Report from the Gene & Disease Specific Database Advisory Council - Peter Taschner
•Download as PPTX, PDF•
1 like•415 views
A report from the Chair of the Human Variome Project Gene & Disease Specific Database Advisory Council
Report
Share
Report
Share
Recommended
Matchmaking initiatives like GeneMatcher, have demonstrated the utility of gene-based matching for identification of unrelated individuals with similar phenotypes and pathogenic variants in the same gene. Phenotype-based matching (PBM) has been attempted less widely because of challenges such as phenotypic variability, relative paucity of phenotypic details in clinical genomic databases, and the use of variable phenotypic terminology by clinicians and researchers. As part of the Baylor-Hopkins Center for Mendelian Genomics (BHCMG), users submit their cases to PhenoDB using PhenoDB phenotypic terms, which enables the use of semantic-similarity based methodologies to quantify phenotypic overlap within the database. To test PBM, we initially compared the following methodologies: Jacquard, Distance, Resnick (OMIM-based and PhenoDB-based corpora), and Wang. The Resnick-PhenoDB algorithm uses the phenotypic features that describe 4,114 cases in PhenoDB as the corpus for calculation of information content instead of the OMIM clinical synopses or HPO annotations. To validate the matching algorithms, we utilized a simulated set of 55 cases phenotyped by using the OMIM clinical synopsis of four well known phenotypes (OMIM 136140, 615960, 117650, 615273), and demonstrated that for 3 of the 4 disorders, all cases known to have the same disorder had the highest phenotypic similarity scores. We then tested the matching algorithms on phenotypic data from 4,114 unrelated probands in the BHCMG PhenoDB. We chose 3 phenotypes for which multiple unrelated probands are present in the database: Gomez-Lopez-Hernandez Syndrome (N=5, GLHS, OMIM 601853), Hemifacial Microsomia (N=12, HFM, OMIM 164210), and Lateral Meningocele Syndrome (N=5, LMNS, OMIM 130720). The average number of features entered per phenotype was 7.3 for GLHS, 8.14 for HFM and 0.8 for LMNS. We selected one case at random for each condition as the query case and determined the proportion of expected matching cases present in the top 1% and 5% among the 4,114 cases. Resnick-PhenoDB algorithm found that for GLHS, 3 of the 5 expected matching cases were identified among the top 1% and 4 of 5 in the top 5%. For HFM, 2 of the 12 expected matching cases were identified among the top 1% and 4 of 12 in the top 5%. For LMNS, 0 out of the 5 expected matching cases were identified among the top 1% and 0 of 5 in the top 5%. Using a simulated set of cases, we showed that all 5 algorithms performed similarly and that Resnick PhenoDB-based algorithm is able to identify and prioritize the expected matching cases among the total number of cases. Applying the Resnick PhenoDB-based algorithm to the real-world BHCMG PhenoDB showed the importance of detailed case descriptions if PBM is desired. Efforts to improve the availability and consistency of phenotypic annotations, as well as enhanced similarity calculation methodologies, will improve the fidelity and utility of PBM.Phenotype-based Matching Using PhenoDB Terms in BHCMG PhenoDB to Maximize Who...
Phenotype-based Matching Using PhenoDB Terms in BHCMG PhenoDB to Maximize Who...Human Variome Project
A key barrier to the efficient clinical utilization of genome sequence data is the lack of a systematic approach for interpreting the pathogenicity of genetic variants, with resultant discordance among laboratories and researchers in classification. The ClinGen (Clinical Genome) Project has been funded by the United States National Institutes of Health with a goal of maximizing the clinical relevance of results from genetic testing. ClinGen has established a Sequence Variant Interpretation (SVI) Working Group to refine and standardize the approach to pathogenicity classification. Recently the American College of Medical Genetics and Genomics (ACMG) published guidelines [1] that emerged from a workgroup that represented the expert opinion of clinical laboratory directors and genetics clinicians. These guidelines were developed to help clinical laboratories that report results from sequencing of single genes, panels, exomes, and genomes. The ClinGen SVI has set short term and long term objectives for advancing the field of pathogenicity interpretation using the ACMG framework as a starting point. There is consensus within the field that correct classification of variants requires integrating multiple lines of evidence, including, clinico-pathologic, epidemiologic, bioinformatics (in silico), and in vitro data. How best to combine them is unclear. The ACMG framework described different categories of evidence and assigned preliminary assessments of what comprises weak or strong evidence favoring a variant’s pathogenicity or neutrality and preliminary rule- based algorithms of how to combine evidence. In the short term, the ClinGen SVI has set up sub-committees to apply more precision in defining criteria for pathogenicity and what comprises strong and weak evidence. In the long term, the ClinGen SVI looks to transition from qualitative descriptions to a quantitative system that can assign an empirically derived probability of pathogenicity for each variant. Preliminary analyses suggest that there will be general rules that apply to all genes, as well as specific approaches for different genetic disorders. Well characterized databases of variants for each genetic disorder will be critical to the process. Funded by the National Human Genome Research Institute through the following three grants: U41 HG006834-01A1, U01 HG007437- 01, U01 HG007436-01, and by the National Cancer Institute through contract HHSN261200800001E. Reference: 1. Richards S, Aziz N, Bale S, et al. Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine2015;17(5):405-424.The ClinGen Sequence Variant Interpretation Working Group: Refining Criteria ...
