HL7 FHIR Genomics Operations - Reference Implementation

Source code for a public reference implementation of HL7 FHIR Genomics Operations.

Please refer to project Wiki page for details of this reference implementation, including how to replicate.

For additional information on the operations and the reference implementation, please see our JAMIA manuscript.

Issues (bugs, enhancements, etc) can be entered here. (Legacy issues are here). Contact [email protected] for other comments.

Use Case

A common use case driving the operations is the notion of an application (e.g. a SMART-ON-FHIR clinical genomics App, a clinical decision support application, an EHR screen) needing specific genotype or phenotype information, for a patient or a population. Applications have diverse needs, such as matching a cancer patient to available clinical trials based on identified somatic variants; screening for actionable hereditary conditions; identifying a risk for adverse medication reactions based on pharmacogenomic variants; updating a patient's risk as knowledge of their variants evolves; and more. A goal for FHIR Genomics operations is to ultimately support any and all of these clinical scenarios.

Scope

In scope are clinical genomics operations. In the future, operations supporting variant calling and annotation, and knowledge base lookups may be added. We further categorize clinical genomics operations along two orthogonal axes - subject vs. population, and genotype vs. phenotype. For example, the 'find-subject-variants' operation retrieves genotype information for a single subject; whereas the 'find-population-tx-implications' retrieves a count or list of patients having specific phenotypes (such as being intermediate metabolizers of clopidogrel).

Response

All operations return a JSON output. However, if an invalid request is submitted, or some other error occurs, a JSON response is returned in the following format:

{
  "type": string,
  "title": string,
  "detail": string,
  "status": int
}

Status Codes

The operations return the following status codes:

Status Code	Description
200	Successfully executed request
400	ERROR: Invalid query parameters
404	ERROR: Patient not found
422	ERROR: Failed LiftOver
500	INTERNAL SERVER ERROR

Testing

To run the integration tests, you can use the VS Code Testing functionality which should discover them automatically. You can also run python3 -m pytest from the terminal to execute them all.

Additionally, since the tests run against the Mongo DB database, if you need to update the test data in this repo, you can run OVERWRITE_TEST_EXPECTED_DATA=true python3 -m pytest from the terminal and then create a pull request with the changes.

Update py-ard database

• Run pyard.init(data_dir='./data/pyard', imgt_version=<new version>) to download the new version
• Run cd data/pyard && tar -czf pyard.sqlite3.tar.gz pyard-<new version>.sqlite3
• Upload pyard.sqlite3.tar.gz in a new release on GitHub
• Update PYARD_DATABASE_VERSION in .env
• Update UTILITIES_DATA_VERSION in .env with the new tag ID (short git sha)