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Jan Jongbloed Laboratory Specialist Clinical Genetics Genome Diagnostics Department of Genetics UMCG Groningen Cardio Gene panel experience

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Page 1: Cardio Gene panel experience - BioSB research schoolbiosb.nl/wp-content/uploads/2014/10/Day-2-Jongbloed... · 2014-10-29 · Department of Genetics Ludolf Boven, Krista Bos, Lennart

Jan JongbloedLaboratory Specialist Clinical GeneticsGenome DiagnosticsDepartment of GeneticsUMCG Groningen

CardioGene panelexperience

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Cohorts:

Department of Genetics

AcknowledgmentsCardioGenetics:Rowida AlmomaniLudolf BovenAnne HerkertYvonne HoedemaekersIrene van LangenElisabetta LazzariniAnna PósafalviWouter te RijdtRichard SinkePeter van TintelenPaul van der Zwaag

Dutch Cardiology Depts:Maarten vd Berg (UMCG)Folkert Asselbergs (UMCU)Sebastiaan Piers (LUMC)Arthur Wilde (AMC)

Project 671239 Doelmatigheidsfonds UMCG

Genome diagnostics:Annemieke van der HoutJos DijkhuisenLennart JohanssonHenny LemminkMartine Meems-VeldhuisInge MulderRenée NiessenArjen ScheperMartijn VielYvonne Vos Dutch Clin Genet Depts:

Jasper vd Smagt (UMCU)Daniella Barge (LUMC)Karin van Spaendonck-Zwarts (AMC)

CGD:Terry VrijenhoekEdwin CuppenJoris VeltmanJohan den DunnenRaoul Hennekam

GCC (bionformatics):Lennart JohanssonJoeri van der VeldenPieter NierinckxMorris Schwertz

Genetics research:Eddy de BoerCleo van DiemenKrista van DijkRolf SijmonsBirgit Sikkema-RaddatzPieter vd VliesCisca Wijmenga

Dutch Diagnotics Sections:Dennis Dooijes (UMCURonald Lekanne (AMC)Marjon Slegtenhorst (EMC)Arthur vd Wijngaard (MUMC)

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cardiomyopathies

Department of Genetics

DCM; dilatedcardiomyopathy

normalheart

HCM; hypertroficcardiomyopathy

ACM; aritmogeniccardiomyopathy

Wilde & Behr (2013) Nat Rev Cardiol 10:571-83

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Rapid increase

Department of Genetics

van der Zwaag, thesis, 2012

Num

ber

of g

enes

repo

rted

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Department of Genetics

Jongbloed (2011) Expert Opin Med Diagn 5:9-24van der Zwaag, thesis, 2012

Challenge for routine diagnostics:Extensive genetic heterogeneity

60+ genes involved

In multiple clinical phenotypes

no full penetrance

~20% diagnosis

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Department of Genetics

Dutch cardio(myopathy) centers

Gene panel based appraochCardiomyopathies:

Groningen

5 Centers:

Dennis Dooijes (UMCU)Ronald Lekanne dit Deprez (AMC)Marjon Slegtenhorst (EMC)Arthur van de Wiingaard (MUMC)Jan Jongbloed (UMCG)

Utrecht

Amsterdam (AMC)

Rotterdam

Maastricht

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Department of Genetics

Exome sequencing

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MYBPC3

PKP2

Exome sequencing 2011/2012:Agilent Sure Selectwhole exome kit vs 4:

Av cov: 30-60xGreen: >20xOrange: 10-20xRed: <10x

Rowida Almomani

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MYBPC3

PKP2

Exome sequencing 2013:Agilent Sure Selectwhole exome kit vs 5:

Av cov: 50-80xGreen: >20xOrange: 10-20xRed: <10x

Rowida Almomani

Exome sequencing:-High number of variants clinical interpretation-Insufficient coverage missing mutations

Targeted sequencing:capturing of exons of certain genes

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Novel strategies: NGS

Department of Genetics

Candidate GeneScreening

Next GenerationSequencing

Which application?