The ClinGen Sequence Variant Interpretation Working Group: Refining Criteria ...Human Variome Project
Background: New technologies and increased competition have, and will continue to improve the cost-effectiveness of genetic testing, making genetic analysis more accessible to medical practices worldwide. However, challenges remain to establishing the validity of such tests. Moreover many patients harbor rare or novel variants and classification is likely to remain a bottleneck in broader deployment of genetic medicine.Establishing validity, reproducibility, and utility of highly scalable geneti...
Establishing validity, reproducibility, and utility of highly scalable geneti...Human Variome Project
Recommended
Matchmaking initiatives like GeneMatcher, have demonstrated the utility of gene-based matching for identification of unrelated individuals with similar phenotypes and pathogenic variants in the same gene. Phenotype-based matching (PBM) has been attempted less widely because of challenges such as phenotypic variability, relative paucity of phenotypic details in clinical genomic databases, and the use of variable phenotypic terminology by clinicians and researchers. As part of the Baylor-Hopkins Center for Mendelian Genomics (BHCMG), users submit their cases to PhenoDB using PhenoDB phenotypic terms, which enables the use of semantic-similarity based methodologies to quantify phenotypic overlap within the database. To test PBM, we initially compared the following methodologies: Jacquard, Distance, Resnick (OMIM-based and PhenoDB-based corpora), and Wang. The Resnick-PhenoDB algorithm uses the phenotypic features that describe 4,114 cases in PhenoDB as the corpus for calculation of information content instead of the OMIM clinical synopses or HPO annotations. To validate the matching algorithms, we utilized a simulated set of 55 cases phenotyped by using the OMIM clinical synopsis of four well known phenotypes (OMIM 136140, 615960, 117650, 615273), and demonstrated that for 3 of the 4 disorders, all cases known to have the same disorder had the highest phenotypic similarity scores. We then tested the matching algorithms on phenotypic data from 4,114 unrelated probands in the BHCMG PhenoDB. We chose 3 phenotypes for which multiple unrelated probands are present in the database: Gomez-Lopez-Hernandez Syndrome (N=5, GLHS, OMIM 601853), Hemifacial Microsomia (N=12, HFM, OMIM 164210), and Lateral Meningocele Syndrome (N=5, LMNS, OMIM 130720). The average number of features entered per phenotype was 7.3 for GLHS, 8.14 for HFM and 0.8 for LMNS. We selected one case at random for each condition as the query case and determined the proportion of expected matching cases present in the top 1% and 5% among the 4,114 cases. Resnick-PhenoDB algorithm found that for GLHS, 3 of the 5 expected matching cases were identified among the top 1% and 4 of 5 in the top 5%. For HFM, 2 of the 12 expected matching cases were identified among the top 1% and 4 of 12 in the top 5%. For LMNS, 0 out of the 5 expected matching cases were identified among the top 1% and 0 of 5 in the top 5%. Using a simulated set of cases, we showed that all 5 algorithms performed similarly and that Resnick PhenoDB-based algorithm is able to identify and prioritize the expected matching cases among the total number of cases. Applying the Resnick PhenoDB-based algorithm to the real-world BHCMG PhenoDB showed the importance of detailed case descriptions if PBM is desired. Efforts to improve the availability and consistency of phenotypic annotations, as well as enhanced similarity calculation methodologies, will improve the fidelity and utility of PBM.Phenotype-based Matching Using PhenoDB Terms in BHCMG PhenoDB to Maximize Who...
