MITOCHONDRIAL DISEASE SEQUENCE DATA RESOURCE
(MSeqDR) CONSORTIUM:
A Global Grass-Roots Effort to
Compile, Organize, Annotate, and Analyze Whole Exome Datasets
from Individuals with Suspected Mitochondrial Disease
Marni J. Falk, M.D., FACMG
Assistant Professor of Pediatrics
Division of Human Genetics
The Children’s Hospital of Philadelphia
University of Pennsylvania Perelman School of Medicine
Philadelphia, Pennsylvania
falkm@email.chop.edu
DISCLOSURES
Marni J. Falk, M.D. is
• Organizer, Mitochondrial Disease Sequence
Data Resource (MSeqDR) Consortium
– Pilot project development funding from UMDF
and NAMDC (U54, NINDS & NICHD, NIH)
• Chair, Scientific and Medical Advisory Board &
Member, Board of Trustees,
United Mitochondrial Disease Foundation
• Consultant, GlaxoSmithKline
• Consultant, Mitokyne
GENOMIC BASIS OF MITOCHONDRIAL DISEASE
•
Mitochondrial disease is highly heterogeneous in causes and features
‒ Traditional single gene testing has had limited diagnostic success
‒ Newer genomics technologies enable comprehensive and efficient
testing for all known genetic causes in dual genomes
‒ >200 nuclear genes
‒ All 37 mtDNA genes
‒ Diagnose >50% of complex mitochondrial diseases in one test*
‒ Novel disease gene discovery
•
We have entered a computationally sophisticated molecular diagnostic
age for understanding subclasses of mitochondrial disease**:
**Calvo S, Mootha R, Ann Rev Genom Hum Genet, 2010; **McCormick E et al, 2012, Disc Med
MSeqDR Consortium Rationale
• MSeqDR Consortium was initiated at the June 2012 Annual Meeting of the United
Mitochondrial Disease Foundation (UMDF) to create an international source of
genomic information in suspected mito diseases
– Utilize genomic data being generated in clinical and research labs world-wide
• Large-scale sequencing panels of mtDNA genome & multiple nuclear genes
• Whole exome or genome data
– Once initial data analysis is complete, dataset itself remains highly valuable
• Informs allele frequency in >10,000 non-synonymous exome variants per person
– Link to phenotypic and laboratory data
• “Missed” disease-causing mutations might later be identified by other
bioinformatics tools or investigators
• Collective exome analysis in specific subgroups or singe gene disorders may reveal
variants that modify phenotypes or predict response to specific therapies
– Common exome/genome scale data repository and analysis tools are needed
to maximize data utility across the mitochondrial disease community
• Optimize tools for mito disease that integrate with existing genomic databases
MSeqDR Consortium Structure
WORKING GROUP 1: TECHNOLOGY AND BIOINFORMATICS
• WG1 Co-Chairs:
– Marni Falk, MD (CHOP/Upenn), Xiaowu Gai, PhD (Loyola), Curt Scharfe, MD, PhD
(Stanford)
• WG1 Advisors:
– Lisa Brooks, PhD (NHGRI, NIH), Deanna Church, PhD (NCBI, NIH)
WORKING GROUP 2: PHENOTYPING, DATABASING, IRB CONCERNS, SECURITY, AND ACCESS
• WG2 Co-Chairs:
– Patrick Chinnery, MD, PhD (Newcastle), Lee-Jun Wong, PhD (Baylor), and Peter White,
PhD (CHOP/Penn)
• WG2 Advisors:
– Donna Maglott, PhD (NCBI, NIH) and Yaffa Rubinstein, PhD (NCATS, ORDR)
WORKING GROUP 3: MITOCHONDRIAL DNA SPECIFIC CONCERNS
• WG3 Co-Chairs:
– Vincent Procaccio, PhD (Angers) and Douglas Wallace, PhD (CHOP/Upenn)
• WG3 Advisor:
– Richard Cotton, PhD (Melbourne/Human Variome Project)
MSeqDR Prototype Development Project
• Prototype Development Project
– Leaders: Marni Falk, MD (CHOP)
Organization
Xiaowu Gai, PhD (MEEI/Harvard) Bioinformatics Pipeline + Tools
Stephan Zuchner, MD (Miami)
Data Visualization and Mining
• Seeking global input from Mito Disease investigators
• Dedicated MSeqDR bioinformatician – Lishuang Shen, PhD
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Establish MSeqDR website and tools
Exome data file handling and server issues
Reannotate exome data to deposit in data visualization tool(s)
Coordinate comparative analysis project of bioinformatics pipelines
Extract/link phenotypic data from NAMDC or other patient registries
Optimize user-friendly web interfaces to mine exome and mtDNA data
https://mseqdr.org
Xiaowu Gai, PhD, Lishuang Shen, PhD
MSeqDR: Major Components
– Web Portal = MSeqDR.ORG
• Incorporates Wordpress, facilitates community participation, controlled access
• https://mseqdr.org
