role of next generation sequencing in clinical care · -diagnosed with type i diabetes after...
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4/14/2018
1
Role of next generation
sequencing in clinical care
Anne Slavotinek
Division of Genetics, Department of
Pediatrics, UCSF
Structure
1. Next generation technologies
2. Consent and secondary findings
3. Genetics of diabetes/clinical examples
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Sanger sequencing
National Human Genome Research Institute's Talking Glossary
Sanger sequencing
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Exome sequencing
Illumina
Exon Coverage
- exons are variably covered
- ‘fill in’ can improve coverage
Sarah Garcia, Personalis
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Whole exome sequencing (WES)What is an exome?
-protein encoding regions of genes
-1-2% of genome, but 85% mutations
-90-95% of exome ‘covered’
Uses of whole exome sequencing:
-non-specific phenotypes e.g. intellectual disability
-atypical presentations
-rare, novel phenotypes
-phenotypes difficult to confirm with clinical testing
-diseases where testing is expensive
-typically sent as a trio with both biological parents
Date of download: 9/6/2014Copyright © 2014 American Medical
Association. All rights reserved.
From: Korf and Rehm, New Approaches to Molecular Diagnosis
Figure Legend:
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What to expect?
Type of Mutation Novel Non-Novel Total
Missense 303 (AA) /192 (C) 10,828/9,319 11,131/9,511
Nonsense 5/5 98/89 103/93
Synonymous 209/109 12,567/10,536 12,776/10,645
Splice 2/2 36/32 38/34
Total 520/307 23,529/19,976 24,049/20,283
Bamshad et al., 2010AA = African American
C = Caucasian
3 general types of results:
- positive
- negative
- variant of uncertain significance (VUS)
2 general categories of results:
- primary findings
- secondary findings
Exome Sequencing
Sequence variants need to be sorted to find relevant ones:
1) Deleteriousness of variant
-frameshift, nonsense > missense
-sequence conservation; protein domain
-SIFT, PolyPhen-2, Mutation Taster, CADD
2) Existing biological/functional information
-segregation with disease
-variant not seen in unaffected individuals
-expression pattern
-predicted function; pathway/gene interactions
-animal models
Jessica van Ziffle
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Diagnostic Yield/Discovery
De Ligt et al,. NEJM, 2012
-100 patients with ID
-‘trio’ approach
-total yield 16%
Srivastava, Ann Neurol, 2014
-78 patients with neurodevelopmental disabilities
-total yield 41%
-19 AD, 11 AR, 1 X-linked, 1 AD and AR
-changed management in all situations
-yield depends on testing indication
Whole exome sequencing
- exome is being considered earlier
- ‘exotyping’ - phenotyping, genotyping, reinterpretation of phenotypic
data (Pinto et al., 2016)
- digenic diseases and blended phenotypes
Whole genome sequencing
- analysis of deep intronic regions, non-coding RNA, regulatory regions
Glissen et al., 2014
- 50 trios with severe ID; array and exome negative
- WGS had yield of 42% (13 SNVs and 8 CNVs)
- no mutations in regulatory regions
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Panel Sequencing
- panel targets <150, not 24,000 genes
- all exons are sequenced
- insertions/deletions detected; algorithm vs exon array
- use a panel when clinical evaluation suggests a diagnosis
- will not result in secondary/incidental findings
- may still result in variants of unknown significance
There are THREE outcomes from arrays/panel testing:
- we may find the cause of the condition in you and your family
- we may NOT find the cause of the condition in you and your family
- we may get a result that we cannot interpret
There are FIVE outcomes from WES testing:
The previous three PLUS
-we may find another change in the DNA of medical significance but not
for the condition we tested you for
-we may find family relationships to not be what you thought they were
(misattributed parentage)
Exome Sequencing
Bob Nussbaum
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Exome sequencing
Advantages of WES:
-syndromes with high genetic heterogeneity
-syndromes that are incompletely characterized
-Neveling et al., 2013
-WES covers 81% of known pathogenic mutations on gene sets
-improved diagnostic yield compensates for reduced sensitivity
-“even if all genes that could have been ordered by physicians had
been tested, the larger number of genes captured by the exome
would still have led to a clearly superior diagnostic yield at a fraction
of the cost”
Elements of Consent
Bick and Dimmock, 2011
-basic genetics (genes, mutations)
-inheritance patterns
-penetrance and expressivity
-types of DNA variants (pathogenic, benign, VUS)
-incidental findings
-false positives, false negatives
-scientific discoveries that may result from test results
-interaction of genes, environment
-information privacy
-non-paternity
-Genetic Information Nondiscrimination Act of 2008
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Limits to informed consent - “Can you really give informed consent when you look so widely [at the
genome]? Is that manageable for patients?... (patient representative)
