Exomes and Beyond Addressing the Genome with OneSeq. Madhuri Hegde, PhD, FACMG Emory University Emory Genetics Laboratory Atlanta, GA

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1 Exomes and Beyond Addressing the Genome with OneSeq Madhuri Hegde, PhD, FACMG Emory University Emory Genetics Laboratory Atlanta, GA

2 Technologies to Detect Various Types of Mutations RESOLUTION Targeted PCR assay Sequencing/ Next Gen Seq targeted array CGH 1 bp 1bp X 1000s CNVs 100s-1000s bp whole genome array CGH FISH G-banded karyotype CNVs 10,000s- 1,000,000s bp CNVs 100,000s- 1,000,000s bp CNVs 5,000,000+ bp Scherer et al. (2007) Nat Genet 39:s7-s15

3 Next Gen Sequencing Hybrid Capture and NGS are not 100% Efficient Need to fill-in low coverage gaps (regions with <15X coverage) ~1-5% of exons have low coverage GC rich regions typically have low coverage due to poor capture Repetitive sequences often have low coverage due to alignment issues Genes with pseudogenes have to be sequenced by specially designed Sanger assays CAN NOT DETECT GENOMIC COPY NUMBER AND INTRAGENIC DELETION AND DUPLICATIONS WHOLE EXOME HAS HOLES

4 Targeted panels/ exome / genome Targeted panels Exome Genome Smaller target region Allows interrogation of entire coding region of the genome(~92% coverage) Targets the Entire genome Highest depth of coverage (sanger fill in to complete is possible) Lower compared to targeted Lowest compared to targeted and exome Difficult for detection of CNVs, Trinucleotide repeats, pseudogenes No incidental findings (Allelic changes) Difficult for detection of CNVs, indels Trinucleotide repeats, Pseudogenes Need to address incidental findings Allows detection of CNVs, indels Difficult for detection Trinucleotide repeats, Pseudogenes Need to address incidental findings

5 EXOME AND GENOME SEQUENCING IN GENOMICS Basic Research Goals (Discovery): Clinical research (medical exome): Sequence a cohort to identify a new causative gene - Need to ensure all known disease causing genes have been covered and ruled out; follow up with functional studies Sequence large subset of genes to identify disease modifiers -Need to ensure adequate coverage of genes Identify novel genomic variants Sequence ambiguous cases to identify the cause of disease - Need to ensure all known disease causing genes have been covered Be able to detect genomic and intragenic CNVs Recommend cases for discovery projects based on phenotypes -Need to ensure adequate coverage of genes and known genes have been ruled out

6 PANELS OR EXOME/GENOME

7 Finishing the exome Gene: Technical aspects Gene: evidence Technically complete assay Medical Exome

8 THE MEDICAL EXOME PROJECT 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

9 just "because three is better than one CHOP Emory Harvard LMM

10 The mendeliome : 4,600 genes Genome Exome Medical Exome v1 Exome ~1% of genome 22,000 genes ~4,600 genes implicated in disease 1998 genes w/pseudogenes ~1% genome 22,000 genes Enhanced coverage of 3,654 genes curated to be medically relevant Intronic mutations Medical Exome V1 Mitochondrial genome Genome ~complete genome with gaps

11 THE MEDICAL EXOME PROJECT STANDARDIZE MEDICAL EXOME SEQUENCING Develop a medically enhanced whole exome assay Develop complementary ancillary assays (pseudogenes) Determine performance metrics of the assay through intra- and inter-laboratory validation tests Define a framework for iterative curation of genes Support and integrate with existing efforts such as ClinGen, ClinVar, ICCG

12 ADDITIONAL CONTENTS OF ENHANCED EXOME CAPTURE ASSAY Ancestry Informative Marker panel* Able to differentiate main continental groups 0.4 Identify Informative Marker panel* 93 SNPs Match probability = 1 / Africa Asia Europe IndMex (J. Fagerness, R. Siburian) (J. Fagerness, R. Siburian) GWAS hits OR>5 n= 178; NHGRI GWAS catalog PGx targets Restricted to those with high evidence (PharmGKB)

13 TECHNICAL ENHANCEMENT

14 WHY THE BRICK WALL? MANY EXOME ASSAYS HAVE HOLES Cardiomyopathy Panel (51 genes) How much is covered at greater than 20x Targeted Capture Panel Exome (Agilent v6)

15 FILLING THE HOLES 2 types of performance issues (Agilent V6 exome kit) Exon not covered in design (no baits) Exon covered but performance suboptimal Worked with Agilent to create Version6-PLUS called the EmExome v2 ~3 Mb novel baits ~4 Mb boosted regions