Aim: apply one comprehensive test.Design and implement various targeted next generation sequencing (NGS) gene-panels

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Targeted NGS

Department of Genetics

Sequencing:Illumina MiSeq machine151 bp sequencingPaired end

Enrichment:Agilent SureSelect

Data analysis:NextGene software

Data filtering/interpretation:Cartagenia software+ Alamut software

In addition Sanger sequenced amplicons: 15 for identification15 of badly covered regions

12 pat/MiSeq run!

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Targeted NGS

Department of Genetics

Sequencing:AMC: Illumina MiSeqEMC: Illumina MiSeqMUMC: Illumina HiSeq 2000UMCU: Solid 5500

Enrichment:AMC: Nimblegen SeqCap easy choiceEMC: Agilent SureSelect custom kitMUMC: Agilent SureSelect WESUMCU: Agilent SureSelect custom kit

Data analysis:AMC: BWA + Genome Analysis TKEMC: BWA + SeqPilotMUMC: MaasVar databaseUMCU: ?

Data filtering/interpretation:AMC: Cartagenia + AlamutEMC: SeqPilot + AlamutMUMC: MaasVar databaseUMCU: Cartagenia + Alamut

Sanger sequencing badly covered amplicons + confirmation

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Department of Genetics

Ludolf Boven, Krista Bos,Lennart Johannson, Eddy de Boer

Cardio-panel v1; 48 genes

MiSeq capacity:1 channel 5 miljoen reads

Readlength 150 bp 5.000.000 x 150 bps = 750.000.000 bp

Paired-end 750.000.000 x2 = 1.500.000.000 bp

Accuracy 75% 75% x 1.500.000.000 = 1.125.000.000 bp

Size Cardio Custom 320.000bp 1.125.000.000 bp /320.000 bp = 3515 bp

12 patients multiplex 3515 /12 = 292

Average coverage 292x

Validationcriteria:

Coverage                                             minimal 30 for each nucleotidecompared to Sanger:                          Specificity 100%

Sensitivity   98%

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Department of Genetics

Ludolf Boven, Krista Bos,Lennart Johannson, Eddy de Boer

Cardio-panel v1; 48 genes

ABCC9, ACTC1, ACTN2, ANKRD1, BAG3, CALR3, CRYAB, CSRP3/MLP, DES, DMD, DSC2, DSG2, DSP, EMD, GLA, JPH2, JUP, LAMA4, LAMP2, LMNA, MYBPC3, MYH6, MYH7, MYL2, MYL3, MYPN, MYOZ1, MYOZ2, PKP2, PLN, PRKAG2, PSEN1, PSEN2, RBM20, RYR2, SCN5A, SGCD, TAZ, TBX20, TCAP, TMEM43, TNNC1, TNNI3, TNNT2, TPM1, TTN, VCL, ZASP/LDB3

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Department of Genetics

Processing in NextGene (1)

Convert FastQ file to FastA

Alignment of reads

Check of read quality with criteria: (removal of unsuitable reads)

median of the read ↑Q20

if a >3bp stretch cannot be called: removal

at least 25 useable bp for mapping

if ≥ 3bp (adjacent) quality ↓Q16: removal or trimming

removal of duplicate reads

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Department of Genetics

Processing in NextGene (2)

Calculating coverage per bp (report on badly covered regions)

Output: mutation report for identity check

Calling of variants in ≥ 20% of reads(Allele frequency >0.20)

Output: vcf-file with variants

Upload into

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Department of Genetics

Birgit Sikkema-Raddatz, Ludolf BovenLennart Johannson, Eddy de Boer

Cardio-panel v1; 48 genes1. Technical validation:- Coverage (quality of sequence data)- Specificity (confirmation Sanger) (100%)- Sensitivity (false positive rate NGS) (98%)- Reproducibility

24 patients, with Sanger sequencing data (~6 genes) 5 patients, duplicate analysis

2. Clinical validation: novel patients multiple runs

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Department of Genetics

Cardio-panel v1; 48 genes

- 48 genes, 1134 targets - Coverage >30: 99% (<30: 4,398 bps out of 323,651 bps)