Phenotype-based Matching Using PhenoDB Terms in BHCMG PhenoDB to Maximize Who...Human Variome Project
A key barrier to the efficient clinical utilization of genome sequence data is the lack of a systematic approach for interpreting the pathogenicity of genetic variants, with resultant discordance among laboratories and researchers in classification. The ClinGen (Clinical Genome) Project has been funded by the United States National Institutes of Health with a goal of maximizing the clinical relevance of results from genetic testing. ClinGen has established a Sequence Variant Interpretation (SVI) Working Group to refine and standardize the approach to pathogenicity classification. Recently the American College of Medical Genetics and Genomics (ACMG) published guidelines [1] that emerged from a workgroup that represented the expert opinion of clinical laboratory directors and genetics clinicians. These guidelines were developed to help clinical laboratories that report results from sequencing of single genes, panels, exomes, and genomes. The ClinGen SVI has set short term and long term objectives for advancing the field of pathogenicity interpretation using the ACMG framework as a starting point. There is consensus within the field that correct classification of variants requires integrating multiple lines of evidence, including, clinico-pathologic, epidemiologic, bioinformatics (in silico), and in vitro data. How best to combine them is unclear. The ACMG framework described different categories of evidence and assigned preliminary assessments of what comprises weak or strong evidence favoring a variant’s pathogenicity or neutrality and preliminary rule- based algorithms of how to combine evidence. In the short term, the ClinGen SVI has set up sub-committees to apply more precision in defining criteria for pathogenicity and what comprises strong and weak evidence. In the long term, the ClinGen SVI looks to transition from qualitative descriptions to a quantitative system that can assign an empirically derived probability of pathogenicity for each variant. Preliminary analyses suggest that there will be general rules that apply to all genes, as well as specific approaches for different genetic disorders. Well characterized databases of variants for each genetic disorder will be critical to the process. Funded by the National Human Genome Research Institute through the following three grants: U41 HG006834-01A1, U01 HG007437- 01, U01 HG007436-01, and by the National Cancer Institute through contract HHSN261200800001E. Reference: 1. Richards S, Aziz N, Bale S, et al. Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine2015;17(5):405-424.The ClinGen Sequence Variant Interpretation Working Group: Refining Criteria ...
The ClinGen Sequence Variant Interpretation Working Group: Refining Criteria ...Human Variome Project
Background: New technologies and increased competition have, and will continue to improve the cost-effectiveness of genetic testing, making genetic analysis more accessible to medical practices worldwide. However, challenges remain to establishing the validity of such tests. Moreover many patients harbor rare or novel variants and classification is likely to remain a bottleneck in broader deployment of genetic medicine.Establishing validity, reproducibility, and utility of highly scalable geneti...
Establishing validity, reproducibility, and utility of highly scalable geneti...Human Variome Project
More Related Content
What's hot
What's hot (20)
The BRCA Challenge & Exchange: Progress and Plans - Gunnar Rätsch
The BRCA Challenge & Exchange: Progress and Plans - Gunnar Rätsch
Clinical Validation of Copy Number Variants Using the AMP Guidelines
Clinical Validation of Copy Number Variants Using the AMP Guidelines
Use of open, curated variant databases: ethics? Liability? - Bartha Knoppers
Use of open, curated variant databases: ethics? Liability? - Bartha Knoppers
Utilization of NGS data and genomic selection to rescue an endangered and her...
Utilization of NGS data and genomic selection to rescue an endangered and her...
Using Public Access Clinical Databases to Interpret NGS Variants
Using Public Access Clinical Databases to Interpret NGS Variants
Clinical Validation of Copy Number Variant Detection by Next-Generation Seque...
Clinical Validation of Copy Number Variant Detection by Next-Generation Seque...
Pathema: A Clade Specific Bioinformatics Resource Center
Pathema: A Clade Specific Bioinformatics Resource Center
Evaluating Copy Number Variants with VSClinical's New ACMG Guideline Workflow
Evaluating Copy Number Variants with VSClinical's New ACMG Guideline Workflow
Introducing VSClinical AMP Guidelines: A Comprehensive Workflow for NGS Testi...
Introducing VSClinical AMP Guidelines: A Comprehensive Workflow for NGS Testi...
