Using Public Access Clinical Databases to Interpret NGS Variants

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In this webcast on February 19th, Gabe Rudy, Vice President of Product Development, will showcase publicly available databases and resources available for interpreting rare and novel mutations in the context of his own personal exome obtained through a limited 23andMe pilot in 2012.

The last couple years have seen many changes in well-established resources such as OMIM and dbSNP, while motivating new efforts such as ClinVar and PhenoDB to bring NGS interpretation to clinical grade through a global data sharing effort.

In this webcast, Gabe will cover:

The changing landscape of public annotations: Then, Now, and Soon.
Will the new human reference (GRCh38) released in December be a game changer?
Specific examples of improvements in annotation and algorithms that result in more accurate analysis of his own exome.
The utility and progress of NGS to different clinical applications in terms of public resources: carrier screening, hereditary cancer risk, pharmacogenomics, oncology care, and genetic disorder diagnosis.
Sharing of new clinical data: How both variation and phenotype level data is currently being shared and what will be the way forward to match rare and undiagnosed cases at a global scale.

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Using Public Access Clinical Databases to Interpret NGS Variants

  1. 1. Using Public Access Clinical Databases to Interpret NGS Variants February 19, 2014 Gabe Rudy VP Product Development Golden Helix
  2. 2. Use the Questions pane in your GoToWebinar window Questions during the presentation
  3. 3. My Background  Golden Helix - Founded in 1998 - Genetic association software - Analytic services - Hundreds of users worldwide - Over 800 customer citations in scientific journals  Products I Build with My Team - SNP & Variation Suite (SVS) - SNP, CNV, NGS tertiary analysis - Import and deal with all flavors of upstream data - GenomeBrowse - Visualization of everything with genomic coordinates. All standardized file formats. - RNA-Seq Pipeline - Expression profiling bioinformatics
  4. 4. Agenda Getting High Quality Variant Calls Data Sharing and the Maturing of Public Resources 2 3 4 Clinical Grade Candidate Variant Identification How I Met My Exomes1 NGS Clinical Utopia: Are We There Yet?5
  5. 5. Exome Sequencing in Consumer Genomics  Exomes done as part of Pilot Program  80x coverage  Raw data with no interpretation Erin JIA Gabe (me) Ethan
  6. 6. Research or clinical grade? Total Reads 140M Unique Align 87% Mean Target 105x % Target at 2x 97% % Target at 10x 94% % Target at 20x 89% % Target at 30x 83%
  7. 7. Agenda Getting High Quality Variant Calls Data Sharing and the Maturing of Public Resources 2 3 4 Clinical Grade Candidate Variant Identification How I Met My Exomes1 NGS Clinical Utopia: Are We There Yet?5
  8. 8. Alignment and Variant Calling Broken Down  2012 2 VCFs from 23andMe - BWA 0.6.1 - GATK (early & late 2012)  2013 Real Time Genomics - v3.1.2 2013-05-02 - Called on Trio  2014 Rerun - BWA 0.7.6 (2014-01-31) - FreeBayes 2014 - BWA/ FreeBayes
  9. 9. PSPH mis-alignment
  10. 10. Splice Mutation
  11. 11. GRCh38 – Here Now, but still Waiting  A better human reference - Revised Cambridge Reference Sequence (rCRS) MT - Has centromere models - ~2000 incorrect alleles fixed - ~100 assembly gaps updated  No Gene Annotations - RefSeqGene - Feb 2014 - Ensembl Q4 2014  No Variant Annotations - Re-align 1000 Genomes and NHLBI 6500? - dbSNP? GRCh37 GRCh38 Ts/Tv 2.06558 2.10171 snps snps mnps mnps indels indels complex complex 270000 280000 290000 300000 310000 320000 330000 340000 GRCh37 GRCh38 My Exome 331,824 319,442
  12. 12. Blog Post
  13. 13. Agenda Getting High Quality Variant Calls Data Sharing and the Maturing of Public Resources 2 3 4 Clinical Grade Candidate Variant Identification How I Met My Exomes1 NGS Clinical Utopia: Are We There Yet?5
  14. 14. Baylor Workflow - Clinical Exomes Paper Disease gene related Medically actionable deleterious variants Deleterious variants in ACMG gene list Deleterious variants VUS in dominant gene or homozygous in recessive gene Deleterious variant in gene with no known disease
