% Exonic Bases Covered
# Genes 100% Covered at
>25x Average Locus Depth
6,000
5,000
4,000
3,000
2,000
1,000
0
ACE 12G
Exome A 12G
Exome B 12G
Impacts diagnostic yield
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Other Factors in Analytic Validity of NGS Based Tests
PIPELINE
SAMPLE
PREP
DNA
74
SEQUEN
CING
REFERE
NCE
INTERPRETA
TION
Augmented Exomes: A New Testing Option with Accurate and Broad
Coverage
Traditional Solutions
Augmented Exomes
Single
gene/panel
Finished
Genes
Standard
Exome
Arrays
SVs
75
Broad
Coverage
Broad Coverage
AND
Finished Genes
AND
SVs
Panels vs Exomes: When to Use as First Line Test?
Genetic Heterogeneity
high
low
specific
Cystic Fibrosis
Long-QT
syndrome
RASopathies
Phenotype
Single gene or Panel
HCM
DCM
Exome/Augmented Exome
Retinitis
Pigmentosa
Hearing loss
Other factors:
Seizures
Non-specific
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Neurodev
Delay
- Diagnostic yield?
- Is panel up-to-date?
- Will repeat testing be covered
by insurance?
- Overall cost ($$$, time, etc)
- Discovery potential
Panels vs. Exomes:
An Interactive Panel Discussion
Analytical consolidation of content
Focused
panels
Broad
panels
Exomes
Genomes
Exomes
Genomes
Interpretive consolidation of test offerings
Focused
panels
Broad
panels