The Monarch Initiative: A semantic phenomics approach to disease discovery
The Monarch Initiative: A semantic phenomics approach to disease discovery
Updates to VSClinical ACMG Guidelines & a Tour of Cancer Annotation Sources
Updates to VSClinical ACMG Guidelines & a Tour of Cancer Annotation Sources
Gene Descriptions WormBase Workshop International Worm Meeting 2015
Gene Descriptions WormBase Workshop International Worm Meeting 2015
Similar to Report from the Gene & Disease Specific Database Advisory Council - Peter Taschner
The success of whole exome sequencing (WES) for highly heterogeneous disorders, such as mitochondrial disease, is limited by substantial technical and bioinformatics challenges to correctly identify and prioritize the extensive number of sequence variants present in each patient. The likelihood of success can be greatly improved if a large cohort of patient data is assembled in which sequence variants can be systematically analysed, annotated, and interpreted relative to known phenotype. This effort has engaged and united more than 100 international mitochondrial clinicians, researchers, and bioinformaticians in the Mitochondrial Disease Sequence Data Resource (MSeqDR) consortium that formed in June 2012 to identify and prioritize the specific WES data analysis needs of the global mitochondrial disease community. Through regular web-based meetings, we have familiarized ourselves with existing strengths and gaps facing integration of MSeqDR with public resources, as well as the major practical, technical, and ethical challenges that must be overcome to create a sustainable data resource. We have now moved forward toward our common goal by establishing a central data resource (http://mseqdr.org/) that has both public access and secure web-based features that allow the coherent compilation, organization, annotation, and analysis of WES and mtDNA genome data sets generated in both clinical- and research-based settings of suspected mitochondrial disease patients. The most important aims of the MSeqDR consortium are summarized in the MSeqDR portal within the Consortium overview sections. Consortium participants are organized in 3 working groups that include (1) Technology and Bioinformatics; (2) Phenotyping, databasing, IRB concerns and access; and (3) Mitochondrial DNA specific concerns. The online MSeqDR resource is organized into discrete sections to facilitate data deposition and common reannotation, data visualization, data set mining, and access management. With the support of the United Mitochondrial Disease Foundation (UMDF) and the NINDS/NICHD U54 supported North American Mitochondrial Disease Consortium (NAMDC), the MSeqDR prototype has been built. Current major components include common data upload and reannotation using a novel HBCR based annotation tool that has also been made publicly available through the website, MSeqDR GBrowse that allows ready visualization of all public and MSeqDR specific data including labspecific aggregate data visualization tracks, MSeqDR-LSDB instance of nearly 1250 mitochondrial disease and mitochodnrial localized genes that is based on the Locus Specific Database model, exome data set mining in individuals or families using the GEM.app tool, and Account & Access Management. Within MSeqDR GBrowse it is now possible to explore data derived from MitoMap, HmtDB, ClinVar, UCSC-NumtS, ENCODE, 1000 genomes, and many other resources that bioinformaticians recruited to the project are organizing.MseqDR consortium: a grass-roots effort to establish a global resource aimed ...
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...Human Variome Project
This presentation was jointly given by Kevin Read and Alisa Surkis of New York University during the two-part NISO webinar, Digital and Data Literacy, held on September 20, 2017.Read Surkis Facilitating Development of Research Data Services
Read Surkis Facilitating Development of Research Data ServicesNational Information Standards Organization (NISO)
Similar to Report from the Gene & Disease Specific Database Advisory Council - Peter Taschner (20)
The Human Variome Database in Australia in 2014 - Graham Taylor
The Human Variome Database in Australia in 2014 - Graham Taylor
Building a Network of Interoperable and Independently Produced Linked and Ope...
Building a Network of Interoperable and Independently Produced Linked and Ope...
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...
dkNET Webinar: Creating and Sustaining a FAIR Biomedical Data Ecosystem 10/09...
dkNET Webinar: Creating and Sustaining a FAIR Biomedical Data Ecosystem 10/09...
Wheat Data Interoperability (1) by Esther DZALE YEUMO KABORE and Richard FULSS
Wheat Data Interoperability (1) by Esther DZALE YEUMO KABORE and Richard FULSS
Enhance your research impact through open science
Enhance your research impact through open science
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health System
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...
Cancer Moonshot, Data sharing and the Genomic Data Commons
Cancer Moonshot, Data sharing and the Genomic Data Commons
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...
