Post on 15-Jul-2015
Supporting Genomics in the Practice of Medicine
Heidi L. Rehm, PhD, FACMGDirector, Laboratory for Molecular Medicine, Partners Personalized Medicine
Associate Professor of Pathology, Brigham & Women’s Hospital and Harvard Medical School
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Questions
MI, 49y MI, 70y
39y
d. SCD, 7y6y 3y
38y
Case Presentation
40y
6 y
71y 68y
36y
Normal
HCM Family
Legend:
= Affected individuals
d. SCD, 49y d. SCD, 70y
39y
d. SCD, 7y6y6y 3y
38yLVHArrhythmia
SCD = Sudden Cardiac Death
LVH = Left Ventricular Hypertrophy
40y
Normal Echo
Normal Echo
Normal Echo
Normal Echo
40y
71y 68y
36y
HCM Family
Legend:
= Affected Individuals
+ = E187Q positive genotype- = E187Q negative genotype
d. SCD, 49y d. SCD, 70y
39y
d. SCD, 7y 7y
Normal ECHO
6y 3y
SCD = Sudden Cardiac Death
LVH = Left Ventricular Hypertrophy +
+- -
- 38yLVHArrhythmia
Glu187GlnTPM1
Pan Cardiomyopathy Test51 genes
Inconclusive
Positive 32%
Negative53%
15%
Copyright 2010 – Partners HealthCare System, Inc. – All Rights Reserved
When should exome/genome sequencing be used in the diagnostic work-up?
How can we increase the rate of success?
Diagnostic Testing for Rare Disease
Baylor exome experience:~25% success
Case: Nonsyndromic Hearing Loss
Congenital bilateral sensorineural hearing loss
Why should we perform genetic testing in children with hearing loss?
10-20% of kids will develop additional clinical features of syndromes later in life (longQT, retinitis pigmentosa, hypothyroidism, infertility, renal failure, etc)
Copyright 2010 – Partners HealthCare System, Inc. – All Rights Reserved
Option #1OtoGenome Test (70 genes) $3900
Clinical Sensitivity for HL = ~30%
Option #2Exome/Genome Sequencing (22,000 genes) $7000-9000
Testing Options for Hearing Loss
Case: Nonsyndromic Hearing Loss
• ~4 million sequence variants per child
• ~1,250,000 shared variants among the three siblings
• Spent 9 months investigating possible etiologies
• Ultimately pursued linkage analysis
WGS on 3 children
Matt Lebo
Copyright 2010 – Partners HealthCare System, Inc. – All Rights ReservedD3S1278 to D3S2453 = chr3:115,124,154-136,278,257 (3q13.31-22.3, 21 Mb)
Jun Shen
Some genes fail analysis by genome/exome sequencing
2 2 1 24
16
47
0
5
10
15
20
25
30
35
40
45
50
0% 1-24% 25-49% 50-74% 75-89% 90-98% >98%
STRC
Exome Coverage of 73 Hearing Loss Genes
Analyzed case by OtoGenome Test
STRC pSTRC
STRC pSTRC
Hom deletion of STRC
pSTRC
pSTRCSTRC Gene
Sami Amr
100 kb deletion(43.89 Mb to 43.99 Mb)
STRCPseudogene
STRC
100,000 Base Deletion Identified
Case: Deafness Infertility Syndrome
Males with this deletion will be infertile due to deletion of the adjacent CATSPER2 gene
Males can father children through intracytoplasmic sperm injection (ICSI)
Detection of full and partial gene deletions through targeted NGS: VisCap
Log2
ratio
sam
ple/
batc
h m
edia
nUSH2A heterozygous
exon 10 deletion
All exons, sorted by genome position USH2A exons (3’→5’)
OTOF deletion47 exons
Trevor Pugh
However, deletion analysis is not as robust in exome sequencing
IMPROVING EXOME SEQUENCING
COLLABORATIONo Harvard/Partners Lab for Molecular Medicine – Birgit Funkeo Children’s Hospital of Philadelphia – Avni Santanio Emory Genetics Laboratory –Madhuri Hegde
GOALSo Define medically relevant genes + develop framework for iterative curation
o Develop a “medically enhanced exome” capture kit (better coverage)
o Develop ancillary assays for genes that cannot be sequenced via NGS
PROGRESS o ~ 4600 genes designated as version 1 – available on ICCG/ClinGen website
(www.iccg.org)o Improved exome capture kit with optimized coverage of these genes ‐ available from
Agilent
HISEQ 2500 rapid ; 4 samples/lane
Medical Exome4,631 genes
10.7 Mb
Pan Cardio Pnl51 genes
262 kb
fully covered exons (100% ≥ 20x)
Agilent v5-PLUS (~200x)
Broad CRSP ICE(~200x)