16 BASE LINE EXOME DATA Whole Exome v6 (all genes) Medically Relevant Genes v6 11% of exons not covered ~10% of exons had a region not covered at greater than 20x

17 From v1 to v2 : Medical Exome v1 to Medical Exome v2 Progression in Design Medical Exome v1 (CREv1) 3654 genes curated to be medically relevant Medical Exome v2 (CREv2) 4753 genes curated to be medically relevant 475 genes removed from enhanced coverage from CREv1 to CREv2 but remain targeted by v remained in common from CREv1 to CREv2 Added curated deep intronic and promoter variants Breakpoint spanning baits for common events by precise mapping of breakpoints Non-coding splice sites OneSeq + CREv2 CREv CREv2 4753

18 Keeping Current on Curation Intensive process of reviewing the new literature Critical evaluation Example of deep intronic variant not in HGMD or LOVD Keeping an open door with clinician scientists

19 % of Exons Fully Covered MEDICAL EXOME v6 PLUS: UP TO 98% COVERAGE OF MEDICALLY RELEVANT HISEQ 2500 rapid ; 4-5 samples/lane GENES ~100X Coverage Across Samples

20 TOWARDS REPLACING TARGETED PANELS WITH EXOME SEQUENCING Cardiomyopathy Panel Targeted Capture Panel Medical Exome v2 (CREv2) = 99% of exons fully covered 99% of exons fully covered

21 WGS to EmExome v2: Coverage Comparison

22 ADDING CNV DETECTION USING AGILENT ONESEQ

23 Whole genome sequencing can find it all but at what cost? Cost of sequencing has defied Moore s law BUT. 1. $1000 genome only accessible with population-scale sequencer 2. And only if sequencer is run at full capacity WGS can cost up to $3,500 on a high throughput sequencer* -cost increase with increased depth Each WGS dataset (30x) is GB Enormous IT infrastructure needed to support data processing and storage Hidden cost of whole genome sequencing For Research Use Only.. Not for use in diagnostic procedures.

24 Knowing the Limitations Next Gen Sanger Single gene Sequencing panels Exome Genome Will not detect large deletions/duplications, repeat disorders, deep intronic variants NGS-based testing will have areas of low coverage

25 CNV Analysis from NGS Data - Benefits Detection of CNVs adds clinical value Can assay entire exome Other methods are reliable (qpcr, MPLA) but not scalable to a whole exome Retterer et al., Genet Med Dec 3. [epub ahead of print]; Poultney et al., Am J Hum Genet Oct 3;93(4): ; Fromer et al., Am J Hum Genet Oct 5;91(4):

26 Can whole exome sequencing detect CNVs? Detecting CNVs using read depth CNV Read Depth Read depth from WES is highly variable Duplication 1. Efficiency of target enrichment baits 2. Sequencing biases 3. Mappability 200+ computational methods to detect CNVs from whole exome sequencing No consensus on which method works best Same WES dataset analyzed by 5 different CNV yielded 10s-100s CNVs (Tan et al., 2014) For Research Use Only.. Not for use in diagnostic procedures.

27 Whole exome sequencing cannot detect non-coding CNVs Not all CNVs associated with constitutional diseases occur in genes Deletion of an intergenic regions upstream of SOX9 and NROB1 genes associated with male-to-female sex reversal in 46 X,Y individuals (Vetro et al., 2014, Smyk et al., 2007) Large duplication upstream of PMP22 associated with CMT1 neuropathy (Zhang et al., 2010, Weterman et al., 2010) Duplication downstream of PLP1 gene associated with spastic paraplegia and axonal neuropathy (Lee et al., 2006) Numerous more examples of CNVs in non-coding region can be found in the literature These CNVs would not be detected by WES! For Research Use Only.. Not for use in diagnostic procedures.

28 More information with just one capture Agilent OneSeq Target Enrichment: CNV, LOH, SNPs and indels in one assay Copy number and LOH determination Gene targets = SNPs and indels OneSeq CNV Backbone Focused Exome targets OneSeq Constitutional Research Panel All in one capture!

29 OneSeq Target Enrichment: One Assay, All Variants Med Exome associated regions Gene A Med Exome associated regions Gene B 1) Evenly spaced genome-wide baits 2) High density baits in Med Exome disease associated regions Copy number & LOH 3) User-defined baits in exonic regions SNVs & indels For Research Use Only.. Not for use in diagnostic procedures.

30 OneSeq is optimized for robust copy number calling OneSeq CNV backbone baits: Targeted to regions optimal GC content Best mappability Empirically validated Read depth from WES Read depth from OneSeq CNV backbone Dispersion Coefficient of variation = 65.8% Coefficient of variation = 36.1% For Research Use Only.. Not for use in diagnostic procedures.