- “In any case I think that it's very naïve to think that a patient is more able to choose [which results to receive] when he knows more. There are limits to what patients can comprehend. Decision-making in principle does not get easier, the more elaborately a patient is informed….the quality is important and also a discussion... (ethicist)”
- greater detail can lead to less understanding
- overwhelming; information overload
- thousands variants identified; need biological parents for interpretation
- only variants relevant to presentation reported
- if parental samples included, only one report generated
Elements of ConsentLimitations
-not every gene tested / not 100% coverage
-not all kind of DNA variants
-not all genetic changes in exome
-current genetic knowledge not comprehensive
Familial relationships/consanguinity
Protection against genetic discrimination
Secondary findings
-different depending on labs
-possible information on parents
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Secondary Findings Green et al., Genet Med, 2013“Results not related to the indication for ordering the test, but that may, nonetheless, be of medical value or utility to the ordering physician or patient”
ACMG recommendations:
-actively look for known pathogenic or expected pathogenic variants
-59 genes, 24 conditions
-prevention and/or treatment available
-may be asymptomatic for a long time
-excludes conditions screened by NBS
-in proband AND for family members (if WES performed), irrespective of proband results
-“..failure to report a laboratory test result conveying the near certainty of an adverse yet potentially preventable medical outcome would be unethical.”
Interpretation of Secondary Findings Goal: Maximize positive predictive value
LOW SENSITIVITY
High false negative rate
Data as generated by WES, not same standard as primary variant finding
-recommendations to be reviewed periodically
-system to submit new genes
Amendola et al., 2015
European ancestry
–112 genes medically actionable genes: 2.0%
–ACMG 56: 0.7%
African ancestry
–112 genes medically actionable genes: 1.1%
–ACMG 56: 0.5%
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Secondary Findings - Considerations
Burke et al., 2013:-screening results, not diagnostic test results
-ascertainment bias, as mutations identified in those with disease
-unknown natural history
-phenotypic spectrum and penetrance not known
-lack of controlled studies regarding interventions
-prior probability of disease is low
-costs should not be generated if patients do not wish for results
Shahmirzadi et al., 2014
-187/200 (93.5%) chose to receive ≥1 categories of IF
-manageable if consented well, understand implications
-conditions/sequence variants need to be curated
Importance of reinterpretation of
negative results
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Genetics of Familial Hyperinsulinism (FHI)
- hypoglycemia (neonatal onset, mild to severe) (Glaser, 2013)
Autosomal recessive ABCC8 (45%) /KCNJ11 (FHI-KATP) (5%)
- associated with large for gestational age infants
- severe refractory hypoglycemia with poor response to medical management
- may require pancreatic resection
Autosomal dominant ABCC8/KCNJ11 (FHI-KATP)
- normal for gestational age
- present around one year of age (2 d - 30 y)
- responds to diet, diazoxide
- other genes include GLUD1 (5%), HNF4A (5%), GCK, HADH, UCP2 (<1%)
Genetics of Familial Hyperinsulinism (FHI)
FHI - good situation for gene panel testing
-diagnosis relatively straightforward
-genetically heterogeneous
-known genes explain much of genetic variation
-panels vary in number of genes included and gene coverage
-want to do both sequencing analysis and del/dup testing
-Panel 1: ABCC8, AKT2, AKT3, GCK, GLUD1, HADH, HK1, HNF1A, HNF4A,
INS, INSR, KCNJ11, PDX1, PGM1, SLC16A1, UCP2
-coverage: 96% at 20x
-Panel 2: 50 genes; includes some metabolic conditions
-panel includes non-coding variants; omits some exons
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Genetics of MODY
Anik et al., 2015
-1-2% of DM in Europe; 21–45/1,000,000 children and 100/1,000,000 adults
-monogenic (AD) inheritance
-early onset of DM (<25 y)
-lack of autoimmune process/insulin resistance with lack of obesity
-endogenous insulin secretion is preserved
-80% are misdiagnosed as T1D, T2D
->10 known genes
-GCK, HNF1A, HNF4A, and HNF1B genes commonest causes in UK
-represent 32%, 52%, 10%, and 6% of MODY respectively
-another good situation for gene panel testing
Genetics of MODYExamples of available panels:
Panel 1: GCK, HNF1A, HNF1B, HNF4A, PDX1
-lists sensitivity
Panel 2: 13 gene panel that includes non-coding variants
-ABCC8, BLK, GCK, HNF1A, HNF1B, HNF4A, INS, KCNJ11, KLF11,
NEUROD1, PAX4, PDX1, RFX6
-lists non-coding variants
Limitations:
-complex inversions/balanced translocations
-gene conversions
-mitochondrial DNA variants/repeat expansion disorders
-single exon deletions/duplications
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Genetics of DM – type I
Type 1 DM:
-40-50% due to inherited factors
-HLA amongst first loci; >50 loci from GWAS
-T1DGC – aggregated studies for research
-41 loci; some well known (HLA, INS, PTPN22, CTLA4, IL2RA)
-27/41 were novel
-verified loci have candidate genes; allelic heterogeneity
Robertson and Rich, 2018
Genetics of DM – type I- research predominantly in Europeans
- prevalence increasing for other ethnic groups
- genetic risk prediction
- may help with treatment
- slice/exome?