fully covered exons (100% ≥ 20x)
94% 98%
88% 99%
Birgit Funke
Improved Coverage with Medical Exome Enhancement
In summary……
Targeted gene panels are recommended when:
• clinical sensitivity is high AND• panel cost is lower than exome
OR• exon level copy number changes are common and
detection is included in panels
Exome suggested when critical genes are well-covered on exome, cost/sensitivity tradeoff makes sense and CNV detection is addressed as needed
Case #3: Distal Arthrogryposis Type 5
Disease is known to be AD and to occur de novo
No known genes for DA5
Skeletal Spine stiffness, Hunched anteverted shoulders, Pectus excavatum, Limited forearm rotation and wrist extension, Bilateral club feet, Congenital finger contractures, Long fingers, Absent phalangeal creases, Poorly formed palmar creases, Camptodactyly, Dimples over large joints
Muscle Decreased muscle mass (especially in lower limbs), Firm muscles Face Triangular face, Decreased facial expression Ears Prominent ears Eyes Ophthalmoplegia, Deep-set eyes, Epicanthal folds, Ptosis, Duane anomaly, Keratoglobus,
Keratoconus, Macular retinal folds, Strabismus, Astigmatism, Abnormal electroretinogram, Abnormal retinal pigmentation
Clinical features:
Case from Michael Murray, MD
Case: Distal Arthrogryposis Type 5
Two de novo mutations in exonic sequence:
ACSM4 – acyl-CoA synthetase medium-chain family member 45 nonsense variants identified in ESP; 1 with 6.4% MAF;
PIEZO2: mechanosensitive ion channel
Great candidate, but how to we prove causality for a novel gene-disease association?
Shamil Sunyeav
Then came serendipity……
Second DA5 family with PIEZO2 mutation was found
BertrandCoste
Matchmaker Needed!
Patient #1Clinical Geneticist #1
Patient #2Clinical Geneticist #2
Genotypic DataGene AGene BGene CGene DGene EGene F
Phenotypic Data
Feature 1Feature 2Feature 3Feature 4Feature 5
GenotypicData
Gene DGene GGene H
Phenotypic Data
Feature 1Feature 3Feature 4Feature 5 Feature 6
GenomicMatchmaker
Notificationof
Match
Courtesy of Joel Krier
Joel Krier
Multiple disconnected
solutions
PhenoDBGene
Matcher
DECIPHER
LOVD
Café Variome
Undiag. Diseases Program
PhenomeCentral
GEM.app
ClinVar&
ClinGenDB
Matchmaker Exchange is a Driver Project for the Global Alliance Success highly dependent on large international effort Critical need for standards Activity spans multiple workgroups
1. Clinical (phenotyping and matching algorithms)2. Data (data format and interfaces)3. Security (patient privacy)4. Regulatory and Ethics (patient consent)
180 organizations from 25 countries so far……
Multiple disconnected
solutionsMatchmaker Exchange
Gene Matcher
DECIPHER
LOVD
Café Variome
Undiag. Diseases Program
PhenomeCentral
GEM.app
ClinGenDB
API V1.0
A New Paradigm in Clinical Genomics
Patient/Provider
ResearcherClinical Lab
Research Discoveries Clinical Lab Patient
Care
Traditional Paradigm
New Paradigm
Inherited Cancer DisordersHereditary Breast and Ovarian CancerLi‐Fraumeni SyndromePeutz‐Jeghers SyndromeLynch Syndrome, FAP, MYH‐Associated PolyposisVon Hippel Lindau syndromeMultiple Endocrine Neoplasia Types 1 & 2Familial Medullary Thyroid Cancer (FMTC)PTEN Hamartoma Tumor SyndromeRetinoblastomaHereditary Paraganglioma‐Pheochromocytoma SyndromeWT1‐related Wilms tumorNeurofibromatosis type 2Tuberous Sclerosis Complex
Cardiac DisordersEhlers Danlos Syndrome ‐ vascular typeMarfan Syndrome, Loeys‐Dietz Syndromes, and Familial Thoracic Aortic AneurysmsHypertrophic, Dilated, and ARV cardiomyopathyCatecholaminergic polymorphic ventricular tachycardiaRomano‐Ward Long QT Syndromes Types 1, 2, and 3 and Brugada Syndrome Familial hypercholesterolemia
Other: Malignant hyperthermia susceptibility
56 Genes
Defining the Low and High Bars for RORin the Clinical Setting………
Return all clinically valid IFs
Return all clinically actionable IFs (disease, carrier, PGx , etc)
Allow opt out of all IFs?