– MSeqDR G-browse - Gene or variant level analytic support
• graphic user interface for accessing exome variants (public & private) in the
context of other genomic annotations in individual “tracks”
• Variant data sharing: other G-browse instances, UCSC Genome Browser, etc
• mtDNA-specific analysis and interpretation tools
• http://gmod.org/wiki/GBrowse
– BioDAS Server = Aggregate data distribution tool
• ProServer up and running, useful to distribute MSeqDR variant data
• Enable sharing private variant data at various investigator comfort levels
– MSeqDR LSVD – Mitochondrial Disease Locus Specific Database
• >1,300 nuclear genes and mtDNA-genes
– curated information of gene/transcript/variant/phenotype/disease
• http://www.lovd.nl/3.0/home (Leiden University Medical Center)
Providing centralized access to
different genome mining tools
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HBCR = Exome Data Annotation Tool
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Human Basepair Codon Resource for web-based variant annotation
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Custom variant annotation tool developed by Xiaowu Gai, PhD
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Dr. Xiaowu Gai, Massachusetts Eye and Ear Infirmary
GEM.app = Exome-level dataset mining tool
• Genome management application
• Web-based exome analysis of individuals-families-cohorts
• Stephan Zuchner, MD, PhD – University of Miami
– Already “live” with >4,500 exomes from neuromuscular disease
patients/families
– Data archive
• MSeqDR optimized
– common exome reannotation, mtDNA genome mining, CDEs, etc
– Common login for MSeqDR users
– Incorporate Global Universal IDs (GUIDs)
– Will display individual exome data in MSeqDR Gbrowse custom “Tracks”
Providing ready access to different
mtDNA-specific genome mining tools
mtDNA GENOME Analayis & Variant Pathogenicity Assessment Tools
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MitoMAP – MitoMaster - MitoWIKI
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Mito Tool Box / HmtDB
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rCRS-based mtDNA haplogroup analysis
Dr. Mannis Van Oven, The Netherlands
Phy-Mer
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mtDNA Variant Annotation & Prioritization Tool
Dr. Fons Stassen, Maastricht University, The Netherlands
Phylotree mtDNA tree build 16 (19 Feb 2014)
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HmtDB variant analysis, including heteroplasmy and haplogroup calling
Drs. Marcella Attimonelli, Maria Angela Diroma, Italy
MT.AT
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Human mtDNA genome database
Dr. Douglas Wallace, Marie Lott, Jeremy Leipzig at CHOP
Mitochondrial haplogroup classifier (alignment-free; reference-independent)
Daniel Novarro-Gomez, Massachusetts Eye and Ear Infirmary
MitoBreak
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mtDNA breakpoints database
Drs. Filipe Pereira, Joana Damas
MSeqDR G-browse:
Aggregate Data Analysis at the
Gene or Variant Level
MSeqDR Gbrowse: Novel annotation tracks
Community-specific & Custom Tracks
MSeqDR Gbrowse:
mtDNA gene analysis
Visualize
mtDNA
Genome
Variation
User Data
Ensembl Genes
RSRS vs rCRS
ClinVar Variants
Ensembl Variants
MitoMap Variants
HmtDB Variants
PhyloTree Variants
Reference
differences:
rCRS vs. RSRS
Known
pathogenic
variants:
ClinVar, Ensembl,
MitoMap
Predicted
pathogenicity of
mtDNA variants:
HmtDB (rCRS)
Predicted
pathogenicity of
likelihood of
mtDNA variants:
HmtDB (RSRS)
Haplogroup
Defining Variants:
PhyloTree
Visualize
mtDNA
Variants
Linked variant data tables by track
MSeqDR Gbrowse:
Nuclear gene analysis
Nuclear
Genome:
POLG
POLG
Mutation
Database
Transgenomic
Nuclear
Mitome Panel
MSeqDR
Exomes
EVS Exomes
Visualize
Nuclear
Genes
Browse
Nuclear
Gene
Variation
MSeqDR Gbrowse:
Interrogate public & MSeqDR variant data
Nuclear-Mito Data Sets
POLG Mutation Database:
http://tools.niehs.nih.gov/polg/
Dr. William C. Copeland
NuclearMitome – Comprehensive Sequence
Analysis of 448 Nuclear Mitochondrial Genes
MSeqDr Exomes – aggregate data of 1,043
exomes analyzed and shared by Dr. Xiaowu Gai
MitoCarta: an inventory of 1,013 human mito
localized genes – Dr. Vamsi Mootha
GeneDx – 101 Gene Panel, gene content
only currently; variant content coming (?)
