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Genomics and Personal Medicine

Genomics and Personal Medicine. Michael Snyder July 25, 2013. Conflicts: Personalis , Genapsys , Illumina. Health Is a Product of Genome + Environment. Genome. Health. Exposome. Health Is a Product of Genome + Environment. Genome. Health. Exposome.

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Genomics and Personal Medicine

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  1. Genomics and Personal Medicine Michael Snyder July 25, 2013 Conflicts: Personalis, Genapsys, Illumina

  2. Health Is a Product of Genome + Environment Genome Health Exposome

  3. Health Is a Product of Genome + Environment Genome Health Exposome

  4. The Cost of DNA Sequencing is Dropping Human Genome Cost ~$3K http://www.genome.gov/

  5. Outline of Lecture 1) Introduction to Genome Variation and Sequencing Human Genomes 2) Impact of Genomics on Treating Disease 3) Impact of Genomics on Heathy People

  6. 1) Single nucleotide variants (SNVs) GATTTAGATCGCGATAGAG GATTTAGATCTCGATAGAG Genetic Variation Among People: Three Types 3.7 Million/person 2) Short Indels (Insertions/Deletions 1-100 bp) GATTTAGATCGCGATAGAG GATTTAGA------TAGAG 300-600K/person

  7. People Also Have Large Blocks of DNA that are Inserted, Deleted or Flipped Around = Structural Variants * • People Have3000 differences Relative to the Reference Human Genome Sequence • Likely responsible for much human differences and disease

  8. Human Genome Project - Determined the DNA Sequence of the Human Genome = 3 billion bases = “Reference Genome”- Completed 2003- Involved 2000 people- Cost: $0.5 to 1 billion- Used machines that sequenced 384 fragments at once

  9. New Machines - Sequence ~1 trillion bases per run~35 genomes at once- Genome Sequencing Cost: $3,000- Machine Cost: $800,000

  10. A Personal Genome Sequence is Determined by Comparing to a Reference Genome Sequence 30X: 75-100 b 35-40X: 101 b Map to Reference Genome Reveals 3.7 M SNPs Snyder et al. Genes Dev 2010;24:423-431

  11. Examples of People Who Have had Their Genomes Sequenced Jim Watson Craig Ventor Ozzy Osbourne From: www.genciencia.com sciencewithmoxie.blogspot.com.au/2010_11_01_archive.html

  12. Impact of Genomics on Medicine • Understand and Treat Disease • Cancer • Mystery diseases • Pharmacogenomics • Determining which drug side effects and doses • Managing Health Care in Healthy Individuals?

  13. Cancer Genome Sequencing • Cancer is a genetic disease: both inherited and somatic 2) 10-20 “driver” mutations 3) Every cancer is unique 4) Sequence genomes (cancer tissue and normal) find genetic changes and suggest possible therapies Vogelstein et al., March Science, 2013

  14. Patient with Metastatic Colon Cancer Chromosome 7: Two amplification regions EGFR CDK6 Chr 7p arm Chr 7q arm Genomic Copy Number CEN

  15. Each cancer is unique, containing private novel variants • Many affect genes lie in known pathways and inform diagnosis: Most times a new drug can be suggested. Gleevac: Targets Abl and Kit oncogenes

  16. Solving Mystery Diseases: Dizygotic Twins: Dopamine Responsive Dystonia • Constantly sick, colicky, failed to meet milestones “floppy”; MRI showed some abnormalities • Children diagnosed with dystonia • Trial of L-DOPA showed dramatic improvement in 2 days • Sequenced genomes-found mutation in SPR Gene • Administered dopamine + seratonin precursor X From Richard Gibbs, Baylor

  17. Sequencing Genomes of Healthy People: Incorporate into Medicine Genomic GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA…. Family History Medical Tests: Few Tests (<20) 1. Predict risk 2. Diagnose 3. Monitor 4. Treat & 5. Understand Disease States

  18. Personalized Medicine: Combine Genomic and Other Omic Information Transcriptomic, Proteomic, Metabolomic Genomic GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA…. 1. Predict risk 2. Diagnose 3. Monitor 4. Treat & 5. Understand Disease States