Return candidate genes in clinical exome/genome?Return raw reads/vcf?
Return certain clinically actionable IFs (ACMG list?)
100 Healthy Patients(10 PCPs)
100 HCM Patients(10 cardiologists)
Cardiac Risk
Supplement
Genome Report
50 50
Project 2 Workflow
Whole Genome Sequencing
MedSeq WGS Pilot Clinical Trial
Standard of CarewithFamily History
50 50
Standard of CarewithFamily History
CardiacRisk
Supplement
Genome Report
Compare Outcomes Compare Outcomes
RobertGreen
The Whole Genome Report
Monogenic disease risk
Carrier risk
Pharmacogenomics
Blood type
49 Mendelian Variants returned in first 20 MedSeq cases
Carrier Status
40 variants
5 Dx4 Cases at Risk
Cardiomyopathy Cohort
Hypertrophic cardiomyopathy x 3 cases with confirmed results
Hypertrophic cardiomyopathy x 1 case – found missed mutation from research NGS
LEOPARD syndrome – case misdiagnosed with HCM
Healthy Cohort
Chondrodysplasia punctata
Long QT
Combined pituitary hormone deficiency
Variegate porphyria
Review of Published Pathogenic Variants Found in WGS
3‐5 million variants
~20,000 Coding/Splice Variants
20‐40 “Pathogenic” Variants
Published as Disease‐Causing
Genes
<1%
Rare CDS/Splice Variants
LOF in Disease Associated Genes
10‐20 Variants
Review evidence for gene-disease association and LOF role
Review evidence for variant pathogenicity
92% Excluded
67% Excluded
Acknowledgements:Heather McLaughlinKalotina MachiniOzge Ceyhan BirsoyMatt LeboDanielle Metterville
Weak disease association
65%
Not medically relevant
33%
Somatic 2%
MedSeq Project:PI: Robert Green
The Problem
> 50 million genomic variants in humans
>20,000 genes
Most we don’t understand
0
200
400
600
800
1000
1200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
0
2
4
6
8
10
12
14
16
18
20
Lung CancerKRAS EGFR G12C L858R
GJB235delG
GJB2M34T PTPN11
N308D
MYBPC3R502W
68% (1120/1648) percent of pathogenic/likely pathogenic variants are seen only once
96% of variants are seen <10 times
Number of Probands
Num
ber o
f Var
iant
sHistogram of Pathogenic Variants from Diagnostic Testing of 15,000 Probands
(cardiomyopathy, hearing loss, rasopathies, aortopathies, somatic and hereditary cancerpulmonary disorders, skin disorders, other genetic syndromes)
31%VUS
25%Positive
61% Negative 14%
Inconclusivenclusive
52% Benign
17%Path
BabySeq: Genome Sequence-Based Screening for Childhood Risk and Newborn Illness
Sequence
Healthy Newborns
Sick Newborns
Symptoms
Additional Related
Symptoms
New symptoms
STOP
Indication-Based Genomic Report 1
Updated Indication-Based Genomic Report 1
Indication-Based Genomic Report 2 Symptoms
Query
Indication-Based Genomic Report
Query
Genomic Newborn Screening Report
Consult GRC and
Laboratory
Consult GRC and
Laboratory
Alan Beggs/Robert Green (PIs)P. Park, H. Rehm, T. Yu, P. Agrawal, R. Parad, I. Holm, A. McGuire (co-PIs)
Fetus with US finding: ↑NT
PTPN11 p.Ile309ValPublished as “pathogenic” for
Noonan syndrome
Patient contacted author of paper who said he later found the variant in 7% of AJ controls; now feels the variant is benign
Courtesy Sherri Bale
Noonan Syndrome Case
?