Dr. Renkui Bai and Sherry Bale
MitoPhenome – 174 Genes
Dr. Curt Scharfe
Phenome Portal for Mitochondrial Diseases
HPO for Mitochondrial Diseases
MSeqDR-LSDB
Curation and analysis at
gene-transcript-variant-disease levels
in all nuclear & mtDNA
mitochondrial genes
Browse
Search
Genes
Diseases
Variants
MSeqDR-LSDB:
>1,300 nDNA & mtDNA mito genes
Mito-Gene:
POLG
Browse
Gene Data
Integration with
other browsers
Integration with
other resources
MSeqDR-LSDB: Variant Data Curation
Search
Browse Variants
Browse
Individual Variant
Data
ClinVar
Data
Integration
Browse
Diseases
Search
MSeqDR TOOLS
Providing access to public and custom
tools for
individual exome and mtDNA genome
analysis
HBCR: Human BP Codon Resources
Exome dataset
Variant Annotation
Xiaowu Gai, PhD
MT.AT: mtDNA genome variant annotation tool
Fons Stasson, PhD
Genome Center Maastricht
MSeqDR – GEM.app
Stephan Zuchner, MD
University of Miami
MSeqDR exome data annotation pipeline
for individual exome deposition & analysis
Xiaowu Gai, PhD
Comparative WES Data analysis
pilot project underway
• No Gold Standard exists for NGS data preparation or analysis
• Assess relative strengths and weaknesses of existing pipelines
• Determine optimal strategy for common exome data analysis
Comparative Metric
Sample
Characteristics
Exome Variant
Detection
Performance
Exome Variant
Characteristics
Pipeline 1
Pipeline 2
Pipeline 3
Sample ID
Subject Gender
Family Relationship
Total # Variants
Total # INDELS
Total # Homozygous Variants
Total # Heterozygous Variants
% de Novo
Coding Variant #
Coding Variants - % de Novo
Synonymous Variant #
Non-synonymous Variant #
Splice Variant #
UTR Variant #
Public (dbSNP 137) Known Variant #
Novel (based on dbSNP) Variant #
Xiaowu Gai, PhD, LiShuang Shen, PhD
MSeqDR-GEM.APP integrated software tool:
Web-based exome dataset mining
Stephan Zuchner, MD
University of Miami
Using gem.app to identify a novel
mitochondrial disease gene
Phenotype-Exome
Data Integration
and Consent
Incorporation of Phenotype Capture
and Display Tools
• REDCap
– Research electronic data capture tool
– Free, web-based, clickable data entry
– Custom design tools to capture any desired data type
• Common data elements (CDE) optimized for mitochondrial disease
• Integrate with NAMDC data capture tools and fields
REDCap-based Mito Disease Data Capture
Claire Sheldon, MD, PhD,
Elizabeth McCormick, MS, Jeff Miller, PhD
Self manage account & data access
Phil Yeske (UMDF), Sharon Terry (Genetic Alliance), Robert Shelton (Private Access)
MSeqDR “go-live” preparation underway
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MSeqDR tools technical optimization and response to community feedback
– G-browse optimization (variant blog, add lab-specific aggregate level data tracks)
– GUID system implementation and assignment to all data types
– Phenotype data integration (existing data on subjects vs newly collected)
• Integrate NINDS Common Data Elements for mitochondrial disease
• HPO ontology tree-like structure to vary phenotype data to access rights
– mtDNA genome analysis optimization
• Integrate with MitoMap, MitoMaster, Mito Tool Box, etc
– Haplogroup and heteroplasmy analysis
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Ethical use and oversight