  15. 15. Data Sources to Replicate Workflow  1000 Genomes (Phase 1)  “ESP” (NHLBI 6500 Exomes v2)  HGMD (Public vs Professional)  Variant’s Protein Coding Effect  RNA Splicing Effect  Genes Lists: - Single-Gene Disorder (OMIM with Inheritance) - Medically Actionable (114 genes NHLBI study) - Dominant Inheritance (MedGen) - ACMG Carrier Panel (ACMG Incidental Findings guidelines)
  16. 16. My Exome Analyzed Start: 235,689 847 234,842 224,914 9,928 9,069 807 859 40 242 13 59 565 0 624 624 255 20 20 20 0 0 598 644
  17. 17. Pathogenic by RSID match
  18. 18. Agenda Getting High Quality Variant Calls Data Sharing and the Maturing of Public Resources 2 3 4 Clinical Grade Candidate Variant Identification How I Met My Exomes1 NGS Clinical Utopia: Are We There Yet?5
  19. 19. Applications of NGS Data in the Clinic Carrier screening – prenatal and standard Lifetime risk prediction Genetic disorder diagnostics Oncology care PGx – dosage and care
  20. 20. ClinVar  Submitters: - OMIM: Johns Hopkins - Samuels - Lab for Molecular Medicine - Invitae - Emory Genetics Lab Star rating system - 0-4 stars – level of review ClinVar is designed to provide a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence.
  21. 21. HGMD  Data mines academic papers for reported functional variants  Also takes submissions, corrections reviewed by team  First available in 1996 - Originally 10k variants - 105k in Public (2014) - 148k in “Pro” (2014)
  22. 22. Example: CFTR  Different Variant Sources - CFTR2 (John Hopkins) - UMD-CFTR - ACMG  ClinVar - 1632 Variants - 442 Marked Pathogenic  ClinVitae - 446 Variants - 325 Marked Pathogenic  Caution Needed – Delta F508 Alignment
  23. 23. CFTR delta F508
  24. 24. BRCA: The back door to Myriad’s database 1995 – Patent issued to Myriad Genetics June 2013 – Patents invalidated by ruling Lab setting up Dx has a lot of catch up “Free the Data” and other ways in which Mryiad’s data is in ClinVar, etc. Sharing Clinical Reports Project
  25. 25. ClinVitae: ClinVar and Friends by Invitae Sources: - ClinVar (62,913) - Emory (13,365) - ARUP (2,850) - Carver Mut (199) - K Cunningham (581) 79,907 V, 9,189 G - 32,523 Pathogenic - 38,796 Likely Pathogenic Provided in HGVS - 59,878 after mapping to genomic space
  26. 26. BRCA: In my wife
  27. 27. Agenda Getting High Quality Variant Calls Data Sharing and the Maturing of Public Resources 2 3 4 Clinical Grade Candidate Variant Identification How I Met My Exomes1 NGS Clinical Utopia: Are We There Yet?5
  28. 28. Training  Most variants are rare or novel - Training to interpret these is extensive  MD/Pathology background is insufficient  Need a PhD in molecular genetics  There’s only 500 board certified Clinical Molecular Geneticists since started  Let’s share in the learning process Baylor Exome Sign-Out
  29. 29. Thank you  Heidi Rehm – Chief Laboratory Director at Laboratory for Molecular Medicine, PCPGM  Joel Parker – Cancer Genetics, UNC Chapel Hill  Gerry Higgins – VP, Pharmacogenomic Science, Assure Rx Health  Frank Schacherer – Chief Technical Officer, BIOBASE  Reece Hart – Computational Biologist, Invitae  Greta Linse Peterson – Director of Product Management and Quality, Golden Helix
  30. 30. Use the Questions pane in your GoToWebinar window Questions?
  31. 31. [cut slides after this]
  32. 32. Phenotypeing and Matchmaking Portals  PhenoDB  PhenomeCentral.org  Orphanet – Resources on over 6000 rare diseases and orphan drugs.  European centric: - GEN2PHEN (G2P)
  33. 33. Updated VCF and report at end of October GATK is a Research Tool. Clinics Beware.
  34. 34. Rare Disease Resources  Rare defined as affecting fewer than 200k people. - Most affect fewer than 6000 - 25M Americans have a rare disease  NIH Genetic and Rare Diseases Information Center (GARD)  ClinicalTrials.gov  Orphanet – Resources on over 6000 rare diseases and orphan drugs.
  35. 35. Cancer Resources  Behind germline because: - Sharing cancer data is more wholesale. You don’t just post a variant + a phenotype, you have to have whole variant sets - Cohorts are not covering enough ethnic groups. African americans under- represented - Not a lot of incentive for large cancer centers to share their internal databases  What do we do with the data? - 70% of tumors can find driver genes. But not many have actionable drugs. - Need much more evidence based trials to find more examples like BRAF V600E  Pic of BRAF V600E and drug

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