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Department of Genetics

Cardio-panel v1; 48 genes

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Reproducibility

Department of Genetics

5 patients analysed twice (in different runs):

- 231 variants (198–268) were detected per sample

- on average, 10 unique variants (8–14) were reported

- in total, 1,007 variants were detected; 51 of these were differently reported; nonconcordance rate: 0.00315%

Due to:* 12/51: badly covered regions* 24/51: alignment problems; different annotation

same variant* 15/51: false positives; allele freq ~0.2; only in F or R

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Conclusions

Department of Genetics

Resequencing of gene panels on the MiSeq can be used in routine diagnostics

99% of all bases of the target genes is of high quality

No false positives

No false negatives

12-16 patients can be multiplexed

Average coverage: >200x (currently ~400x)

~15 exons require Sanger sequencing in parallelSikkema-Raddatz (2013) Hum Mutat

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Department of Genetics

Onco-panel v1; 73 genesBRCA1, BRCA2, PTEN, NF1, CDK4, MUTYH, APC, MSH2, MSH6, MLH1, PMS2, CDH1, STK11, SDHB, RET, SDHD, WT1, SDHC, MEN1, SDHA, FLCN, VHL, NF2, PTCH, FH, BMPR1A, SMAD4, CHEK2, RAD51C, RAD51D, BRIP1, XRCC2, BARD1, HOXB13, KLLN, MITF, ENG, AXIN2, BMP4, TMEM127, CDC73, AIP, CDKN2B, CDKN2C, CDKN1A, CDKN1B, SDHAF2, MAX, PHOX2B, TERT, RUNX1, CEBPA, GATA2, PTCH2, MET, SUFU, TP53, CDKN2A, BAP1, PALB2, DICER1, SMARCB1, SMARCA4, BUB1B, PALLD, EGFR, PDGFRA, KIT, PRKAR1A, ATM, CEP57

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Department of Genetics

Onco panel v1; 73 genesApplication on36 patients

9000 variants

40 variants- 35 substitutions- 5 indels

Filter onnovel variants

n = 40

Sanger Sequencingup to 6 genes

No false-positivesTotal: 105 variants

64 variants- 19 substitutions- 45 indels

Validation on24 patients

Targeted NGS for 73 genes

Sanger Sequencing

64/64 confirmed

Total no. of variants

No false-positives ornegatives

73 genes,996 targetsCoverage >30:99%

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Department of Genetics

Onco panel v1; 73 genes

Class 2 (n = 32/70 ) e.g. RAD51C, MAX, ALK, …Preventive options available for the frequently associated tumor types

No official guidelines yet

Class 1 (n= 25/70 ) e.g. BRCA1, MLH1, RET,…

Preventive options available for the frequently associated

tumor types

Following national / international guidelines

Class 3 (n =  13/70) e.g. TP53, KIT, BAP1, ….

No preventive options available for frequently associated  tumor 

types (e.g. pancreatic cancer, sarcoma)

3 virtual sub-panels based on preventive options

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Department of Genetics

Onco panel v1; 73 genes

Class 2 (n = 32/70 ) e.g. RAD51C, MAX, ALK, …Preventive options available for the frequently associated tumor types

No official guidelines yet

3 virtual sub-panels based on preventive options

Class 1 (n= 25/70 ) e.g. BRCA1, MLH1, RET,…

Preventive options available for the frequently associated

tumor types

Following national / international guidelines

Class 3 (n =  13/70) e.g. TP53, KIT, BAP1, ….