MseqDR consortium: a grass-roots effort to establish a global resource aimed ...
Read Surkis Facilitating Development of Research Data Services
Read Surkis Facilitating Development of Research Data Services
Identifying and tracking research resources using RRIDs: a practical approach
Identifying and tracking research resources using RRIDs: a practical approach
NIH iDASH meeting on data sharing - BioSharing, ISA and Scientific Data
NIH iDASH meeting on data sharing - BioSharing, ISA and Scientific Data
The Diversity of Biomedical Data, Databases and Standards (Research Data Alli...
The Diversity of Biomedical Data, Databases and Standards (Research Data Alli...
More from Human Variome Project
Introduction and Background: The Web-based PhenX Toolkit (consensus measures for Phenotypes and eXposures, https://www. phenxtoolkit.org/) is a catalog of standard measures designed to facilitate collaborative biomedical research. PhenX measures help ensure that phenotypes from different studies are collected and represented in a consistent format. This consistency can enable data comparability across sites in large cohorts (e.g., Precision Medicine Initiative) and facilitates combining data to validate clinically actionable variants, increase statistical power (e.g., studies of rare genetic conditions or gene-enviroment interactions), or compare treatments and outcomes between patients.The PhenX Toolkit: Standard Measures for Collaborative Research - Wayne Huggins
The PhenX Toolkit: Standard Measures for Collaborative Research - Wayne HugginsHuman Variome Project
There are many factors that impede genomic variant sharing in the UK, despite it becoming a necessary part of clinical care. These include the lack of a designated infrastructure or mechanism aggravated by the complexity of laws that apply, and fragmented and variable advice from local ‘Caldicott guardians’ who guide NHS trusts on their responsibilities concerning data protection and confidentiality. Since the legitimacy of data sharing in the UK is framed in terms of ‘personal data’ being shared for ‘direct care’ (subject to legal exceptions), the blurred boundaries between clinical care and research, and the spectrum of identifiability of data also lead to differing interpretations resulting in inconsistent practices.
In a multidisciplinary collaboration, the PHG Foundation and the UK’s Association for Clinical Genetic Science co-hosted a workshop to examine the clinical necessity for sharing variant data and associated phenotypic information, the technical feasibility and the legal and regulatory impediments to such sharing. Delegates included clinicians, laboratory scientists, and key policy makers, including the National Data Guardian for Health and Care and representatives from the 100,000 Genomes Project, a pioneering research project which promises to build a legacy for future genomics services in the UK. The key finding from our work was that current arrangements for sharing genomic variants within the NHS are unsatisfactory and inconsistent practices are compromising safety and quality. Our workshop report [1] highlights the urgent need for (i) national agreement to optimise sharing within the NHS and develop consensus on the legitimacy of data sharing, (ii) standardised operational processes, including a designated sustainable database or mechanism for sharing, and (iii) strong leadership by the multiple relevant health organisations to demonstrate the benefits and risks associated with sharing and not sharing data.
Since publication of the workshop report, the NHS Consortium (operating within the DECIPHER database) has reported a 120% increase in the number of cases shared, the 100,000 Genomes Project and associated data embassy have got underway and the EU Data Protection Regulation has been finalised. However research highlights continuing public reservations about some aspects of data sharing including commercial access and misgivings around secondary uses of data. Publication of the National Data Guardian’s long-awaited review of consent and security provisions to provide guidance on a new consent and opt-out model for sharing patient information in the NHS, has been delayed pending the results of the EU referendum being known. Against this backdrop, the imperative to develop robust, proportionate policies for genomic data sharing becomes increasingly acute.
Funding from the PHG Foundation and the Association for Clinical Genetic Science.Legal and regulatory challenges to data sharing for clinical genetics and ge...
Legal and regulatory challenges to data sharing for clinical genetics and ge...Human Variome Project
A report from the Chair of the International Confederation of Countries Advisory Council.Report from the International Confederation of Countries Advisory Council - M...