LMMCase
To improve our knowledge of DNA variation will require a massive effort in data sharing
Clinical Domain WGsChairs: Jonathan Berg &
Sharon PlonCancer co‐chairs:
Matthew Ferber, Ken Offit, Sharon PlonCardiovascular co‐chairs: Euan Ashley, Birgit Funke, Ray Hershberger
Metabolic co‐chairs: Rong Mao, Robert Steiner, David Valle
Pharmacogenomic co‐chairs: Teri Klein, Howard McLeod
ClinGen Working Groups (WG)
Actionability WG
Chair: Jim Evans
Informatics WG
Chair: Carlos Bustamante
EHR WG
Chair: Marc Williams
ClinVar IT Standards and Data Submission
WG
Chairs: Sandy Aronson & Karen Eilbeck
Gene Curation WG
Chairs: Jonathan Berg & Christa Martin
Sequence Variant WG
Chairs: Sherri Bale & Madhuri Hegde
Structural Variant WG
Chairs: SwaroopArahdya & Erik
Thorland ELSI and Genetic Counseling WG
Chair: Andy Faucett & Kelly Ormond
Education, Engagement, Access
WG
Chair: Andy Faucett
Phenotyping WG
Chair: David Miller
ClinGenThe Clinical Genome Resource
LaunchedSept 2013
NCBI ClinVar LeadsMelissa LandrumDonna MaglottSteve Sherry
U41 Grant PIsDavid LedbetterChrista MartinBob NussbaumHeidi Rehm
U01 PIsJonathan BergJim Evans
David LedbetterMike Watson
U01 PIsCarlos Bustamante
Sharon Plon
NHGRI Program DirectorsLisa BrooksErin Ramos
Data Model WG
Chairs: Jonathan Berg & Heidi Rehm
Goals of ClinGenTo raise the quality of patient care by:
• Standardizing the annotation and interpretation of genomic variants
• Sharing variant and case level data through a centralized database for clinical and research use
• Developing machine‐learning algorithms to improve the throughput of variant interpretation
• Implementing an evidence‐based expert consensus process for curating genes and variants
• Assessing the actionability of genes and variants and supporting their use in clinical care systems
Rating System for Gene DosageHighest -- 3, 2, 1, 0, unlikely dosage sensitive -- Lowest
ACMG Lab QA Committee on theInterpretation of Sequence VariantsACMGSue Richards (chair), Heidi Rehm (co-chair)Sherri Bale, David Bick, Soma Das, Wayne Grody, Madhuri Hegde, Elaine Spector
AMPJulie Gastier-Foster, Elaine Lyon
CAPNazneen Aziz, Karl Voelkerding
42
PopulationData
Computational Data
Segregation Data
Other Database
Prevalence in affecteds statistically increased over controls
MAF frequency is too high for disorderOR observation in controls inconsistent with disease penetrance6
Truncating variant in a gene where LOF is a known mechanism of disease1
De novo (paternity & maternity confirmed)3
Well‐established functional studies show a deleterious effect 4
Novel missense change at an amino acid residue where a different pathogenic missense change has been seen before2
Multiple lines of computational evidence support a deleterious effect on the gene /gene product 9
De novo (without paternity & maternity confirmed)3
Non‐segregation with disease5
Patient’s phenotype or FH matches gene
For recessive disorders, detected in trans with a pathogenic variant11
Found in case with an alternate cause
Type of variant does not fit known mechanism of disease
Multiple lines of computational evidence suggest no impact on gene /gene product9
Well‐established functional studies show no deleterious effect4
Located in a mutational hot spotand/or known functional domain7
In‐frame indels in a repetitive region without a known function7
Same amino acid change as an established pathogenic variant2
In‐frame indels in a non‐repeat region
Stop‐loss variants12
Dominants: Observed in trans with a pathogenic variant 11
Functional Data
