Disease gene identification with exome sequencing

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1 Disease gene identification with exome sequencing Christian Gilissen Dept. of Human Genetics Radboud University Nijmegen Medical Centre

2 Contents Infrastructure Exome sequencing Gene identification

3 SOLiD instruments

4 Computer infrastructure 1 (10 nodes, 18Tb): normal analysis Cluster (Bioscope v1.3) Cluster 2 (7 nodes, 18Tb): testing, backup etc. NAS 20Tb Gigabit network switches

5 6 hours Sequencing 7days Backup Compressed files Read + Quality files Error correction 160Gb 3 hours Detected SNPs SNP detection Variant annotation 10 hours Annotated variants 50Mb Detected Indels Indel detection 20Mb 32 hours 2 hours Overlapping variants Prioritization DeNovo variants Candidate variants 50Gb Variant detection Corrected Read file Mapping 40Gb 80 hours Alignment bam files Run statistics 50Gb Visualization Variant interpretation

6 Another level of complexity In-house db Annotation adjustments Variant calling improvement Mapping corrections Sequencer Build

7 Custom analysis Java developed software based on a common framework Modular (pipeline) setup Quality control: Code repository Unit testing Ticket system Build scripts Release schedules

8 Run Monitoring & Interpretation Proces Monitor Monitor in: ICS Run logs for error messages Focal map Monitor in: Cycle Scan Useable beads Best + Good beads Failed panels Bead colour balance Exposure times Satay quality N2S Data Stored From: Muliplexing assignment report Percentage of BC assigned From: Multiplexing assignment report Total beads / slide Assigned beads / sample Monitor in: ICS Run logs for error messages Focal map Monitor in: Cycle Scan Useable beads Best + Good beads Failed panels Bead colour balance Exposure times Satay quality N2S Continue with Data Analysis Interpretation ICS + Cycle Scan: Focal map: Even distribution of beads Useable beads: 500 M M / slide Best + Good beads: L1: 50-60% of useable beads L10: 20-30% of useable beads Failed panel: max 5% Exposure times: <500 ms Satay quality: low amount of mixed beads N2S: % Noise L1<<< % Noise L2 with L1 preferably ~ 10% If not: go to Troubleshoot guide GO/ NO GO CRITERIA BC assignment: < 90% BC assignment: > 90% < 100 M beads / sample 100 M-120 M beads / sample1 >120 M beads / sample

9 Data Analysis Data Stored From: Mapping software % of reads mapped to genome % of reads with zero mismatches % uniquely mappable reads From: Mapping software % in target % near target % of target From: Mapping software 10x coverage statistics of all targets Median coverage of all targets Median coverage for diseasebased gene package Prediction of gender From: Annotation pipeline Total variants Nr. of unique variants Nr. of substitutions Nr. of indels Nr. of heterozygous variants Nr. of homozygous variants Transition/Transversion ratio Continue with Data Prioritization GO/ NO GO CRITERIA < 60% of reads map to genome > 60% of reads map to genome <60% of reads in/near targets 60-80% of reads in/near target1 >80% of reads in/near target <75% of all targets 10x coverage <30-fold median coverage <75% of all targets 10x coverage >30-fold median coverage >75% of all targets 10x coverage <30-fold median coverage >75% of all targets 10x coverage >30-fold median coverage <10,000 variants 10,000-15,000 variants1 >15,000 variants 1 Depending on further results obtained, sample might need to be resequenced. REPEAT sample Proces

10 Contents Infrastructure Exome sequencing Gene identification

11 Gigabases of sequence coverage

12 Enrichment efficiency

13 Number of known variants called

14 Contents Infrastructure Exome sequencing Gene identification

15 Approaches for identifying pathogenic mutations

16 Homozygousity based strategy 1 patient with Osteogenesis Imperfecta Becker et al. AJHG, 2011

17 Variant prioritization Type of prioritization filter All variants Number of remaining variants Coding and canonical splice site (SS) variants after quality13487 filtering (>5 variant reads, >15% variation) Non-synonymous variants, SS variants 6298 Not in dbsnp Not in in-house database 318 Homozygous variants (>80% variation reads) (of which autosomal) Of which overlap homozygous regions 20 (17) 3 Becker et al. AJHG, 2011