31 OneSeq- Detecting Genomic CNV Genomic CNV EGL size OneSeq size Terminal deletion 21q 309 kb 272 kb Intragenic TCOF1 duplication 34.7 kb 34.9 kb Xq21 duplication 165 kb 181 kb Unbalanced translocation: 6p loss 5.89 Mb 5.73 Mb Chromothripsis: 7q21 loss 1.0 Mb 767 kb Chromothripsis: 11q14 loss 2.44 Mb 2.02 Mb Unbalanced translocation: 10q loss 1.49 Mb 1.29 Mb Unbalanced translocation: 17q gain 4.17 Mb 4.34 Mb

32 OneSeq 35-kb TCOF1duplication 309-kb 21q terminal deletion

33 Gene-specific Mutation Spectrum Familial Adenomatous Polyposis (APC) 80% point mutations 20% deletions/duplications RETT Syndrome (MECP2) 80% point mutations 20% deletions/duplications Point mutations Deletions/Duplications Cystic Fibrosis (CFTR) 90% point mutations 10% deletions/duplications Nuclear receptor binding SET domain protein 1 (NSD1) 75% point mutations 25% deletions/duplications Duchenne Muscular Dystrophy (DMD) 65% deletions/duplications 35% point mutations

34 Gene-specific Mutation Spectrum Familial Adenomatous Polyposis (APC) RETT Syndrome (MECP2) 80% point mutations 80% point mutations DMD gene : 65% del/dup 20% deletions/duplications 20% deletions/duplications Common intragenic deletion and duplications: GALC, FKTN Most known genes: 10-30% del/dup Point mutations Deep intronic/promoter/splice site targets Deletions/Duplications Common large events Cystic Fibrosis (CFTR) 90% point mutations Newly identified genes??? 10% deletions/duplications Nuclear receptor binding SET domain protein 1 (NSD1) 75% point mutations 25% deletions/duplications Duchenne Muscular Dystrophy (DMD) 65% deletions/duplications 35% point mutations

35 AJ Pathogenic Changes Disease ABCC8-Related Hyperinsulinism Bloom Syndrome Canavan Disease Cystic Fibrosis Familial Dysautonomia Fanconi Anemia Type C Gaucher Disease Glycogen Storage Disease Type 1a Joubert Syndrome Type 2 MSUD MSUD Type 3 (DLD) Mucolipidosis Type IV Nemaline Myopathy Niemann-Pick Disease Type A Tay-Sachs Disease Usher Syndrome Type IF Usher Syndrome Type III Mutations p.f1388del, c g>a (IVS32-9G>A), p.v187d c.2281del6ins7, c.2407inst c.433-2a>g, p.y231x, p.e285a, p.a305e 39 mutations c t>c (IVS20+6T>C), p.r696p c.322delg, IVS4+4A>T, p.q13x, p.r548x c.84gg, del55bp, IVS2+1G>A, p.n370, p.v394l, p.d409h, p.d409v, p.l444p, p.r463c, p.r463h, p.r496h c.79delc, c.378dupta, c.979del3, c.648g>t, p.r83c, p.r83h, p.g188r, p.e242x, p.g270v, p.q347x p.r12l p.r183p, p.g278s, p.e372x c.105insa (p.y35x), p.g229c Exon 1-exon 7del (Del6.4kb), IVS3-2A>G p.r2478_d2512del fsp330, p.r610del, p.l302p, p.t324i, p.h423y, p.r476w, p.r496l c.1278instatc, del7.6kb, G>C, IVS7+1G>A, IVS9+1G>A, p.r178h, p.r247w, p.r249w, p.g269s p.r245x c.459del3, p.n48k, p.l150p

36 Strategy for detection of large indels

37 NEXT STEPS TO ADDRESS LIMITATIONS OF NGS

38 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 ACMG Incidental gene list: PMS2, SMN Courtesy: Diana Mandelker

39 The Mendeliome: ~4,700 genes OneSeq Medical exome V2 Medical Exome V1 Exome Exome ~1% of genome 22,000 genes 4600 clinically relevant 1998 genes w/pseudogene(s) Medical Medical Exome Exome V2 V1 ~1% ~1% genome 22,000 genes Enhanced coverage of of ~4,700 4,600 clinically relevant genes Deep intronic mutations Mitochondrial genome Structural variants (including known targeted CNVs (eg 30kb GALC del) microrna OneSeq +Medical Exome V2 ~1% genome 22,000 genes Genome Enhanced coverage of ~4,700 clinically relevant genes Deep intronic mutations Mitochondrial genome Structural variants (including known targeted CNVs (eg 30kb GALC del) microrna PLUS copy number! Genome ~complete genome with gaps