Co‐segregation with disease in multiple affecteds in multiple families5
Co‐segregation with disease in multiple affected family members5
De novo Data
Allelic Data
Absent in 1000G and EVS
Strong
Observed in cis with a pathogenic variant Reputable database = benign
Strong Very StrongModerateSupporting Supporting
Reputable database = pathogenic
Missense in gene with low rate of benign missense variation and pathogenicmissenses common
Other Data
Benign Pathogenic
The Scoring Rules for Classification
Pathogenic 1 Very Strong AND
1 Strong OR≥2 (Moderate OR Supporting)
2 Strong 1 Strong AND
≥3 Moderate OR≥2 Moderate and 2 Supporting OR≥1 Moderate and 4 Supporting
Likely Pathogenic1 Very strong or Strong AND
≥1 Moderate OR ≥2 Supporting
≥3 Moderate ≥2 Moderate AND 2 Supporting ≥1 Moderate AND 4 Supporting
Very Strong: PVS1Strong: PS1-PS4Moderate: PM1-PM6Supporting: PP1-PP5Stand-Alone: BA1Strong: BS1-BS4Supporting: BP1-BP6
Benign1 Stand Alone OR≥ 2 Strong
Likely Benign1 Strong and ≥1 Supporting OR>2 Supporting
Uncertain SignificanceIf other criteria are unmet or arguments for benign and pathogenic are equal in strength
Public LSDBs>600
PharmGKB
PopulationDatabases
EVS1000GdbSNP
MedicalLiterature
Clinical LabDatabases
OMIM
Variant Databases
COSMIC
HGMD$$$
Research Lab Databases
Largely absent from the public domain
Largely without standardized
assertions
Need genomic data and phenotypes/outcomes to objectively inform our knowledge of human variation
www.ncbi.nlm.nih.gov/clinvar
Submitter Variants GenesClinical LabsHarvard Medical School and Partners Healthcare 6996 155Emory Genetics Laboratory 5252 507Ambry Genetics 4167 ?International Standards For Cytogenomic Arrays 4134 17711GeneDx 3700 250University of Chicago 3687 462Sharing Clinical Reports Project 2045 2ARUP Laboratories 1417 7LabCorp 1391 140InVitae 436Counsyl 112 20University Pennsylvania Genetic Diagnostic Lab 68 1American College of Med Genetics and Genomics 23 1
26459
General DatabasesOMIM 24443 3360GeneReviews 3738 406
28181LSDB/Researcher – Assertions SubmittedBreast Cancer Information Core (BIC) 3793 2InSiGHT 2360 4Juha Muilu Group; FIMM, Finland (FIMM) 840 39ClinSeq Project 425 35Martin Pollak (Nephrology, BIDMC, Harvard) 234 39CFTR2 133 1
7785LSDB/Researcher – No Assertions111 Submitters 50063 >6957
ClinVar120,830 submissions107,098 unique variants
50,063 variants without assertions from 111 submitters
62,425 variants with assertions from >3360 genes
ClinGenDB
Data Flows in ClinGen
ExpertCuratedVariants
Case-level Data
Variant-level DataClinVar
Data
Locus‐Specific Databases
Clinical Labs Clinics Patients
Sharing Clinical Reports Project
Curation Interface
Free‐the‐Data Campaign
Patient Registries
Researchers
Unpublished or Literature Citations
InSiGHT
CFTR2PharmGKB
The Sharing Clinical Reports Project and Free‐the‐Data Campaign for BRCA1 and BRCA2
Goal: Improve the care and safety of patients through data sharing
Method: Request clinical lab reports from clinics and patients
Status: >60 clinics and > 200 patients have submitted de-identified reports leading to 4278 variants collected
sharingclinicalreports.org
Acknowledgements: Bob Nussbaum (UCSF)Danielle Metterville (ICCG)Laura SwaminathanGeorge Riley (NCBI)Larry Brody (BIC)Sharon Terry (Genetic Alliance) Genetic Alliance Staff and SC
www.free‐the‐data.org
Public BRCA1/2 Variants
5712 unique variants in ClinVar
GeneDx, Counsyl and ENIGMA submissions being processed
Global Alliance BRCA ChallengeLOVD: 3262 variantsUniversal Mutation Database: 3913 variantsBRCA Circos DatabaseCOGR Database (Canada) UK database