– Data security protections (aggregate data, cloud computing)
– Develop web portal to enable patient privacy access for genetic +/- deidentified
phenotype data to be deposited into MSeqDR
• Genetic Alliance IRB approval
• Translate access page into different languages
– Develop data access and use oversight committee
• Clinical diagnostic labs, researchers, physicians, family support groups, etc
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Long-term financial support structure
– Commercial diagnostic laboratories and UMDF assessing long-term support options
– Integrate with NIH Clinical Genome Project and related resources (dbGAP, ClinVar)
CONCLUSIONS
• The international mitochondrial disease community is collaborating through the
MSeqDR Consortium to create a unified genomic database that facilitates
diagnosis and improved understanding of individual mitochondrial diseases
• MSeqDR website (http://mseqdr.luhs.org/) provide a common entry for
clinicians, diagnostic labs, and researchers involved in genomic analyses in
suspected mitochondrial disease
– Provide flexible and continually updated suite of web-based and open access software
tools accessible by clinicians, labs, and researchers from their office/clinic desktop to
securely mine exome data in a real-time setting
– Exploit collective information of variant allele frequencies in a large cohort of
individuals with suspected mitochondrial disease (gem.app, G-browse, etc), potentially
linked to relevant phenotype & laboratory data
– Accelerate pace and accuracy of known a& novel gene discovery in mito disease
– Deposit aggregate-level deidentified exome or variant data to share at various levels
of comfort (BioDAS Server)
– Provide improved knowledge and centralized resource for locus and variant allele
frequencies in nuclear AND mtDNA-based mitochondrial diseases
– Assist with transfer of anonymized data to public resources (NIH, Global Alliance)
Acknowledgements
MEEI/Harvard
University of Miami
Xiaowu Gai, PhD
Lishuang Shen, PhD
Stephan Zuchner, MD
Michael Gonzalez, PhD
CHOP
Claire Sheldon, MD, PhD
Danuta Krotoski, PhD
Elizabeth McCormick, MS, CGC Melisa Parisi, MD, PhD
MSeqDR PROTOTYPE DEVELOPMENT PARTICIPANTS:
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NICHD, NIH
Doug Wallace, Michio Hirano, Doug Kerr, Curt
Scharfe, Li Dong, Hakon Hakonarson, Bruce Cohen,
Amy Goldstein, Richard Haas, Russell Saneto (USA)
Marcella Attimonelli, Mannis van Oven (Italy)
Holger Prokisch (Germany)
Mark Tarnopolsky, Isabella Thiffault (Canada)
Richard Rodenburg, Jan Smeitink, IFM de Coo, Bert
Smeets, Fons Stassen (The Netherlands)
Virginia Brilhante (Finland)
Yasushi Okazaki (Japan)
Donna Maglott, Wendy Rubinstein (NCBI)
Heidi Rehm (ClinGen)
Clinical diagnostic laboratories:
• Jeana DaRe, David Ralph (Transgenomics)
• Renkui Bai, Sherri Bale (GeneDx)
• Richard Boles, Christine Stanley (Courtagen)
UMDF
Chuck Mohan, CEO
Dan Wright, President
Philip Yeske, PhD
Janet Owens
Cliff Gorski
FUNDING
United Mitochondrial Disease Foundation
NAMDC Pilot Grant Award #NAMDC7407
(NINDS/NICHD, NIH)
U01-HG006546 (NHGRI, NIH)
falkm@email.chop.edu
MSeqDR WEBSITE
https://mseqdr.org
falkm@email.chop.edu