No preventive options available for frequently associated  tumor 

types (e.g. pancreatic cancer, sarcoma)

Most patients choose 1 + 2

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Department of Genetics

Status of targeted gene panels

Panel No. of genes

Coverage > 20 for each

No. of patients

Cardio 55 99,3 >1000Onco 73 99,3 ~200Movement 88 99,4 ~100Skin 63 99,4 32Epilepsy 147 99,6 ~40Neuro Agilent IDLiver Agilent ID

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Department of Genetics

Jos Dijkhuis, Martine Meems-Veldhuis, Inge Mulder, Paskal Norel, Arjen Scheper, Martijn Viel

Cardio-panel v2; 55 genesABCC9, ACTC1, ACTN2, ANKRD1, BAG3, CALR3,

CAV3, CRYAB, CSRP3/MLP, DES, DMD, DSC2, DSG2, DSP, DTNA, EMD, EYA4,

GATAD1, GLA, JPH2, JUP, LAMA4, LAMP2, LMNA,

MYBPC3, MYH6, MYH7, MYL2, MYL3, MYPN, MYOZ1, MYOZ2, NEXN, PKP2, PLN, PRKAG2,

PSEN1, PSEN2, RBM20, RYR2, SCN5A, SGCD, TAZ, TBX20,

TCAP, TMEM43, TNNC1, TNNI3, TNNT2, TPM1, TTN, TXNRD2, VCL, ZASP/LDB3

Since September 2012 in Routine Diagnostics:

>1000 patients received~1000 sequenced

-> ~1000 reports sent-> ~2-4 MiSeq run (12 patients) per week-> Of these 2 cardiomyopathy runs

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Department of Genetics

Dutch cardio panels

AMC:23 genes (454): 350 patients41 genes (Solid): 140 patients41/46 (MiSeq): 270 patientsTTN: 60 (454), 20 (S), 50 (Mi)Aritmie: 130 (S), 50 (Mi)

Groningen

Utrecht

Amsterdam (AMC)

Rotterdam

Maastricht

EMC:45 genes (cardiochip): 500 patients

UMCU:CM, 64 genes: 300 patientsConduction panel, 33 genes: 60 patientsCongenital, 34 genes: 50 patientsTTN: 100 patientsConnective tissue, 18 genes: 60 patients

MUMC:34 genes (cardiochip): 260 patients34 genes (454): 100 patients45 (HiSeq): 220 patients

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Department of Genetics

Analysis: workflow

FASTQ-file FASTA-file

VCF-file

Challenge: Data interpretationPer patient ± 250 variants

Benign -- Pathogenic

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Department of Genetics

Filtering:- BED file (exons +/- 20 bp)- Quality (>20x)- 1000 genomes (≥0.02 MAF; ≥200 observation)- GoNL (≥0.02 MAF; ≥200 observation)- ESP (≥0.05 MAF; ≥200 observation)- SNP database (≥0.02 MAF; ≥200 observation)- “managed variant lists”

Cardio-panel v2; filteringPer patient 5 – 15 variants

Annemieke van der Hout, Henny Lemmink, Renée Niessen, Yvonne Vos

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Department of Genetics

Cardio-panel v2; filtering

Annemieke van der Hout, Henny Lemmink, Renée Niessen, Yvonne Vos

Coverage >20

MVL* poly MVL

artefactGONL 2%

1000 genomes2% >200 observations

ESP5%>200 observations

dbSNP2% >200 observations

MVL Likely Benign

*MVL = managed variant list

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Department of Genetics

Filtering:- BED file (exons +/- 20 bp)- Quality (>20x)- 1000 genomes (≥0.005 MAF; ≥200 observation)- GoNL (≥0.005 MAF; ≥200 observation)- ESP (≥0.005 MAF; ≥200 observation)- SNP database (≥0.005 MAF; ≥200 observation)- “managed variant lists”

Cardio-panel v2; filteringPer patient 0 – 5 variants

Annemieke van der Hout, Henny Lemmink, Renée Niessen, Yvonne Vos

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Department of Genetics

Annemieke van der Hout, Henny Lemmink,Renée Niessen, Yvonne Vos

Cardio‐panel v2; interpretationFields Gene Variant Previous Classification HGMD How often found Relevante isoforms Grantham Score Allele frequency Population frequency

(1000 G, GoNL)

Conclusion Category:

Alamut: PhyloP score Mutation Taster Polyphen SIFT Align GVGD Conservation Splicing Google Scholar

BenignLikely BenignVOUSLikely PathogenicPathogenic

Pathogenic:-truncating mutations in “usual suspects” -missense mutations with sufficient proof