The Scoring Rules for ClassificationPathogenic
1 Very Strong AND1 Strong OR≥2 (Moderate OR Supporting)
2 Strong 1 Strong AND
≥3 Moderate OR≥2 Moderate and 2 Supporting OR≥1 Moderate and 4 Supporting
Likely Pathogenic1 Very strong or Strong AND
≥1 Moderate OR ≥2 Supporting
≥3 Moderate ≥2 Moderate AND 2 Supporting ≥1 Moderate AND 4 Supporting
Very Strong: PVS1Strong: PS1-PS4Moderate: PM1-PM6Supporting: PP1-PP5Stand-Alone: BA1Strong: BS1-BS4Supporting: BP1-BP6
Benign1 Stand Alone OR≥ 2 Strong
Likely Benign1 Strong and ≥1 Supporting OR>2 Supporting
Uncertain SignificanceIf other criteria are unmet or arguments for benign and pathogenic are equal in strength
Expert Panel
Single-Source
1. Literature references without assertions2. Inconsistency in assertions
Multi-Source Consistency
Practice Guideline
ClinVar Review Levels
Mendelian Categories:PathogenicLikely pathogenicUncertain significanceLikely benignBenign (InSiGHT and CFTR2)
(e.g. 23 CF)
No stars
Summary Assertions in ClinVar
Clinical Assertions
ClinVar Evidence Tab
ClinVar Expert Panel Designation (3 stars)
• Download submission form on ClinVar website• Panel should include multiple institutions and expertise
– medical specialists in disease area– medical geneticists– clinical laboratory diagnosticians/ molecular pathologists – researchers relevant to the disease, gene, functional assays and statistical analyses
• Process for COI review and updating assertions• Publications or links that describe annotation process• Information provided is reviewed by ClinGen Executive Committee and posted on ClinVar w/designation
Expert Panel
Single-SourceEvidence-Based Review Method Provided
1. Literature references without assertions2. Inconsistency in assertions
Multi-Source ConsistencyEvidence-Based Review Methods Provided
Practice Guideline
New Idea for ClinVar Review Levels
Mendelian Categories:PathogenicLikely pathogenicUncertain significanceLikely benignBenign (InSiGHT and CFTR2)
(e.g. 23 CF)
No stars
Single-SourceNo Method Provided
?
Proposed Access Level Requirementsand Data Types
Clinical Labs
Lab 1
Lab 2
Variants reassessed
by Lab 140 variants
still discrepant
40 variants consistent
Variants reassessed
by Lab 2
10 variants consistent
Clinical Experts
Committee Review
Expert Committee Review• Discuss classification rules• Review discrepant variants with
input from experts in that disease and assign classification
80 variants discrepant
50 variants consistent
• Rule Differences• Silent (VUS vs LB)• Differences in frequency cut-offs
• Reporting differences influence stringency!• Lab 1 excludes Lik Ben, Lab 2 includes• Greater willingness of Lab 1 to classify as
Lik Ben!• Other (use of computational data)
• 1/80 variants needs expert input • atypical GLA/Fabry variant
30 variants still discrepant
Info disseminated back to labs
Feedback to Committee
Courtesy of Birgit Funke
Pass on what
needs expert input
CONF. CALL
Lab 1+2 review • Discrepancies• Rules
VARIANT HARMONIZATION (LMM – EMORY GENETICS LAB)
ClinGenDBCuration Tool
Expert Curation of Genes and Variants by Clinical Workgroups
Gene Resource
ExpertCuratedVariants
Case-level Data
Variant-level DataClinVar
Disease WGs
Clinical Domain WGs
Data
Machine Learning Algorithms
Locus‐Specific Databases
Clinical Labs
PharmGKB CFTR2
QC report
InSiGHT
Disease-Targeted NGS Tests on the Market
Disease area GenesCancerHereditary cancers (e.g. breast, colon, ovarian) 10‐50
Cardiac diseasesCardiomyopathies 50‐70Arrhythmias (e.g. LongQT) 10‐30Aortopathies (e.g. Marfan) 10