Report from the International Confederation of Countries Advisory Council - M...Human Variome Project
Numerous databases containing information about DNA, RNA and protein variations are available. Gene-specific variant databases (locus specific variation databases, LSDBs) are typically curated and maintained for single genes or groups of genes for a certain disease(s). These databases are widely considered as the most reliable information source for a particular gene/protein/disease, but it should also be made clear they may have widely varying contents, infrastructure, and quality. Quality is very important to evaluate because these databases may affect health decision-making, research and clinical practice. The Human Variome Project (HVP) established a Working Group for Variant Database Quality Assessment. The basic principle was to develop a simple system that nevertheless provides a good overview of the quality of a database [1]. The HVP quality evaluation criteria that resulted are divided into four main components: data quality, technical quality, accessibility, and timeliness. Instructions are available for the developed quality criteria and how implementation of the quality scheme can be achieved ([1], http://www.humanvariomeproject.org/finish/19/255.html). Examples are provided for the current status of the quality items in two different databases, BTKbase, an LSDB, and ClinVar, a central archive of submissions about variants and their clinical significance.
Reference: [1] Vihinen, M., Hancock, J. M., Maglott, D. R., Landrum, M. J., Schaafsma, C. P., Taschner, P. Human Variome Project quality assessment scheme for variation databases. Hum. Mutat. (in press).Human variome project quality assessment criteria for variation databases - M...
Human variome project quality assessment criteria for variation databases - M...Human Variome Project
Rare and common variants contribute to the complex inheritance of Hirschsprung disease Rare and common variants contribute to the complex inheritance of Hirschsprun...
Rare and common variants contribute to the complex inheritance of Hirschsprun...Human Variome Project
We have investigated compliance with guidelines regarding HGVS nomenclature and/or submission of variants to public databases in the author instructions of several genetics and genomics journals. For this, we used a list of genetics and genomics journals created by the Human Variome Project (HVP) office (See http://www.humanvariomeproject.org/resources/genetics-and-genomics-journals.html). The HVP aims to work towards open and free sharing of high quality information on genetic variation and its effect on human health. The HVP Gene and Disease-specific Database Council has created example instructions for authors for genetics and genomic journals containing guidelines regarding HGVS nomenclature and submission of variants to public databases. The HVP office has sent this document to journal editors asking them to include similar requirements in their instructions for authors. The rationale is that better instructions will help improving the quality of variant descriptions in manuscripts and access to variant information in databases.
We have used this list to select journals including both requirements. We have investigated the January 2016 issue of several of them. A group of students has checked the publications first for the basic requirements: mentions of the reference sequence used to describe variants and the presence of the variants in public databases. The next step was to check variant and phenotype descriptions with specific attention for predicted protein effects. Our preliminary results suggest that predicted protein effects in publications cannot be verified in case of altered splice sites without supporting RNA-level evidence or in case of insertions of unspecified nucleotides. Although the underlying variants are likely to have disease-causing effects, lack of supporting evidence disqualifies this information for diagnostic use. In several cases, the RNA-level information was specified in the gene variant database submission, indicating that submission of variants to databases prior to manuscript acceptance might improve the quality of publications. We have observed that authors include statements suggesting variants have been submitted to databases, but the variants were not found. Reviewers and journal editors could help improving manuscript quality by insisting on compliance and enforcing the existing guidelines.Checking the experts: compliance with author instructions regarding HGVS nome...
Checking the experts: compliance with author instructions regarding HGVS nome...Human Variome Project
Rare disease (RD) research is a field of medicine increasingly reliant on information technology, with the advent of low-cost whole-genome sequencing revolutionising the discovery of genetic causes of disorders. Detailed phenotype data, combined with genomic data, have an enormous potential to accelerate the identification of clinically actionable prognostic or therapeutic implications and to improve our understanding of RD. The harmonisation of phenomics information, including disorders and phenotype traits that are stored in different contexts in a non-standardised way, is a cornerstone for producing sound data to foster research.
HIPBI-RD («Harmonising phenomics information for a better interoperability in the rare disease field ») is a three-year project starting in 2016 funded via the E-Rare 3 ERA-NET. This project builds on three resources largely adopted by the RD community: Orphanet, its ontology ORDO (the Orphanet Rare Disease Ontology), HPO (the Human Phenotype Ontology) and PhenoTips, with the support of outstanding bio-ontologies players, the European Bioinformatics Institute and the Garvan Institute. The project aims to provide the community with an integrated, RD-specific bio-informatics ecosystem that will harmonse the way phenomics information is stored in databases and patient files worldwide, and thereby contribute to interoperability. This ecosystem will consist of a suite of tools and ontologies, optimised to work together, made available through commonly used software repositories.