  19. Personal “Omics” Profiling (POP) Genome Epigenome Transcriptome (mRNA, miRNA, isoforms, edits) Personal Omics Profile Proteome Cytokines Metabolome Autoantibody-ome Microbiome

  20. Personal “Omics” Profiling (POP) Genome Epigenome Personal Omics Profile Transcriptome (mRNA, miRNA, isoforms, edits) Proteome Initially 40K Molecules/Measure-ments Now Billions! Cytokines Metabolome Autoantibody-ome Microbiome

  21. Personal Omics Profile 39 months; 62 Timepoints; 6 Viral Infections / / Chen et al., Cell 2012

  22. Accurate Genome Sequencing • Whole Genome Sequencing • Complete Genomics: 35 b paired ends (150X) • Illumina: 100 b paired ends (120X) • Exome Sequencing • Nimblegen • Illumina • Aglilent Illumina CG 345K 9% 3.30M 89% 100K 2% 3.3 M Hi conf. SNVs, 217K Indels and 3K SVs 2 or more Platforms (Plus low confidence)

  23. Genome Phasing: Assign Variants to Parental ChromosomesInitially Used Mother’s DNA • Variants M P

  24. Approach I: Mendelian Disease Risk Pipeline All variants ~3.5M Rare/novel variants (<5%) Coding Non-Coding Synonymous mRNA stability tRNA rate Nonsynonymous (1320) miRNA Splice UTR Seed sequence miRNA targets Damaging (234) SIFT PP2 OMIM/CuratedMendeliandisease (51) Rick Dewey & Euan Ashley

  25. Curated List of Rare Variants(SNVs, All heterozygous) Missense • ALAD, ABCC2, ACADVL, ADAMTS13,AGRN, BAAT, CDS1, CHD7, COL4A3, CTSD, DGCR2,DLD, DYSF, EPCAM, FGFR1OP, FKRP, GAA, GNAI2, HSPB1, IGKC, ITPR1, MED12, MKS1,NTRK1, PCM1, PKD1, PLEKHG5, PMS2, PRSS1, PTCH2, SERPINA1, SETX, SYNE1,TERT, TTN, VWF, ZFPM2, PNPLA2. • Nonsense • PRAMEF2, PLCXD2, NUP54, RP1L1, PIK3C2G, NDE1, GGN,CYP2A7,IGKC • Not Rare But Important • KCNJ11 , KLF14, GCKR … Bolded Genes expressed in PBMC (RNA).

  26. Rare Variants in Disease Genes (51 Total) Missense • ALAD, ABCC2, ACADVL, ADAMTS13,AGRN, BAAT, CDS1, CHD7, COL4A3, CTSD, DGCR2,DLD, DYSF, EPCAM, FGFR1OP, FKRP, GAA, GNAI2, HSPB1, IGKC, ITPR1, MED12, MKS1,NTRK1, PCM1, PKD1, PLEKHG5, PMS2, PRSS1, PTCH2, SERPINA1, SETX, SYNE1,TERT, TTN, VWF, ZFPM2, PNPLA2. High Cholesterol Aplastic Anemia Diabetes • Nonsense • PRAMEF2, PLCXD2, NUP54, RP1L1, PIK3C2G, NDE1, GGN,CYP2A7,IGKC • Not Rare But Important • KCNJ11 , KLF4, GCKR …

  27. Approach II: Complex Disease Risk Profile Using VariMed * * 0% 100% Rong Chen & Atul Butte

  28. GLUCOSE LEVELS HbA1c (%): 6.4 6.7 4.9 5.4 5.3 4.7 (Day Number) (329) (369) (476) (532) (546) (602) LIFESTYLE CHANGE (DAY 380-CURRENT) RSV INFECTION (DAY 289-311) HRV INFECTION (DAY 0-21)

  29. Expression of 50 Cytokines ? HRV RSV DAY 0 DAY 0 DAY 12

  30. Many SNVs are Expressed RNA 2.67 B 100 b PE reads 30,963 (40 reads or more) 1,797nonsynonymous 8 nonsense Protein >130 Hi Confidence RNA Editing 2,376 Hi confidence Allele Specific Expression Jennifer Li-Pook-Than