18 Control based strategy Sensenbrenner syndrome Recessive disorder Gilissen et al. AJHG, 2010

19 Variant prioritization Default prioritization yielded 139 and 158 variants Recessive model yielded 3 and 4 candidates Patient Chr Position Ref Var % Var Gene Gene Id Mut AA N K G PhyloP Inheritance NM_ NM_ NM_ Ref AA S E E chr1 chr1 chr2 150,542, ,551,993 20,009,029 C C T T T C FLG FLG WDR Paternal Paternal Paternal chr2 chr11 chr11 chr1 chr1 chr1 chr2 20,052, ,721, ,721, ,914, ,914, ,915,533 19,994,617 T C G G G G AAGGTT C A A A A A AAGTT WDR35 MFRP MFRP USH2A USH2A USH2A WDR35 NM_ NM_ NM_ NM_ NM_ NM_ NM_ SS G T P T R P SS V M S M C X Maternal Maternal Paternal Paternal Not Paternal Paternal Paternal 2 chr2 19,996,711 C T 50 WDR35 NM_ A T 5.70 Not Paternal 2 chr3 185,488,365 C T ECE2 NM_ A V 3.66 Not Paternal 2 chr3 185,491,136 C T 49.6 ECE2 NM_ R C 3.94 Not Paternal 2 chr7 57,532,789 C G ZNF716 NM_ T S Not validated 2 chr7 57,532,965 T C ZNF716 NM_ Y H 0.90 Not validated Gilissen et al. AJHG, 2010

20 Overlap based strategy Schinzel-Giedion syndrome Sporadic MR syndrome (dominant) Hoischen et al. Nat. Gen., 2010

21 Variant prioritization Variants Patient 1 Patient 2 Patient 3 Patient 4 Mean Candidate genes Total called 22,916 22,602 22,152 19,528 21,800 4,735 Exonic + SpliceSites(SS) 12,196 12,255 11,796 10,498 11,686 3,331 Non-synonymous (NS) + SS 5,556 5,618 5,427 4,802 5,351 1,634 New (dbsnp130) New (~50 in-house exomes) Hoischen et al. Nat. Gen., 2010

22 De novo based strategy MR trio High confidence variant calls After exclusion of nongenic, intronic & synonymous variants After exclusion known variants After exclusion inherited variants average 20,810 21,658 21,338 22,647 17,694 22,333 21,369 22,658 24,085 22,962 21,755 5,556 5,665 5,691 5,991 4,607 5,567 5,716 5,628 5,985 5,994 5, not validated in proband Median variant reads: 5 13 validated: 9 de novo!!! Median variant reads: 17 Vissers et al. Nat. Gen., 2010

23 De novo based strategy 10 patients with MR + parents Vissers et al. Nat. Gen., 2010

24 Why things don t work out? there are non-synonymous rare mutations within the patients, that lie within the targeted Ifexome, why don t we find them? 1.Phenotyping 2.Lack of coverage 3.Variant calling 4.Large indels 5.Biased research 6.No follow up possible

25 Future developments 1000 exomes in 2011 (5500xl) Implementation of exome sequencing in diagnostics Paired-end / Transcriptome / Whole genome

26 Acknowledgements Collaborators Clinicians worldwide AnEUploidy consortium Department Head Han Brunner Clinical Genetics Bregje van Bon, Bert de Vries, Nine Knoers Molecular Genetics Heleen Arts, Ronald Roepman Next generation sequencing team Genomic Disorders Group: Alexander Hoischen, Lisenka Vissers, Joep de Ligt, Nienke Wieskamp, Peer Arts, Marisol del Rosario, Bart van Lier, Marloes Steehouwer, Petra de Vries, Irene Janssen, Terry Vrijenhoek, Joris A. Veltman

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