GAClinical & Phenotypic Data Capture
2022-2023 Roadmap Refresh
Motivation and Mandate
The widespread adoption of Electronic Health Records (EHRs), deep phenotyping methods, and improved patient generated multi-modal data provides an opportunity for information from genomics to be integrated into existing or emerging digital health infrastructure to support patient care and research. The existing health information infrastructure that needs to work with genomics includes the request for a genomics test, the sharing of the results from the test and the representation of genomics information in clinical and patient information systems. There is also a need for improved ways of representing a collection of subjects that satisfy a defined criteria represented by a phenotype.
This Work Stream establishes information models and technical standards to describe clinical and phenotypic data for use in genomic medicine and research, including the capture and exchange of information between clinical, research, and patient-centered systems. As part of these efforts, it will coordinate the integration of clinical and phenotypic data with other GA4GH and external standards.
A number of GA4GH Driver Projects are already developing the information infrastructure (forms, term lists, information models) which they are using to support the capture or sharing of information. This includes the forms which they are using to capture data on patients as they are sequenced as part of clinical demonstration projects. These examples will provide an important starting point for understanding the terminology and information models that are needed to describe, exchange and analyze clinical data supporting care and research.
Proposed Solution
The potential solution set for this Work Stream will include:
- Standardized exchange formats for representing clinical data[a]
- Extend and integrate GA4GH-initiated FHIR IGs and other resources for representing and working with phenotype and other clinical data
- Promote use of GA4GH phenopacket & pedigree within and outside of GA4GH, collecting feedback on pain points and developing software to ease adoption and promote correct use of the standard
- Define the sets of attributes required to describe a cohort consistently (eg. age, biological sex) and[b] capture the deep phenotypic characteristics as standard definitions to execute against a baseline population to extract counts and statistics of the subjects within and across datasets.
2022-2023 Deliverables
Pedigree | ||
Type(s) | Technical Specification ❑ API ☑ Data Model / Ontology ❑ Protocol ❑ File Format Policy Framework ❑ Policy Framework | Resource ☑ Guide ❑ Technical Toolkit ❑ Ongoing Project ❑ Other (Please Specify) |
Description | The GA4GH Pedigree Standard allows for the computable exchange of family health history as well as representation of larger, more complex families. The collection of specific clinical or genetic data is outside the scope of this deliverable, and is instead handled by other formats through references to individuals within the pedigree representation. It is intended to fit within the structure of other standards like HL7 FHIR and Phenopackets. The need for high quality, unambiguous, computable pedigree and family information is critical for scaling genomic analysis to larger, complex families. Pedigree data is currently represented in heterogeneous formats that frequently result in the use of lowest-common-denominator formats (e.g., PED) or custom JSON formats for data transfer. The HL7 FHIR standard core data models do not support pedigrees, but there is a draft extension to support genomic pedigrees that was evaluated. Standardizing the way systems represent family structure will allow patients to share this information more easily between healthcare systems and help software tools to use this information to improve genome analysis and diagnosis. | |
Source Driver Projects | Australian Genomics H3Africa GEM Japan Monarch Initiative BRCA Challenge | |
Other communities that will contribute to development | KidsFirst DRC Software vendors (PhenoTips, TrakGene...) | |
Proposed Implementations (at least 2 - only applicable if new version planned) |
| |
Milestone Type (see taxonomy) | Description | Expected Completion Date (by quarter) |
Deliverable: Conceptual Model v1.0 | To support the interoperability of family health history data within and between existing standards (such as HL7 FHIR and Phenopackets), the GA4GH Clinical and Phenotypic Data Capture Workstream developed the Pedigree Conceptual Model. The Pedigree Conceptual Model defines core classes and their properties, and is based on A Recommendation for The Common Data Set for Family Health History (see below). The three main classes are Individual, Relationship and Pedigree. | Sept. 2022 |
Deliverable: KIN Ontology | The Kinship Ontology (KIN) is a family relations ontology developed as part of the Global Alliance for Genomics and Health Pedigree Standard project. It allows using an OWL reasoner to automatically validate a family history graph and infer new relations. The latest version of the ontology can be found at: http://purl.org/ga4gh/kin.owl The Ontology is open-source and managed in this GitHub repo: https://github.com/GA4GH-Pedigree-Standard/family_history_terminology | Sept. 2022 |
Deliverable: Common Dataset | The collection and use of family health histories span medical activities from genetic research to heritable risk assessment in patient care. For all the stakeholders in this process, the goal must be data that is accurate and coded for effective analysis, and transferable between systems. To achieve this, a globally accepted and universally implemented family health history (FHH) data set should be established as a benchmark. The purpose of the common dataset document is to create an updated recommended data set that can be used not only in both research and clinical settings, but to eliminate the gap between the two disciplines. This recommendation should also guide the development of research, clinical, and patient-facing FHH data and information collection tools, applications, and data repositories. This document should only be used as informative. | Sept. 2022 |
Deliverable: Implementation within existing models
| The working group will prepare implementations and implementation guides of the common dataset elements across commonly-used data modeling systems, specifically HL7 FHIR, Phenopackets, and RDF. This will support the use of interoperable pedigree data across these ecosystems.