Robertson and Rich, 2018
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Personalized Medicine
Whole Exome Sequencing
-testing with whole exome sequencing (WES) is effective
-variant return has implications for other family members
-variant interpretation is likely to change
-secondary findings can be medically actionable
-returning results in children for adult onset disorders
-how should care be delivered?
-genetics versus non-genetic professionals
-ad hoc versus a dedicated clinic
UCSF Personalized Genomics Clinic
- provides an identity for genomic services
- not a gateway
- interpretive and research function
- supportive role for clinical testing
- consent process, results provision/interpretation
- ‘registry’ function for annual review
- database for re-analysis
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UCSF Personalized Genomics Clinic
- started in 2013; was 2x per month with 2 physicians
- now weekly with 4 physicians
- >250 patients
- providers: GeneDx, UCLA, Baylor, Ambry, Personalis, Fulgent
- now to UCSF
- referrals: numerous testing providers from different specialties
Able to:
- facilitate re-analysis, longitudinal data collection
- aid to clinicians in variant interpretation
- dedicated clinic enables consistent provider care
- trainee education
Trios increase yield: 12/143 (8%) proband only
11/143 (8%) duo
120/143 (84%) trio
Lee et al., 2014: trio approach increases diagnostic yield
-proband only 74/338 (22%)
-trio 127/410 (31%)
Age group tested can influence yield: 15/143 (10.5%) ≤ 1 yr of age
60/143 (42%) 1-5 y
32/143 (22.5%) 6-10 y
20/143 (14%) 11-17 y
16/143 (11%) 18+ y
Posey et al., 2015: diagnostic rate: 85/486 17.5% in adults
-lower than for Pediatrics; 7% had blended phenotypes
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Patient examples
1. WES can result in unexpected findings that improve care
-11 yo male referred to Genetics
-sensorineural hearing loss at 2 yo
-loss is severe to profound
-diagnosed with type I diabetes after presenting with
several days of emesis and abdominal pain
-cytopenias, including anemia, also noted at presentation
-negative Otoscope gene panel (108 gene panel)
-chromosomal microarray arr(1-22)x2, (X,Y)x1
-MRI of inner ear normal
Patient examples-two pathogenic variants in SLC19A2
-maternally inherited variant: c.484C>T, p.Arg162Ter
-classified as pathogenic
-paternally inherited variant: c.515G>A, p.Gly172Asp
-previously reported variant, classified as pathogenic
-biallelic pathogenic variants in SLC19A2 are associated
with Thiamine-responsive megaloblastic anemia syndrome
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Patient examples
2. Results have implications for other family members:
-9 yo male with autism
-frameshift variant in TREX1; heterozygous; paternally inherited
-father had h/o iritis and fevers
-AR TREX1: Aicardi-Goutieres disease
-AD TREX1: chillblains; SLE
-51 yo male with retinal dystrophy
-deleterious variant in PRRT2; heterozygous; unknown inheritance
-paroxysmal kinesogenic dyskinesia
-benign seizures in grandchild
-importance of experienced counselors/genetic interpretation
Summary
-next-generation technologies are in common use
-panel and exome are most frequent tests
-differ in consent process, secondary findings
-testing has implications for other family members
-testing can yield complicated, unanticipated results
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Thank you!
UCSF Genomics Medicine Initiative - Pediatrics
Co-directors:
Neil Risch Pui-yan Kwok
Clinicians: Wet lab:
Anne Slavotinek Jessica van Ziffle
Joseph Shieh Heather Pua
Marta Sabbadini
Bioinformaticians: Pathology/Fellow:
Mark Kvale Jude Abadie
Ugur Hodoglogil
Ethics: Library/Scholarly Communication:
Barbara Koenig Megan Laurance
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