Aug2013 Mike Snyder the genomics revolution and human health
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Aug2013 Mike Snyder the genomics revolution and human health

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  • BRCA1/BRCA2: Women with significant amily history are screened to determine their risk. Mutations in BRCA1 or BRCA2 in women with breast cancer increases the probability that they will develop secondary cancer of the ovaries. BRCA1 and BRCA2 testing is done to identify those women who need extra surveillance for possible ovarian cancer or prophylactic oophorectomyLQTS – there are a number of genetic mutations identified that affect cardiac ion channel conductance and cause long QT interval and associated increased risk of fatal arrhythmia. Patients with long QT syndrome are tested for which mutation is present. Some mutations increase the risk of arrhythmia associated with auditory stimulation during sleep, others increase the risk associated with exercise; knowing which mutation is present, the individual can be counseled to avoid the relevant trigger. Furthermore, certain drugs work better for some mutations than others, so genetic testing allows tailoring of drug therapyIn May 2004, PGx Health Pharmaceuticals, based in New Haven, CT, introduced the FAMILION™TCF7L2 testing in patients with impaired glucose tolerance is important for identifying those individuals who need aggressive lifestyle modification and drug treatmetn to prevent progression to diabetes
  • NOTE: the grey lines represent the exon boundaries- so exons such as the MYBPC3 one shown here are very, very small and thus potentially not actually an exon
  • 667 VaraintsAffecting 93 genes – half of these have heteroalelic expression

Aug2013 Mike Snyder the genomics revolution and human health Aug2013 Mike Snyder the genomics revolution and human health Presentation Transcript

  • The Genomics Revolution and Human Health Michael Snyder August 15, 2013 Conflicts: Personalis, Genapsys, Illumina
  • Health Is a Product of Genome + Environment Exposome Health Genome
  • Health Is a Product of Genome + Environment Exposome Health Genome
  • • Understand and Treat Disease – Cancer – Mystery diseases • Pharmacogenomics – Determining which drug side effects and doses • Managing Health Care in Healthy Individuals Impact of Genomics on Medicine
  • Personalized Omics Profiling: Combine Genomic and Other Omic Information Genomic Transcriptomic, Proteomic, Metabolomic 1. Predict risk 2. Diagnose 3. Monitor 4. Treat & 5. Understand Disease States GGTTCCAAAAGTTTATTGGATGCCGTT TCAGTACATTTATCGTTTGCTTTGGAT GCCCTAATTAAAAGTGACCCTTTCAAA CTGAAATTCATGATACACCAATGGATA TCCTTAGTCGATAAAATTTGCGAGTAC TTTCAAAGCCAAATGAAATTATCTATG GTAGACAAAACATTGACCAATTTCATA TCGATCCTCCTGAATTTATTGGCGTTA GACACAGTTGGTATATTTCAAGTGACA AGGACAATTACTTGGACCGTAATAGAT TTTTTGAGGCTCAGCAAAAAAGAAAAT GGAAATTAATTTTGAAGTGCCATTGA ….
  • Genome Transcriptome (mRNA, miRNA, isoforms, edits) Proteome Metabolome Personal Omics Profile Autoantibody-ome Microbiome Personal “Omics” Profiling (POP) Cytokines Epigenome
  • Genome Transcriptome (mRNA, miRNA, isoforms, edits) Proteome Metabolome Personal Omics Profile Autoantibody-ome Microbiome Personal “Omics” Profiling (POP) Cytokines Epigenome Initially 40K Molecules/ Measure- ments Now Billions!