| Sept. 2022 + additional implementations in 2023 |
Deliverable: Tools
|
| Sept. 2022 Q2 2023 Q3 2023 |
Deliverable: Outreach/ Implementation Support
| Activities focused on promoting and supporting the use of the standard and common dataset within the broader community and ecosystem of vendors.
| Q1 2023 Q2 2023 Q2 2023 Q3 2023 |
P[c]henopackets | ||
Type(s) | Technical Specification ❑ API ☑ Data Model / Ontology ❑ Protocol ❑ File Format Policy Framework ❑ Policy Framework | Resource ❑ Guide ❑ Technical Toolkit ❑ Ongoing Project ❑ Other (Please Specify) |
Description | The Phenopacket Schema represents an open standard for sharing disease and phenotype information to improve our ability to understand, diagnose, and treat both rare and common diseases. A Phenopacket links detailed phenotype descriptions with disease, patient, and genetic information, enabling clinicians, biologists, and disease and drug researchers to build more complete models of disease. | |
Source Driver Projects | Monarch Initiative Biosamples (EBI & ELIXIR) Solve-RD (EJP-RD) | |
Other communities that will contribute to development | GEM-Japan EUCANcan ICGC-ARGO AGHA EGA / ENA / EVA | |
Implementations (at least 2 - only applicable if new versions are planned) |
| |
Deliverable Type (see taxonomy) | Description | Expected Completion Date (by quarter) |
Deliverable: FHIR IG (xSDO coordination) | HAPI Server Code A FHIR client for performing roundtrip between GA4GH Phenopacket <-> FHIR Phenopacket will enable exchange of phenotypic information between research and clinical settings | Q3 2022 |
Deliverable: ISO Approved Phenopackets Documentation (xSDO coordination) | Without intentional coordination to keep our respective products aligned, there is a risk of unnecessary proliferation of redundant standards, as well as the development of semantically- and syntactically-conflicting standards that will hamper large scale interoperability and introduce confusion within the adopter community.Phenopackets will be published through ISO in ISO TC215/SC1 as “4454 Genomics Informatics – Phenopackets: A Format for Phenotypic Data Exchange”. This work will increase the availability of standardized phenotypic information and expand the collection of use cases to develop a standard relevant to genomics communities internationally. | Q3 2022 |
Deliverable: Interoperability with other GA4GH standards
| To align with the GA4GH Strategic Aims, future work will be focus on integrating the Phenopackets Schema with other GA4GH standards
| Q2 2022 Q4 2022 |
Deliverable: Implementation Support
| Activities focused on supporting the use of the standard within the broader community and ecosystem of tools:
| Target?? Q4 2023 |
Deliverable: Dissemination
| Activities focused on promoting adoption of the standard within the broader community:
| Q3 2022 (submitted to Nature Biotech) Q3 2022 (submitted to Advanced Genetics) Q4 2022? Q1 2023? |
Deliverable: Journal Interactions
| Is this still a goal? Please add detail and target date[d] Outreach | |
Deliverable: | ||
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To be iterated:
Computable Cohort Representation | ||
Type(s) | Technical Specification ❑ API ❑ Data Model / Ontology ❑ Protocol ❑ File Format Policy Framework ❑ Policy Framework | Resource ❑ Guide ❑ Technical Toolkit ❑ Ongoing Project ❑ Other (Please Specify) |
Description | The product aims to create a computable cohort representation standard to computationally describe a cohort and execute the cohort definition as a query across a network. The product will be comprised on two parts:
We aim to leverage and extend existing standards e.g. HL7 FHIR, OHDSI OMOP, Beacon v2, Data Connect and Phenopackets to operationalise this work. | |
Source Driver Projects | Monarch Initiative ELIXIR EJP-RD AGHA NCI GEM-Japan | |
Other communities that will contribute to development | HDR UK CINECA IHCC N3C | |