Likely Pathogenic:-truncating mutations in genes less studied-missense mutations fullfilling:

*conserved (at least up to chicken)*most or all prediction programs: pathogenic*not or <0.0005 MAF in control populations

Exception: not fullfilling the above,but additional data available

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Department of Genetics

Diagnostic ReportReport includes

Only (likely) pathogenic mutations(with disclaimer that not all variants are reported)

Conclusion regarding: genotype – phenotype correlation

All tested genes

Total coverage (% of total region of interest covered with >20x)

Average read depth

Request for affected family members for segregation analysis

Normal reports: made by senior technicians, authorized by staff

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Department of Genetics

Anna Pósafalvi

Cardio-panel v2; diagnostic yield

• Yield LP +P = 45%• Note: ~15% >1 P/LP• ~40% P/LP in “usual suspects” • Truncating TTN mutations: 36 (9%) of cases

-> 28 (13%) DCM patients-> 5 (5%) HCM patients-> 1 ARVC, 1 NCCM, 1 CM patient

43 pathogenic; 11%

134 likely pathogenic; 34%

213 VOUS/likely benign; 55%

390 patients

Diagnostic yield:

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Department of Genetics

Anna Pósafalvi

Cardio-panel v2; diagnostic yield

• Yield LP +P = 45%• Note: ~15% >1 P/LP• ~40% P/LP in “usual suspects” • Truncating TTN mutations: 36 (9%) of cases

-> 28 (13%) DCM patients-> 5 (5%) HCM patients-> 1 ARVC, 1 NCCM, 1 CM patient

43 pathogenic; 11%

134 likely pathogenic; 34%

213 VOUS/likely benign; 55%

390 patients

Diagnostic yield:

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Department of Genetics

Conclusion implementation

Improvements:* Reducing Turn-Around-Times1. Further robotization of sample processing

2. Optimizing the “pipe line” (Filtering parameters)

3. Automation of interpretation process

* Detection of exon deletions/duplications to avoid additional

MLPA analyses.

* Improving data interpretation

1. Targeted NGS can replace Sanger Sequencing

2. Improved diagnostic yield (~50% for cardiomyopathies)

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Department of Genetics

Improvements/challenges

Improvements:* Reducing Turn-Around-Times1. Further robotization of sample processing

2. Optimizing the “pipe line” (Filtering parameters)

3. Automation of interpretation process

* Detection of exon deletions/duplications to avoid additional

MLPA analyses.

* Improving data interpretation

1. Targeted NGS can replace Sanger Sequencing

2. Improved diagnostic yield (~50% for cardiomyopathies)

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Department of Genetics

Annemieke van der Hout, Henny Lemmink,Renée Niessen, Yvonne Vos

Cardio‐panel v2; interpretationFields Gene Variant Previous Classification HGMD How often found Relevante isoforms Grantham Score Allele frequency Population frequency

(1000 G, GoNL)

Conclusion Category:

Alamut: PhyloP score Mutation Taster Polyphen SIFT Align GVGD Conservation Splicing Google Scholar

BenignLikely BenignVOUSLikely PathogenicPathogenic

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Department of Genetics

Jos Dijkhuis, Inge Mulder, Jerbic

Cardio‐panel v2; interpretation

Dit zit er nog ondercartagenia alamut

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Department of Genetics

Improvements/challenges

Improvements:* Reducing Turn-Around-Times1. Further robotization of sample processing

2. Optimizing the “pipe line” (Filtering parameters)

3. Automation of interpretation process

* Detection of exon deletions/duplications to avoid additional

MLPA analyses.