  • Personal Omics Profile 40 months; 61 Timepoints; 6 Viral Infections / / Chen et al., Cell 2012
  • Accurate Genome Sequencing 3.3 M Hi conf. SNVs, 217K Indels and 3K SVs 2 or more Platforms (Plus low confidence) Whole Genome Sequencing • Complete Genomics: 35 b paired ends (150X) • Illumina: 100 b paired ends (120X) Exome Sequencing • Nimblegen • Illumina • Aglilent 3.30M 89% 100K 2% 345K 9% CGIllumina
  • Local phasing + population data= highly phased blocks Moleculo: Volodymyr Kuleshov, Michael Kertesz Percent SNPs phased 98.2% Switch accuracy 99.9%+
  • CodingNon-Coding miRNA Splice UTR miRNA targets Seed sequence SIFT PP2 OMIM/Curated Mendelian disease (51) Nonsynonymous (1320) Synonymous mRNA stability tRNA rate I. Highly Penetrant Variants: Mendelian Disease Risk Pipeline Rick Dewey & Euan Ashley Damaging (234) All variants ~3.5M Rare/novel variants (<5%)
  • Missense • ALAD, ABCC2, ACADVL, ADAMTS13, AGRN, BAAT, CDS1, CHD 7, COL4A3, CTSD, DGCR2, DLD, DYSF, EPCAM, FGFR1OP, FKR P, GAA, GNAI2, HSPB1, IGKC, ITPR1, MED12, MKS1, NTRK1, P CM1, PKD1, PLEKHG5, PMS2, PRSS1, PTCH2, SERPINA1, SETX, SYNE1, TERT, TTN, VWF, ZFPM2, PNPLA2. Nonsense • PRAMEF2, PLCXD2, NUP54, RP1L1, PIK3C2G, ND E1, GGN, CYP2A7, IGKC Not Rare But Important • KCNJ11 , KLF4, GCKR … High Cholesterol Aplastic Anemia Rare Variants in Disease Genes (51 Total)
  • Integrate Over Many Markers: Complex Disease 0% 100% Predict Type 2 Diabetes
  • "# )"# %""# %)"# 200 250 300 350 400 450 500 550 600 650# +%""# +)"# 0 )"# %""# %)"# &""# &)"# '""# ')"# (""# ()"# )""# HRV Infection (Day 0-21) RSV Infection (Day 289-311) Life Style Change (Day 380-Current) Glucose(mg/dL) Day Number (Relative to 1st Infection) 80 90 100 110 120 130 140 150 160 -150 Glycated HgA1c (%): (Day Number) 6.4 (329) 6.7 (369) 4.9 (476) 5.4 (532) 5.3 (546) 4.7 (602) GLUCOSE LEVELS HRV INFECTION (DAY 0-21) RSV INFECTION (DAY 289-311) LIFESTYLE CHANGE (DAY 380- CURRENT) 14 HbA1c (%): 6.4 6.7 4.9 5.4 5.3 4.7 (Day Number) (329) (369) (476) (532) (546) (602)
  • Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome george mias RSV 18 days Platelet Plug Formation Glucose Regulation of Insulin Secretion
  • The Future? Genomic Sequencing 1. Predict risk 2. Early Diagnose 3. Monitor 4. Treat Omes and Other Information: Home Sensors http://www.baby-connect.com/ GGTTCCAAAAGTTTATTGGATGC CGTTTCAGTACATTTATCGTTTG CTTTGGATGCCCTAATTAAAAGT GACCCTTTCAAACTGAAATTCAT GATACACCAATGGATATCCTTAG TCGATAAAATTTGCGAGTACTTT CAAAGCCAAATGAAATTATCTAT GGTAGACAAAACATTGACCAATT TCATATCGATCCTCCTGAATTTAT TGGCGTTAGACACAGTTGGTATA TTTA….
  • Study of 10 Healthy People 5 Asian, 5 European Dewey, 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
  • Many Unaddressed Challenges 1) Accuracy and coverage 2) Interpretation 1) Interpreting non-protein coding regions 2) DNA Methylation 5) Sample size 6) Exposome
  • 1) Accurate Genome Sequences and Coverage Whole Genome Sequencing • Complete Genomics: 35 b paired ends (150X) • Illumina: 100 b paired ends (120X) 3.30M 89% 100K 2% 345K 9% CGIllumina Single Nucleotide Variants Getting Better. Indels and Structural Variants Need Work!