  31. Transcriptome, Proteome, MetabolomeAnalysis Summary: Processing Steps (1) Preprocessing (2) Common Classification Scheme (3) Clustering and Enrichment Analysis - Overall trends (autocorrelation) - Spikes at specific timepoints george mias

  32. Integrated Analysis of Proteome, Transcriptome, Metabolome Dynamics: Overall trend RSV george mias

  33. Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome Glucose Regulation of Insulin Secretion Platelet Plug Formation georgemias RSV 18 days

  34. Autoantibody Profiling - Probe Array containing ~9000 human proteins; - Reactivity with DOK6; an insulin receptor binding protein + 3 other proteins related to T2D snyderome.stanford.edu

  35. Many Unaddressed Challenges Interpreting regulatory/non protein coding regions DNA Methylation Complex Cells Large Volume Used Microbiome 6) Exposome

  36. DNA Methylation Modified Cytosines: Usually associated with gene inactivation • Deep Sequencing: two time points analyzed a) 1.5 B Uniquely mapped reads (50X) b) 2.69 B Uniquely mapped reads (89.6X) • ~19,000 non CG disruption allele differential methylated CGs • 539 allele differential methylated regions (DMRs) • Identified well known regions: H19, GNAS • Identified many novel regions

  37. Incorporate Methylation Data

  38. Possible Phenotypic Consequences of Differentially Methylated Regions?

  39. 2. Other Data Types: Sensors 71 71 Moves App AliveCor Measures ECG

  40. The Future? Genomic Sequencing Omes and Other Information GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTA…. 1. Predict risk 2. Early Diagnose 3. Monitor 4. Treat http://www.baby-connect.com/

  41. Conclusions Personal genome sequencing is here. The medical interpretation is difficult. Genome sequencing can predict disease risk that can be monitored with other omics information. Integrated analysis can provide a detailed physiological perspective for what is occurring. Regulatory information is variable among humans; it and DNA methylation data needs to be incorporated into genome interpretation Every person’s complex disease profile is different and following many components longitudinally may provide valuable information.

  42. Final Conclusion 6) You are responsible for your own health Data at: snyderome.stanford.edu

  43. The Personal Omics Profiling Project Rui Chen, George Mias, Hugo Lam, Jennifer Li-Pook-Than, Lihua Jiang, KonradKarczewski, Michael Clark, Maeve O’Huallachain, ManojHariharan,Yong Cheng, Suganthi Bali, Sara Hillemenyer, RajiniHaraksingh, ElanaMiriami, Lukas Habegger, Rong Chen, Joel Dudley, Frederick Dewey, Shin Lin, Teri Klein, Russ Altman, Atul Butte, Euan Ashley, Tom Quetermous, Mark Gerstein, Kari Nadeau, Hua Tang, Phyllis Snyder

  44. Acknowledgements Human Regulatory Variation: Maya Kasowski, Fabian Grubert, Alex Urban, Alexej A, Chris Heffelfinger, ManojHarihanan, AkwasiAsbere, Lukas Habegger, Joel Rozowsky, Mark Gerstein, Sebastian Waszak, Jan Korbel (EMBL, Heidelberg) Regulome DB: Alan Boyle, ManojHariharan, Yong Cheng, Eurie Hong, Mike Cherry Methylome: Dan Xie, VolodymyrKuleshov, RuiChen, Dmitry Pushkarev, KonradKarczewski, Alan Boyle, Tim Blauwkamp, Michael Kertesz 44

  45. 3. Big Data Handling and Storage Genome (1TB) Epigenome(2TB) Personal Omics Profile Total = 5.74TB/Sample + 1 TB Genome Transcriptome(0.7TB) (mRNA, miRNA, isoforms, edits) Proteome (0.02 TB) Cytokines Metabolome(0.02 TB) Autoantibody-ome Microbiome(3TB)

  46. High Interest Drug-Related Variants

  47. Study of 10 Healthy People5 Asian, 5 EuropeanDewey, Grove, Pan, Ashley, Quertermous et al Median 5 reportable disease risk associations (ACMG) per individual (range 2-6) 3 followup diagnostic tests (range 0-10) Cost $362-$1427 per individual 54 minutes per variant

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