The HIPBI-RD ecosystem will contribute to the interpretation of variants identified through exome and full genome sequencing by harmonising the way phenotypic information is collected, thus improving diagnostics. The ultimate goal of HIPBI-RD is to provide a resource that will contribute to bridging genome-scale biology and a disease-centered view on human pathobiology.
Acknowledgements:
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the ERA-NET Cofund action N° 643578, E-Rare3. The project is co-funded by the French National Research Agency (ANR), the German Federal Ministry of Education and Research (BMBF), German Research Foundation (DFG), and the Canadian Institutes for Health Research (CIHR)HIPBI-RD: Harmonising phenomics information for a better interoperability in ...
HIPBI-RD: Harmonising phenomics information for a better interoperability in ...Human Variome Project
More from Human Variome Project (20)
The PhenX Toolkit: Standard Measures for Collaborative Research - Wayne Huggins
The PhenX Toolkit: Standard Measures for Collaborative Research - Wayne Huggins
Legal and regulatory challenges to data sharing for clinical genetics and ge...
Legal and regulatory challenges to data sharing for clinical genetics and ge...
Report from the International Confederation of Countries Advisory Council - M...
Report from the International Confederation of Countries Advisory Council - M...
Human variome project quality assessment criteria for variation databases - M...
Human variome project quality assessment criteria for variation databases - M...
HVP Country Node: Venezuela - Aida Falcon de Vargas
HVP Country Node: Venezuela - Aida Falcon de Vargas
Human Genetics of Infectious Diseases - Laurent Abel
Human Genetics of Infectious Diseases - Laurent Abel
GENETIC HETEROGENEITY OF MITOCHONDRIAL DISORDERS - Agnès Rötig
GENETIC HETEROGENEITY OF MITOCHONDRIAL DISORDERS - Agnès Rötig
Richard GH Cotton: He may have been a bit before his time - Michael Watson
Richard GH Cotton: He may have been a bit before his time - Michael Watson
Report from the International Scientific Advisory Committee - John Burn
Report from the International Scientific Advisory Committee - John Burn
Rare and common variants contribute to the complex inheritance of Hirschsprun...
Rare and common variants contribute to the complex inheritance of Hirschsprun...
Checking the experts: compliance with author instructions regarding HGVS nome...
Checking the experts: compliance with author instructions regarding HGVS nome...
Pathogenicity Decision Pathway in Tuberous Sclerosis Complex - Rosemary Ekong
Pathogenicity Decision Pathway in Tuberous Sclerosis Complex - Rosemary Ekong
HVP Country Node: The Netherlands - Marielle van Gijn
HVP Country Node: The Netherlands - Marielle van Gijn
HIPBI-RD: Harmonising phenomics information for a better interoperability in ...
HIPBI-RD: Harmonising phenomics information for a better interoperability in ...
HVP Country Node: Democratic Republic of Congo - Aimé Lumaka Zola
HVP Country Node: Democratic Republic of Congo - Aimé Lumaka Zola
Recently uploaded
Recently uploaded (20)
Call Girls Alandi Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Alandi Call Me 7737669865 Budget Friendly No Advance Booking
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Dopamine neurotransmitter determination using graphite sheet- graphene nano-s...
Dopamine neurotransmitter determination using graphite sheet- graphene nano-s...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Chemical Tests; flame test, positive and negative ions test Edexcel Internati...
Locating and isolating a gene, FISH, GISH, Chromosome walking and jumping, te...
Locating and isolating a gene, FISH, GISH, Chromosome walking and jumping, te...