Immune disordersSevere combined immunodeficiency syndrome 18Periodic fever 7
Neurological/Neuromuscular/MetabolicAtaxia 40Cellular Energetics/Metabolism 656Congenital disorders of glycosylation 23‐28Dementia (e.g. Parkinson, Alzheimer) 32Developmental Delay/Autism/ID 30‐150Epilepsy 53‐130Hereditary neuropathy 34Microcephaly 11Mitochondrial disorders 37‐450Muscular dystrophy 12‐45
SensoryEye disease (e.g. retinitis pigmentosa) 66‐140Hearing loss and related syndromes 23‐72
OtherRasopathies (e.g. Noonan) 10Pulmonary disorders (e.g. cystic fibrosis) 12‐40Ciliopathies 94Short stature 12
Only 63% (92/145) of genes in clinical hearing loss tests have sufficient evidence for a disease-association
AGMG NGS Guideline
ACMG (www.acmg.net) > Publications > Laboratory Standards and Guidelines > NGS
Evaluating Evidence for Gene‐Disease Associations
Definitive evidenceStrong evidenceModerate evidenceLimited evidenceNo evidenceDisputed evidenceEvidence against
Evidence Level Evidence Description
DEFINITIVE The role of this gene in this particular disease has been repeatedly demonstrated in both the research and clinical diagnostic settings, and has been upheld over time (in general, at least 3 years). No valid evidence has emerged that contradicts the role of the gene in the specified disease.
STRONG
There is strong evidence by at least two independent studies to support a causal role for this gene in this disease, such as:Strong statistical evidence demonstrating an excess of pathogenic variants1 in affected individuals as compared to appropriately matched controlsMultiple pathogenic variants1 within the gene in unrelated probands with several different types of supporting experimental data2. The number and type of evidence might vary (eg. fewer variants with stronger supporting data, or more variants with less supporting data)In addition, no valid evidence has emerged that contradicts the role of the gene in the noted disease.
MODERATE
There is moderate evidence to support a causal role for this gene in this disease, such as:At least 3 unrelated probands with pathogenic variants1 within the gene with some supporting experimental data2. The role of this gene in this particular disease may not have been independently reported, but no valid evidence has emerged that contradicts the role of the gene in the noted disease.
LIMITED
There is limited evidence to support a causal role for this gene in this disease, such as:Fewer than three observations of a pathogenic variant1 within the gene Multiple variants reported in unrelated probands but without sufficient evidence for pathogenicity per 2014 ACMG criteria
NO EVIDENCE No evidence reported for a causal role in disease.
DISPUTED Valid evidence of approximate equivalent weight exists both supporting and refuting a role for this gene in this disease.
EVIDENCE AGAINST
Evidence refuting the role of the gene in the specified disease has been reported and significantly outweighs any evidence supporting the role.
Proposed Evidence Required to Include a Gene In a Clinical Test?
Definitive evidenceStrong evidence
Moderate evidence
Limited evidenceDisputed evidence
Definitive evidenceStrong evidence
Moderate evidence
Limited evidenceDisputed evidence
Exome/Genome
Predictive Tests & IFs
Diagnostic Panels
www.iccg.org
clinicalgenome.org
ClinGen AcknowledgementsJonathan BergLisa BrooksCarlos BustamanteJim EvansMelissa LandrumDavid LedbetterDonna MaglottChrista MartinRobert NussbaumSharon PlonErin RamosHeidi RehmSteve SherryMichael WatsonErica AndersonSwaroop ArahdyaSandy AronsonEuan AshleyLarry BabbErin BaldwinSherri BaleLouisa BaroudiLes BieseckerChris BizonDavid BorlandRhonda BrandonMichael BrudnoDamien BrunoAtul ButteHailin ChenMike CherryEugene Clark