Implementations (at least 2 - only applicable if new versions are planned) | ||
Deliverable Type (see taxonomy) | Description | Expected Completion Date (by quarter) |
Deliverable 1: Landscape Analysis Spreadsheet | Engagement of key stakeholders, compilation of use cases, gap analysis of existing formats and APIs to represent these cases https://docs.google.com/spreadsheets/d/1KXUr2DeRNCbiaHim9l_mnogkqhKAQAid0LbphTHN1Xg/edit?usp=sharing | |
Deliverable 2: Hackathon | 1) Crosswalk asthma phenotypes to represent an ideal asthma phenotype 2) Specify the phenotype in an executable format (CQL etc) and 3) test the standard across multiple implementations in a federated manner. Each federated implementation will test it against a real dataset to evaluate what is needed to expand the standard. | |
Deliverable 3: Model v1.0 | ||
Deliverable 4: | ||
Future Roadmap Items for Discussion
- Don’t have a terminology/ontology project but could be something to consider
- How does the work relate to registry standard and longitudinal data capture
- How to support better usage of terminologies and codesets
- [insert ideas here]
Items proposed in 2020:
- Ontology Best Practices - possible joint effort with TASC to do in context of GA4GH
- Tabular phenopackets - look into SearchAPI
- Cohort Representation - Expand the cohort message in Phenopackets with additional attributes. Will help metadata harmonization and federated querying across cohorts.
2020-2021 Roadmap
Pedigree | ||
Type(s) | Technical Specification ❑ API ☑ Data Model / Ontology ❑ Protocol ❑ File Format Policy Framework ❑ Policy Framework | Resource ❑ Guide ❑ Technical Toolkit ❑ Ongoing Project ❑ Other (Please Specify) |
Description | The need for high quality, unambiguous, computable pedigree and family information is critical for scaling genomic analysis to larger, complex families. Pedigree data is currently represented in heterogeneous formats that frequently result in the use of lowest-common-denominator formats (e.g., PED) or custom JSON formats for data transfer. The HL7 FHIR Family member history for genetics analysis profile supports pedigrees, but there is a need to add more data elements and rethink the datTa model to support a broader range of use cases. This will be accomplished by creating a minimum core dataset for family health history and developing the next version of a data model. Both will be evaluated and extended by GA4GH, extensions sent back to HL7 for inclusion into FHIR, and feedback gathered from G2MC. Standardizing the way systems represent family structure will allow patients to share this information more easily between healthcare systems and help software tools to use this information to improve genome analysis and diagnosis. | |
Source Driver Projects | AGHA Genomics England Monarch Initiative | |
Other communities that will contribute to development | KidsFirst DRC H3Africa EGA SPHN BRCA Challenge GEM Japan Software vendors (PhenoTips, ...) | |
Proposed Implementations (at least 2) |
| |
Expected Completion Date | 2021-09-01 | |
Milestone Type (see taxonomy) | Description | Expected Completion Date (by quarter) |
Milestone 1: Landscape Analysis | Stakeholders engagement M1.1 Engagement of key pedigree data producers (EHRs, vendors) and consumers (projects, tools, analyses) within the GA4GH to motivate the need for export and interoperability M1.2 Compilation of difficult family history cases from stakeholders (e.g., 3-person IVF, 2 pairs of identical twins, multiple loops of consang., etc.) M1.3 Gap analysis of existing formats and APIs to represent these cases | 2020-Q1 |
Milestone 2: Draft specification | Development of a draft standard for pedigree data exchange M1.1 Creation of working group to incubate the technical standard development M1.2 Invite GA4GH stakeholders to join working group and participate in the early development and pilot implementation M1.3 Development of draft specification / implementation guide | 2020-Q3 |