* Improving data interpretation

1. Targeted NGS can replace Sanger Sequencing

2. Improved diagnostic yield (~50% for cardiomyopathies)

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Department of Genetics

Exon deletion/duplications

Average coverage per target

Targets from X chromosome

One serie of 12 samples

Another serie of 12 samples

Instead of MLPANumber of reads: deletion, duplication compared to normal

Normalisation to avoid false positives

Lennart Johansson, Birgit Raddatz

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Department of Genetics

Exon deletion/duplications

1. Best match: determine the control group

0

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Controles with the most similar pattern compared to the sample

2. Normalisation per sample and per gen

Compare sample with control group:

Deletion: Ratio 0.65, Z-score <-3 Duplication: Ratio 1.25, Z-score >3

Lennart Johansson, Birgit Raddatz

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Department of Genetics

Exon deletion/duplications

Quality control (calculation of variation)

Threshold to exclude

Bad samples

Bad genes (targets)

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Department of Genetics

Exon deletion/duplications

Validation of 120 samples, including 10 known deletions/ duplications

On average 907 of the 930 targets of the onco panel pass the thresholds.No false negative results.

Lennart Johansson, Birgit Raddatz

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Department of Genetics

Exon deletion/duplications

Results

2 positive controls in bad samples1 positive control bad target

1 positive control bad target

CardioOnco

Lennart Johansson, Birgit Raddatz

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Department of Genetics

Improvements/challenges

Improvements:* Reducing Turn-Around-Times1. Further robotization of sample processing

2. Optimizing the “pipe line” (Filtering parameters)

3. Automation of interpretation process

* Detection of exon deletions/duplications to avoid additional

MLPA analyses.

* Improving data interpretation

1. Targeted NGS can replace Sanger Sequencing

2. Improved diagnostic yield (~50% for cardiomyopathies)

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Department of Genetics

Interpretation

29 support pathogenicity (affected carrier)

13 partly support pathogenicity

6 support no pathogenicity (affected not carrier)

81 families

Outcome cosegregation analysis:

33 not decisive (presymptomatic; 1st degree rel)

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Data sharing

Department of Genetics

Database/sharing (groep 3, Richard Sinke, UMCG)

•Project 1: VKGL, open source (Morris Swertz)Sharing of all data (vcf files); focus on technical aspects first

•Project 2: Cartagenia (Renée Niessen)Sharing data cardiomyopathy panels

•Project 3: pre-NGS data:Via managed variant lists Cartagenia?

Renée Niessen, Dennis Dooijes, Marjon Slegtenhorst,Ronald Lekanne dit Deprez, Arthur van de Wijgaard

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Department of Genetics

Data sharing; cardiomyopathies

Gene panel based appraochCardiomyopathies:

Groningen

5 Centers:

Dennis Dooijes (UMCU)Ronald Lekanne dit Deprez (AMC)Marjon Slegtenhorst (EMC)Arthur van de Wiingaard (MUMC)Jan Jongbloed *UMCG)

Utrecht

Amsterdam (AMC)

Rotterdam

Maastricht

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Department of Genetics

Guidelines NGS

Apply to diagnostic guidelines and recommendations

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Department of Genetics

Minimal gene set:

As of may2013:

Core disease gene listCardiomyopathieën:

46 genes

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Department of Genetics

Data sharing; cardiomyopathies

Goals

• Proof of concept• Identify potential issues• Guide development of complete NGS

consortium solution

Renée Niessen, Dennis Dooijes, Marjon Slegtenhorst,Ronald Lekanne dit Deprez, Arthur van de Wijgaard

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Department of Genetics

Filtering:- BED file (exons +/- 20 bp)- Quality (>20x)- 1000 genomes (≥0.02 MAF; ≥200 observation)- GoNL (≥0.02 MAF; ≥200 observation)- ESP (≥0.05 MAF; ≥200 observation)- SNP database (≥0.02 MAF; ≥200 observation)- “managed variant lists”

Cardio-panel v2; filteringPer patient 5 – 15 variants

Annemieke van der Hout, Henny Lemmink, Renée Niessen, Yvonne Vos

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Department of Genetics

Data sharing; cardiomyopathies

benignbenignpathogeniclikely benignbenign

(Limited) phenotype:Hypertrophic CMRestrictive CMDilated CMRight ventricular CM

One‐clickSubmission

Patie

nt1234

Curation, Validation

Phenotype, filter, assess, interpret, classify, report

LAB

• Frequency statistics• Curation information• hom/het; affected

ClinicalUse

NGS consortium solution

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• Lessons learned: Lowlands consortium for CNV– >20k cases; already solved diagnostic cases!