  • SNV Comparison • Complete Genomics: 35 b paired ends (150X) • Illumina: 100 b paired ends (120X) 3.30M 89% 100K 2% 345K 9% Complete Genomics Illumina Hugo Lam, Michael Clark, Rui Chen Ti/Tv = 1.68 17/18 Sanger Ti/Tv = 2.14 20/20 Sanger Ti/Tv = 1.40 2/15 Sanger 31 Disease Associated SNP 3 Disease Associated SNP
  • Sequencing Accuracy Sequencing the Same Genome Twice Personalis 146,100 SNPs (3.7%)
  • Exome-seq and WGS-specific detection 45X WGS vs 80X Exome Clark et al. 2011 Nature Biotech
  • Overall Statistics for Finishing Medically Interesting Genes- ACE ACE v1 = Thick Lines TruSeq Exome (10G) = Thin Lines Personalis Normal Exome ~2,000 Custom Exome (ACE) ~2,000
  • Exons Covered by ACE, Missed by Standard Exome Personalis
  • 1) Search for disease causing mutations (highly penetrant) GCKR (high lipids); TERT (aplastic anemia) 2) Sum over multiple common risk allele to predict risk 2. Genome Interpretation Missense Variants ALAD, ABCC2, ACADVL, ADAMTS13, AGRN, BAAT, CDS1, CHD7, COL4A3, CTSD , DGCR2, DLD, DYSF, EPCAM, FGFR1OP, FKRP, GAA, GNAI2, HSPB1, IGKC, ITP R1, MED12, MKS1, NTRK1, PCM1, PKD1, PLEKHG5, PMS2, PRSS1, PTCH2, SERPI NA1, SETX, SYNE1, TERT, TTN, VWF, ZFPM2, PNPLA2. 0% 100% Ashley, Butte et al.
  • Missing Regulatory Variation 88% of Disease Variants Lie Outside of Genes! 26 X Two approaches: 1) Mapping transcription factor binding in different people. 2) RegulomeDB: Assembling regulatory information from the ENCODE Project and other sources.
  • Damaging Variation in an Individual Gene Regulatory region Protein Coding Non-coding and CAPN1: Protective against Alzheimer’s Coding Variants Regulatory Variants
  • 3. Incorporate Methylation Data
  • Possible Phenotypic Consequences of Differentially Methylated Regions?
  • 4. Sample Size—Need to Reduce
  • AliveCor Measures ECG 5. Other Data Types: Sensors 71 Moves App
  • Conclusions 1) Personal genome sequencing is here. The medical interpretation is difficult. 2) Genome sequencing can predict disease risk that can be monitored with other omics information. 3) Integrated analysis can provide a detailed physiological perspective for what is occurring. 4) Every person’s complex disease profile is different and following many components longitudinally may provide valuable information. 5) You are responsible for your own health Data at: snyderome.stanford.edu
  • The Personal Omics Profiling Project Rui Chen, George Mias, Hugo Lam, Jennifer Li-Pook- Than, Lihua Jiang, Konrad Karczewski, Michael Clark, Maeve O’Huallachain, Manoj Hariharan,Yong Cheng, Suganthi Bali, Sara Hillemenyer, Rajini Haraksingh, Elana Miriami, 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
  • Acknowledgements 34 Human Regulatory Variation: Maya Kasowski, Fabian Grubert, Alex Urban, Alexej A, Chris Heffelfinger, Manoj Harihanan, Akwasi Asbere, Lukas Habegger, Joel Rozowsky, Mark Gerstein, Sebastian Waszak, Jan Korbel (EMBL, Heidelberg) Regulome DB: Alan Boyle, Manoj Hariharan, Yong Cheng, Eurie Hong, Mike Cherry Methylome: Dan Xie, Volodymyr Kuleshov, Rui Chen, Dmitry Pushkarev, Konrad Karczewski, Alan Boyle, Tim Blauwkamp, Michael Kertesz
  • Genome (1TB) Transcriptome (0.7TB) (mRNA, miRNA, isoforms, edits) Proteome (0.02 TB) Metabolome (0.02 TB) Personal Omics Profile Total = 5.74TB/Sa mple + 1 TB Genome Autoantibody-ome Microbiome (3TB) 6. Big Data Handling and Storage Cytokines Epigenome (2TB)