Milestone 3: Implementation | Interoperability pilot M3.1 Compliance test suite implementation M3.2 Two implementations of draft specification that pass compliance tests (potential parties: EHR, patient-facing vendor, clinical-facing vendor, CDS, research system); shoot for end-to-end: patient survey → clinical system → CDS → clinical system → research system | 2021-Q3 |
Phenopackets | ||
Type(s) | Technical Specification ❑ API ☑ Data Model / Ontology ❑ Protocol ❑ File Format Policy Framework ❑ Policy Framework | Resource ❑ Guide ❑ Technical Toolkit ❑ Ongoing Project ❑ Other (Please Specify) |
Description | The Phenopacket Schema represents an open standard for sharing disease and phenotype information to improve our ability to understand, diagnose, and treat both rare and common diseases. A Phenopacket links detailed phenotype descriptions with disease, patient, and genetic information, enabling clinicians, biologists, and disease and drug researchers to build more complete models of disease. There are a few anticipated releases: 1) Point updates to better represent time and include expanded fields about treatment course, exposure, and medical actions in response to COVID-19; 2) A core HL7 FHIR Implementation Guide; and 3) An update that will expand the cancer fields in Phenopackets and integrate Variant Representation (VRS) from the GKS Work Stream. | |
Source Driver Projects | Monarch Initiative Biosamples (EBI & ELIXIR) Solve-RD (EJP-RD) | |
Other communities that will contribute to development | GEM-Japan EUCANcan ICGC-ARGO AGHA All of Us H3Africa | |
Implementations (at least 2) | Server Implementations
Client Implementations
| |
Expected Completion Date | V1.1 - Q4 2020 V2.0 - Q2 2021 | |
Milestone Type (see taxonomy) | Description | Expected Completion Date (by quarter) |
Milestone 1: Feature Set | v1.1 - Many initiatives are undertaking COVID-19 analysis. Phenopackets will provide a computational model for ontology-based and machine-based learning that would be otherwise difficult to implement with SQL. This update will include additional fields useful for COIVD-19 research:
| 2020 - Q4 |
Milestone 2: Feature Set (Possible Major Version) | Enhancement - Incorporation of Variant Representation Specification (VRS) into the Variant building block. Expansion of phenopackets to better serve the cancer community
| 2020 - Q4 for draft prototype 2021 - Q2 for release |
Milestone 3: Implementation | The Phenopackets schema core HL7 FHIR Core Implementation Guide is based on the Phenopackets standard. M3.1 - Operations & Community Engagement
M3.2 - Alignment of Phenopackets FHIR Core IG with Phenopackets v1
M3.3 - Pilot Testing
| 2021 - Q3 |
Milestone 3: Implementation | Phenopackets will be published through ISO in ISO TC215/SC1 as “4454 Genomics Informatics – Phenopackets: A Format for Phenotypic Data Exchange”. | 2022 - Q2 (estimated) |
[a]Are we really going after ALL clinical data?
[b]sorry 5am just too early for me! one thing very pertinent is design - case control, case cohort, cohort with selection bias etc. something to consider to add in. get very skewed ideas of phenotype representation when there are selection criteria for establishing a cohort
[c]@j.jacobsen@qmul.ac.uk @Peter.Robinson@jax.org @moni@tislab.org @MONICA.MUNOZ-TORRES@cuanschutz.edu @MELISSA.HAENDEL@cuanschutz.edu - Hi all, can you please review this 22/23 roadmap draft and make any edits to content/dates as needed? Roadmaps are due to be presented to GA4GH SC in June! You'll see a few question marks or highlight where suggestions would be appreciated. Thank you!
[d]Not sure if this is still a goal, but IF we can develop tooling and get a journal (even one journal; GiM?) on board, that would be HUGE
[e]see my comment above about study design