Data in genomics: Dr Richard Scott, Clinical Lead for Rare Disease, 100,000 Genomes Project, Genomics England
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This document discusses the importance of structured data and standardized nomenclature for analyzing genomic data at scale. It notes that every person's genome contains 3 billion DNA base pairs with around 5 million variants compared to the reference genome. The 100,000 Genomes Project aims to generate genomic and clinical data from 100,000 participants to help find treatments for rare diseases. Key challenges discussed include dealing with large amounts of genomic and associated unstructured clinical data, and the need for automated and standardized approaches using structured data models and established clinical terminologies to enable machine learning and clinical interpretation of the data.
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Data in genomics: Dr Richard Scott, Clinical Lead for Rare Disease, 100,000 Genomes Project, Genomics England
- 1. Data in genomics Dr Richard Scott Clinical Lead for Rare Disease, Genomics England
- 2. 225 April 2018 Clinical diagnosis Molecular diagnosis Understanding the disease Finding a treatment Unrecognised
- 3. 325 April 2018 Everyone’s genome has… • 3 billion DNA ‘letters’ (known as base pairs, ACGT) • 5 million variants (places where our genome is different from the ‘reference’ sequence) • 500,000 variants which have never been seen before • 20,000 variants which affect protein-coding genes • 100 very rare variants affecting protein coding genes 1 variant changing 1 DNA base pair is enough to cause a rare disease!
- 4. The last 10 years have changed things 425 April 2018 Single gene Small panel (5-10) Larger panel (50-100) Clinical exome Whole exome Whole genome
- 7. The 100,000 Genomes Project 725 April 2018
- 8. 8 825 April 2018 Think structured data first • It is very difficult to do anything sensible at scale with unstructured data • Think clinical notes on 70,000 people • Required for • Automation • Standardisation and interoperability • Machine learning • Standardised nomenclature is key • Data models are key
- 9. 25/04/2018 9 925 April 2018 Structured data is key • Core clinical data set: • Disease status (bespoke) • Pedigree data (Panogram) • Human Phenotyping Ontology • ICD10, SNOMED CT, OMIM • Clinical test data where relevant and not captured by HPO • Using established standards wherever possible
- 10. Use the (structured) data that’s out there Life course data: Secondary sources NHS Digital • Hospital Episodes • ONS death details • Diagnostic Imaging • Patient recorded outcomes • Mental health & intellectual disability Public Health England • Cancer registry & datasets (COSD, SACT, RTDS, DID) • Other disease registries • Clinical audit • Screening programmes GP data • Prescribing/dispensing • Reports/letters • Notes (free text) GeL genomic results Interpretation Validation Clinical application Germline Somatic Exit questionnaire GMC clinical data Interpretation data: RD: HPO terms, Pedigree Ca: Diagnosis & staging Comprehensive clinical data: Data models for key data Including lab test results EHR data dump Treatments & Investigations GMC registration Demographics Consent status Additional findings Registration Sample management 1025 April 2018 NHSD success: annual agreement, received quarterly, matched 98.2% participants April HES delivery 2.3m episodes on 31,781 participants, increases 400K / quarter Death data received on 430 participants, other data sets arriving now Font colour key Receiving data Data requested Planned source
- 11. HES data Outpatient episodes A&E Inpatient Critical care = cancer treatment = non-cancer treatment
- 12. Patient/ family Phenotypes & Pedigree DNA Genome sequence Annotated VCFs Tiered variants Gene Panel Variant filtering Annotation Companies Review Gene Panels Clinical assessment GeCIP(s) Validation Outcomes Reporting tool Semi-automated Interpretation pipeline Report QA Test request WORKFLOW MANAGER DATA DISTRIBUTION FRAMEWORK PanelAssigner PanelApp
- 13. Modelling the data: an ‘interpretation request’ "interpretation_request_id": XXX, "version": "1", "created_at": "2016-12-09T10:43:57.137142Z", "cip": "congenica", "family_id": "FM50000XZY", "sample_type": "raredisease", "interpretation_request_data": { "json_request": { "genomeAssemblyVersion": "GRCh37.p13", "virtualPanel": null, "pedigreeDiagram": null, "analysisVersion": "1", "TieringVersion": "0.3.7", "additionalInfo": null, "analysisReturnURI": "/gel/returns/SAP-XXX-1", "pedigree": { "gelFamilyId": "FM50000XZY", "participants": [ { "personKaryotipicSex": "XO", "fatherId": 4, "dataModelCatalogueVersion": "v4.2", "twinGroup": null, "sex": "female", "superMotherId": null,
- 14. Interpretation ecosystem 1425 April 2018 GeL genomes and clinical data CIP API Interpretation Request Interpreted Genome NHS GMCs GeL Knowledgebase External Knowledgebases Interpretation/annotation services GeL Tiering Service 5 Service 1 Service 2 Service 3 Decision support tools Tool 4Tool 1 Tool 2 Tool 3 CIPAPI GeCIP Interpreted Genome The CIP API is also accessible to NHS GMCs
- 15. Interpreting a genome 15 • Compare with the ‘reference genome’ • Compare with what we already know from biology • Compare variants with those in people with and without the condition • Look most carefully at parts of the genome known to be associated with the condition Multidisciplinary working is key • Clinical and laboratory working • Integration across medical specialties
- 16. 16 c Interpreting a genome When current knowledge doesn’t cut it • Compare with the ‘reference genome’ • Compare with what we already know from biology • Compare variants with those in people with and without the condition WORLDWIDE • Look most carefully at parts of the genome known to be associated with the condition • ENSURE RESEARCH AND DIAGNOSTICS ARE WORKING HAND IN HAND
- 17. ‘With great power comes great responsibility’
- 18. Repeating themes • The themes are shared in other areas of genomics • Cancer • Common disease • Pharmacogenomics 1825 April 2018
- 19. Summary • Genomics is about big data • We need to take key steps to capitalise on it • Use standard nomenclatures • Use structured data formats • Automate as many of our interactions with it as possible • Recognise the peculiarities of the questions we are asking and the challenges that those bring given our current knowledge • Given the potential reach of genomics, we must do this! 1925 April 2018
- 20. Thank you! Stay in touch Follow ‘Genomics England’ www.genomicsengland.co.uk @genomicsengland #genomes100k Like the ‘Genomics England’ page
Editor's Notes
- One genome is about 200GB – it would take up the memory of an average laptop The project will generate about 21 petabytes of data – that’s 2,000 years worth of music on an mp3 player!
- Why should you care?
- Centralised software where GMCs can view and perform a multi-disciplinary team (MDT) interpretation of their cases Sharing of cases with colleagues in other GMCs and GeCIPs Connected to a central GeL knowledgebase so previous interpretations of same variant are displayed
- Why should you care?