Soma DasJohan den DunnenEdwin DodsonKaren EilbeckMarni FalkAndy FaucettXin FengMike FeoloMatthew FerberPenelope FreireBirgit FunkeMonica GiovanniKatrina GoddardRobert GreenMarc GreenblattRobert GreenesAda HamoshBret HealeMadhuri HegdeRay HershbergerLucia HindorffSibel KantarciHutton KearneyMelissa KellyMuin KhouryEric KleePatti KrautscheidJoel KrierDanuta KrotoskiShashi KulkarniMatthew LeboCharles Lee
Jennifer LeeElaine LyonSubha MadhavanTeri ManolioRong MaoDaniel MasysPeter McGarveyDominic McMullanDanielle MettervilleLaura MilkoDavid MillerAleksander MilosavljevicRosario MongeStephen MontgomeryMichael MurrayRakesh NagarajanPreetha NandiTeja NelakuditiElke Norwig‐EastaughBrendon O’FallonKelly OrmondDaniel Pineda‐AlvarazDarlene ReithmaierErin RiggsGeorge RileyPeter RobinsonWendy RubinsteinShawn RynearsonCody SamAvni SantaniNeil SarkarMelissa Savage
Jeffery SchlossCharles SchmittSheri SchullyAlan ScottChad ShawWeronika Sikora‐WohlfieldBethanny Smith PackardTam SneddonSarah SouthMarsha SpeevakJustin StarrenJim StavropoulosGreer StephensChristopher TanPeter Tarczy‐HornochErik ThorlandStuart TinkerDavid ValleSteven Van VoorenMatthew VarugheeseYekaterina VaydylevichLisa VincentKaren WainMeredith WeaverKirk WilhelmsenPatrick WillemsMarc WilliamsEli Williams
Project LeadershipRobert Green, MD, MPHZak Kohane, MD, PhDCalum MacRae, MD, PhDAmy McGuire, JD, PhD Michael Murray, MD Heidi Rehm, PhD Christine Seidman, MDJason Vassy, MD, MPH, SM
Project ManagerDenise Lautenbach, MS, CGC
Project PersonnelSandy Aronson, ALM, MAStewart Alexander, PhDDavid Bates, MD Jennifer Blumenthal-Barby, PhDOzge Ceyhan-Birsoy, PhD Kurt Christensen, MPH, PhDAllison Cirino, MS, CGCLauren Conner Kelly DavisJake Duggan
Project Personnel (Cont.)Lindsay Feuerman, MPHSiva Gowrisankar, PhD Carolyn Ho, MDLeila Jamal,ScM, CGC Peter Kraft, PhDJoel Krier, MD Sek Won Kong, MD William Lane, MD, PhD Matt Lebo, PhDLisa Lehmann, MD, PhD, MScIn-Hee Lee, PhDIgnat Leschiner, PhD Christina LiuPhillip Lupo, PhD, MPHKalotina Machini, PhD, MS David Margulies, MDHeather McLaughlin, PhDDanielle Metterville, MS, CGCRachel Miller Kroouze, MA Sarita PanchangJill Robinson, MAMelody Slashinski, MPH, PhDShamil Sunyaev, PhD Peter Ubel, MD Scott Weiss, MD
External Advisory BoardKatrina Armstrong, MDDavid Bentley, DPhilRobert Cook-Deegan, MDMuin Khoury, MD, PhDBruce Korf, MD, PhD (Chair)Jim Lupski, MD, PhDKathryn Phillips, PhDLisa SalbergMaren Scheuner, MD, MPHSue Siegel, MSSharon Terry, MA
ConsultantsLes Biesecker, MDGeorge Church, PhD Geoffrey Ginsburg, MD, PhDTina Hambuch, PhDJ. Scott Roberts, PhDDavid Veenstra, PharmD, PhD
Protocol Monitoring CommitteeJudy Garber, MD, MPHDavid Miller, MD, PhDCynthia Morton, PhD
The MedSeq Project Collaborators
Matchmaker Exchange AcknowledgementsS BalasubramanianMike BamshadSergio Beltran AgulloJonathan BergKym BoycottAnthony BrookesMichael BrudnoHan BrunnerOriean BuskeDeanna ChurchRaymond DalglieshAndrew DevereauJohan den DunnenHelen FirthPaul Flicek
Jan FriedmanRichard GibbsMarta GirdeaRobert GreenMatt HurlesAda HamoshEkta KhuranaSebastian KohlerJoel KrierOwen LancasterMelissa LandrumPaul LaskoRick LiftonDaniel MacArthurAlex MacKenzie
Danielle MettervilleDebbie NickersonWoong‐Yan ParkJustin PaschallAnthony PhilippakisHeidi RehmPeter RobinsonFrancois SchiettecatteRolf SijmonsNara SobreiraJawahar SwaminathanMorris SwertzRachel ThompsonStephan Zuchner