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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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | CONFIDENTIAL BRCA1: c.1175_1214del40 53 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | CONFIDENTIAL BRCA2: c.9203del126 54 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | CONFIDENTIAL BBS9 exon 4 Alu insertion 55 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | CONFIDENTIAL SMAD4 – Any Guesses? 56 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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 7/2/2014 | Copyright © InVitae, Inc. All Rights Reserved | 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. 72 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 73 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 76 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