• In parallel: similar NGS pilot in US– 5 labs (CHOP hospital lead); panels & exomes

Department of Genetics

Data sharing; cardiomyopathies

One-clickSubmission

Curation, Validation• Frequency statistics• Curation information• hom/het; affected

ClinicalUse

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Department of Genetics

Data sharing; cardiomyopathies

• First Phase (Finished..)

–Groningen (UMCG) & Utrecht (UMCG)–Both using Bench Lab NGS platform

• Second Phase (Started…)

–Add Rotterdam (EMC), Amsterdam (AMC) & Maastricht (MUMC)

Pilot: Who?

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Department of Genetics

Data sharing; cardiomyopathies

• Analyzed variants– Per analysis => Frequency information– Inconsistent or incomplete labeling possible

• Curated variants– In Bench: Managed Variant List (MVL)– Analysis independent

Two levels of Data

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Department of Genetics

Data sharing; cardiomyopathies

• UMCG– # patients: 1000– # analyzed variants: 230k (6k unique)– # curated variants: 2000 (1800

unique)

• UMCU– # patients: 150– # analyzed variants: 40k 2.5k unique)– # curated variants: 530 (500

unique)

First Phase: Some Numbers

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Department of Genetics

Data sharing; cardiomyopathies

• Only 4 pathogenic variants common between the two labs:

– p.C796R PKP2– p.R79* PKP2– p.Q791fs MYBPC3– p.P955fs MYBPC3

First Phase: Findings

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Department of Genetics

Data sharing; cardiomyopathies

• Both g-, c- and p-notation important!

• Example:–Utrecht

• 11_47359280_-/C c.2373dupG p.Q791fs

–Groningen• 11_47359282_T/CT c.2372delAinsAG p.Q791fs• 11_47359282_-/C c.2371_2372insG p.Q791fs

First Phase: Findings

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Department of Genetics

Data sharing; cardiomyopathies

• Sharing immediately provides new and valuable information.

• Identified important issues.–Notation–Labeling methodology

• e.g. What exactly means ‘likely pathogenic’ ?• e.g. When and how often do you label variants?

Intermediate Conclusions

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Conclusions

Department of Genetics

Gene panel based NGS succesful in clinical diagnostics

Challenges (technical, interpretation, reporting) however

still remain, even in gene-panel based approaches

Deletion/duplication detection from targeted NGS data

possible

Data sharing important for further interpretation

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Cohorts:

Department of Genetics

AcknowledgmentsCardioGenetics:Rowida AlmomaniLudolf BovenAnne HerkertYvonne HoedemaekersIrene van LangenElisabetta LazzariniAnna PósafalviWouter te RijdtRichard SinkePeter van TintelenPaul van der Zwaag

Dutch Cardiology Depts:Maarten vd Berg (UMCG)Folkert Asselbergs (UMCU)Sebastiaan Piers (LUMC)Arthur Wilde (AMC)

Project 671239 Doelmatigheidsfonds UMCG

Genome diagnostics:Annemieke van der HoutJos DijkhuisenLennart JohanssonHenny LemminkMartine Meems-VeldhuisInge MulderRenée NiessenArjen ScheperMartijn VielYvonne Vos Dutch Clin Genet Depts:

Jasper vd Smagt (UMCU)Daniella Barge (LUMC)Karin van Spaendonck-Zwarts (AMC)

CGD:Terry VrijenhoekEdwin CuppenJoris VeltmanJohan den DunnenRaoul Hennekam

GCC (bionformatics):Lennart JohanssonPieter NierinckxMorris Schwertz

Genetics research:Eddy de BoerCleo van DiemenKrista van DijkRolf SijmonsBirgit Sikkema-RaddatzPieter vd VliesCisca Wijmenga

Dutch Diagnotics Sections:Dennis Dooijes (UMCURonald Lekanne (AMC)Marjon Slegtenhorst (EMC)Arthur vd Wijngaard (MUMC)

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