genomics and personal medicine
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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. - PowerPoint PPT PresentationTRANSCRIPT
Genomics and Personal Medicine
Michael Snyder
July 25, 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
The Cost of DNA Sequencing is Dropping
Human Genome Cost ~$3Khttp://www.genome.gov/
Outline of Lecture1) Introduction to Genome Variation and Sequencing Human Genomes
2) Impact of Genomics on Treating Disease
3) Impact of Genomics on Heathy People
Genetic Variation Among People: Three
Types
3.7 Million/person
2) Short Indels (Insertions/Deletions 1-100
bp)
GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG
1) Single nucleotide variants(SNVs)
GATTTAGATCGCGATAGAGGATTTAGA------TAGAG
300-600K/person
People Also Have Large Blocks of DNA that are Inserted, Deleted or Flipped Around =
Structural Variants
*
- People Have 3000 differences Relative to the Reference Human Genome Sequence
- Likely responsible for much human differences and disease
- 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
Human Genome Project
New Machines
- Sequence ~1 trillion bases per run~35 genomes at once
- Genome Sequencing Cost: $3,000
- Machine Cost: $800,000
A Personal Genome Sequence is Determined by Comparing to a Reference Genome
Sequence
Snyder et al. Genes Dev 2010;24:423-431
30X: 75-100 b
Reveals 3.7 M SNPs
Map to Reference Genome35-40X: 101 b
Examples of People Who Have had Their Genomes Sequenced
From: www.genciencia.com
Jim Watson Craig Ventor Ozzy Osbourne
sciencewithmoxie.blogspot.com.au/2010_11_01_archive.html
• 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
Cancer Genome Sequencing1) Cancer is a genetic disease: both inherited and
somatic
Vogelstein et al., March Science, 2013
2) 10-20 “driver” mutations
3) Every cancer is unique
4) Sequence genomes (cancer tissue and normal) find genetic changes and suggest possible therapies
Patient with Metastatic Colon Cancer
Chromosome 7: Two amplification regions
Chr 7p arm Chr 7q armGenomic Copy
Number
CEN
EGFR CDK6
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
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
From Richard Gibbs, Baylor
X
Sequencing Genomes of Healthy People:Incorporate into Medicine
Genomic
1. Predict risk2. Diagnose3. Monitor4. Treat &5. UnderstandDisease States
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA….
Family HistoryMedical Tests:Few Tests (<20)
Personalized Medicine: Combine Genomic and Other Omic Information
Genomic Transcriptomic, Proteomic, Metabolomic
1. Predict risk2. Diagnose3. Monitor4. Treat &5. UnderstandDisease States
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA….
Genome
Transcriptome(mRNA, miRNA, isoforms, edits)
Proteome
Metabolome
PersonalOmicsProfile
Autoantibody-ome
Microbiome
Personal “Omics” Profiling (POP)
Cytokines
Epigenome
Genome
Transcriptome(mRNA, miRNA, isoforms, edits)
Proteome
Metabolome
PersonalOmicsProfile
Autoantibody-ome
Microbiome
Personal “Omics” Profiling (POP)
Cytokines
Epigenome
Initially 40K
Molecules/Measure-
ments
Now Billions!
Personal Omics Profile39 months; 62 Timepoints; 6 Viral Infections
/
/
Chen et al., Cell 2012
Accurate Genome Sequencing
3.3 M Hi conf. SNVs, 217K Indels and 3K SVs2 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
3.30M89%
100K2%
345K9%
CGIllumina
Genome Phasing: Assign Variants to Parental ChromosomesInitially Used Mother’s DNA
Percent SNPs phased ~80%
Variants
MP
CodingNon-Coding
miRNA Splice UTR
miRNA targets
Seedsequence SIFT PP2
OMIM/Curated Mendelian disease
(51)
Nonsynonymous(1320)
Synonymous
mRNA stability
tRNA rate
Approach I: Mendelian Disease Risk Pipeline
Rick Dewey & Euan Ashley
Damaging(234)
All variants~3.5M
Rare/novel variants (<5%)
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.
Bolded Genes expressed in PBMC (RNA).
Nonsense• PRAMEF2, PLCXD2, NUP54, RP1L1, PIK3C2G,
NDE1, GGN, CYP2A7, IGKC
Not Rare But Important• KCNJ11 , KLF14, GCKR …
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 , KLF4, GCKR …
Diabetes
High Cholesterol
Aplastic Anemia
Rare Variants in Disease Genes (51 Total)
Approach II: Complex Disease Risk Profile Using VariMed
Rong Chen & Atul Butte
0% 100%
**
GLUCOSE LEVELS
HRV INFECTION(DAY 0-21)
RSV INFECTION(DAY 289-311)
LIFESTYLE CHANGE(DAY 380-
CURRENT)28
HbA1c (%): 6.4 6.7 4.9 5.4 5.3 4.7 (Day Number) (329) (369) (476) (532) (546) (602)
Expression of 50 Cytokines?HRV RSV
DAY 0 DAY 0 DAY 12
Many SNVs are Expressed
RNA 2.67 B 100 b PE reads30,963 (40 reads or more)
1,797 nonsynonymous8 nonsense
Protein>130 Hi Confidence
Allele Specific Expression
Jennifer Li-Pook-Than
RNA Editing2,376 Hi confidence
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
Integrated Analysis of Proteome, Transcriptome, Metabolome Dynamics: Overall trend
george mias RSV
Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome
george mias RSV 18 days
Platelet Plug Formation
Glucose Regulation of Insulin Secretion
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
Many Unaddressed Challenges1) Interpreting regulatory/non protein coding
regions
2) DNA Methylation
3) Complex Cells
4) Large Volume Used
5) Microbiome
6) Exposome
Modified Cytosines: Usually associated with gene inactivation
• Deep Sequencing: two time points analyzeda) 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
DNA Methylation
Incorporate Methylation Data
Possible Phenotypic Consequences of Differentially Methylated Regions?
AliveCor Measures ECG
2. Other Data Types: Sensors
71
Moves App
71
The Future?
Genomic Sequencing
1. Predict risk2. Early Diagnose3. Monitor4. Treat
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTA….
Omes and Other Information
http://www.baby-connect.com/
Conclusions1) 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) Regulatory information is variable among humans; it and DNA methylation data needs to be incorporated into genome interpretation
5) Every person’s complex disease profile is different and following many components longitudinally may provide valuable information.
Final Conclusion
6) 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
44
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)
PersonalOmicsProfileTotal =5.74TB/
Sample + 1 TB
GenomeAutoantibody-ome
Microbiome (3TB)
3. Big Data Handling and Storage
Cytokines
Epigenome (2TB)
Gene SNP Patient genotype
Drug(s) Affected
rs10811661 C/T Troglitazone (Increased Beta-Cell Function)
CYP2C19 rs12248560 C/T Clopidogrel (Increased Activation)
LPIN1 rs10192566 G/G Rosiglitazone (Increased Effect)
SLC22A1 rs622342 A/A Metformin (Increased Effect)
VKORC rs9923231 C/T Warfarin (Lower Dose Required)
High Interest Drug-Related Variants
Study of 10 Healthy People5 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