pumpkin fruit fly, water melon fruit fly, cucumber fruit fly
pumpkin fruit fly, water melon fruit fly, cucumber fruit fly
Pulmonary drug delivery system M.pharm -2nd sem P'ceutics
Pulmonary drug delivery system M.pharm -2nd sem P'ceutics
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
Forensic Biology & Its biological significance.pdf
Forensic Biology & Its biological significance.pdf
SAMASTIPUR CALL GIRL 7857803690 LOW PRICE ESCORT SERVICE
SAMASTIPUR CALL GIRL 7857803690 LOW PRICE ESCORT SERVICE
9999266834 Call Girls In Noida Sector 22 (Delhi) Call Girl Service
9999266834 Call Girls In Noida Sector 22 (Delhi) Call Girl Service
Report from the Gene & Disease Specific Database Advisory Council - Peter Taschner
- 1. SESSION VI HVP Gene & Disease Specific Database Advisory Council Thursday 2nd June Report from the Gene & Disease Specific Database Advisory Council Peter Taschner (chair)
- 3. • Share gene and disease-specific variant and phenotype data using public databases • Improve data interoperability to support data integration – Standardize descriptions: • Sequence variants: HGVS nomenclature • Structural variants: ISCN cytogenetic nomenclature Contact Varnomen@HGVS.org with questions and suggestions! • Phenotypes: Human Phenotype Ontology (HPO) Gene & Disease Specific Database Advisory Council Recommendations
- 4. • Gene/Disease Specific Variant Database Quality Parameters (HVP/GL/001-01/EN) – Next step: implementation by databases & platforms • Disclaimer Statements for Gene/Disease Specific Databases (HVP/GL/002-01/EN) • Variant Terminology and Exon Numbering (HVP/GL/003-01/EN) Guidelines produced by GDSDBAC
- 5. • WG06: Disease & Phenotype descriptions in Gene/Disease Specific Databases (chair: Peter Robinson) • WG08: Ethics checklist for Gene/Disease Specific Database curators and submitters (chair: Rosemary Ekong) Working Groups sponsored by GDSDBAC
- 6. • WG02: Assigning pathogenicity to a Genetic Variant (chair: Marc Greenblatt) • WG03: Minimal content for Gene Variant Databases (LSDBs)(chair: Peter Taschner) Related to WG07 • WG04: Sequence variant description committee (chair: Johan den Dunnen) Working Groups sponsored by ISAC
- 7. sequence variant nomenclature (varnomen.hgvs.org) Leiden Open Variation Database (www.lovd.nl) Mutalyzer sequence variant checker (mutalyzer.nl) Sequence format: Stable reference for diagnostics (www.lrg-sequence.org) HVP Recommended Systems
- 8. Journals mandatory submission (genotype + phenotype) genome browsers drawer archive current database mandatory submission (genotype + phenotype) Gene variant databases (whole genome installation) + per gene views Central repository Diagnostic labs ( National Databases ) ( Ethnic Databases ) per gene data exome data genome data Research Ideal genotype-phenotype data flow Who checks the databases? Who checks the journals?
- 9. Letters drawn up for editors of genetics/genomics journals: • Ask inclusion of correct nomenclature use and database submission in instructions to authors. Letters sent by HVP ICO HVP Genetics and Genomics Journal List: http://www.humanvariomeproject.org/resources/genetics-and-genomics- journals.html GDSDBAC action
- 10. Call on HVP Members: • Ensure nomenclature and database submission adequately addressed in: – own papers, – papers they review – instructions to authors issued by the journals that they are involved with. • Timeline: next 12 months Call on ICCAC members: • Ask national human genetics societies to address: – Proper use of nomenclature and database submission GDSDBAC action
- 11. FAIR data Findable: data uniquely and persistently identifiable. Others should be able to find your data. Accessible: conditions for use clear to machines and humans. Interoperable: machine-readable data, terminologies, vocabularies, or ontologies commonly used in the field; Reusable: compliant with the above, sufficiently well described with metadata and provenance information supporting linking or integration with other data sources, enabling proper citation.
- 12. Research data life cycle http://data4lifesciences.nl/hands/handbook-for-adequate-natural-data-stewardship/
- 13. • Search using http://Gene_symbol.LOVD.nl – http://TP53.lovd.nl redirects to list or TP53 database homepage – http://USH2A.lovd.nl redirects to list Gene variant database lookup
- 14. http://findis.org Finnish Disease portal: selected data
- 15. Genetic testing in Amsterdam: 178 nationalities Why share genetic data? Genetics vs. Genomics Reducing disease, increasing health
- 16. Variant assessment and classification Share genotype and phenotype information with gene variant databases
- 17. Heidi Rehm Mark variants by level of curation Clinical utility Research Genotype interpretation requires deep phenotype information
- 18. Capacity building • HVP members from different countries should: – Not re-invent the wheel, but contribute: inclusive innovation • Adopt and translate international guidelines, standards and ontologies • Mention problems, suggest solutions, help to implement – Get sufficient training: follow guidelines, apply standards clinical practice • Gene variant database curation courses – Help to develop training and education locally • Dutch medical geneticists: curriculum “Genetic nurse” • Accreditation necessary An important role for country nodes!