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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.

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genomics and personal medicine

Genomics and Personal Medicine

Michael Snyder

July 25, 2013

Conflicts: Personalis, Genapsys, Illumina

slide4

The Cost of DNA Sequencing is Dropping

Human Genome Cost ~$3K

http://www.genome.gov/

slide5

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

genetic variation among people three types

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

slide7

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
slide8

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

slide9

New Machines

- Sequence ~1 trillion bases per run~35 genomes at once- Genome Sequencing Cost: $3,000- Machine Cost: $800,000
slide10

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

slide11

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

impact of genomics on medicine
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?
cancer genome sequencing
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

patient with metastatic colon cancer
Patient with Metastatic Colon Cancer

Chromosome 7: Two amplification regions

EGFR

CDK6

Chr 7p arm

Chr 7q arm

Genomic Copy Number

CEN

each cancer is unique containing private novel variants
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
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

slide17

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

slide18

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

slide19

Personal “Omics” Profiling (POP)

Genome

Epigenome

Transcriptome

(mRNA, miRNA, isoforms, edits)

Personal

Omics

Profile

Proteome

Cytokines

Metabolome

Autoantibody-ome

Microbiome

slide20

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

slide21

Personal Omics Profile

39 months; 62 Timepoints; 6 Viral Infections

/

/

Chen et al., Cell 2012

accurate genome sequencing
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)

genome phasing assign variants to parental chromosomes initially used mother s dna
Genome Phasing: Assign Variants to Parental ChromosomesInitially Used Mother’s DNA
  • Variants

M

P

slide24

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

curated list of rare variants snvs all heterozygous
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).

slide26

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 …
slide28

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)

slide29

Expression of 50 Cytokines

?

HRV

RSV

DAY 0

DAY 0

DAY 12

many snvs are expressed
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

transcriptome proteome metabolome analysis summary processing steps
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
Integrated Analysis of Proteome, Transcriptome, Metabolome Dynamics: Overall trend

RSV

george mias

dynamical outcomes for integrated analysis of proteome transcriptome metabolome
Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome

Glucose Regulation of Insulin Secretion

Platelet Plug Formation

georgemias

RSV

18 days

slide34

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 challenges

Many Unaddressed Challenges

Interpreting regulatory/non protein coding regions

DNA Methylation

Complex Cells

Large Volume Used

Microbiome

6) Exposome

slide36

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
2 other data types sensors
2. Other Data Types: Sensors

71

71

Moves App

AliveCor Measures ECG

slide40

The Future?

Genomic Sequencing

Omes and Other Information

GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTA….

1. Predict risk

2. Early Diagnose

3. Monitor

4. Treat

http://www.baby-connect.com/

conclusions

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.

final conclusion

Final Conclusion

6) You are responsible for your own health

Data at: snyderome.stanford.edu

the personal omics profiling project
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

acknowledgements

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

slide45

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)

study of 10 healthy people 5 asian 5 european dewey grove pan ashley quertermous et al

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