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