Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states. Meet the speaker, Professor Michael Snyder (Stanford): Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.

1. Personal Medicine and the Omics
Revolution
Michael Snyder
September 3, 2014
Conflicts: Personalis, Genapsys, AxioMx

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

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

4. The Cost of DNA Sequencing is Dropping
Human Genome Cost ~$2K
http://www.genome.gov/

5. For each person it is
packaged into 2 copies
of each of 23
chromosomes
Our DNA Has 6
Billion letters of a 4
Letter Code
(Genome)
From: www.ikkeweer.net

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

8. 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

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

10. Cancer Genome Sequencing
1) 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

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

12. 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

13. Solving Mystery Diseases: Child With Variety of
Conditions
Developmentally Delayed, Significant Health Issues
F M
A1
Mother
SNVs: 3,125,880
Private: 581,754
Indels: 723,379
Father
SNVs: 3,119,588
Private: 596,691
Indels: 750,522
Child
SNVs: 3,118,638
Private: 33,158
Indels: 673,809
SNVs: Single
nucleotide variants
Indels: =
Insertions/deletions
(~<100bp)
Candidates:
TCP10L2,
SUPV3L1, PIEZO1
DNAH2, NGLY1,
FANCA, WFS1

14. Lessons Learned
Overall success rate for identifying causative mutations
is low 25%
What is Needed
1) Large database to share information:
Recurrence is key. ClinVar
2) Functional information to determine which
variant is likely causative

15. Fetal DNA Sequencing
1) Cell free fetal DNA can be detected in maternal
blood as early a 4-5 weeks gestation
2) 4-10% circulating DNA is fetal  increases with
pregnancy
3) Targeted detection of mutations
4) Whole genome sequencing routinely used to detect
trisomies: Down’s (Chr. 21), Chromosome 18 and
Chromosome 13. 99% sensitivity
5) Taking over from aminocentesis

16. Fetal DNA Sequencing
Srinivasan A, Bianchi DW, Huang H, et al. Am J Hum Genet 2013; 1–10.

17. Personal Genome Sequencing:
Can genome sequencing of a healthy
person be useful in health care?

18. Sequencing Genomes of Healthy People:
Incorporate into Medicine
Genomic
1. Predict risk
2. Diagnose
3. Monitor
4. Treat &
5. Understand
Disease States
GGTTCCAAAAGTTTATTGGATGCCGTTTCA
GTACATTTATCGTTTGCTTTGGATGCCCTA
ATTAAAAGTGACCCTTTCAAACTGAAATTC
ATGATACACCAATGGATATCCTTAGTCGAT
AAAATTTGCGAGTACTTTCAAAGCCAAATG
AAATTATCTATGGTAGACAAAACATTGACC
AATTTCATATCGATCCTCCTGAATTTATTG
GCGTTAGACACAGTTGGTATATTTCAAGTG
ACAAGGACAATTACTTGGACCGTAATAGAT
TTTTTGAGGCTCAGCAAAAAAGAAAATGGA
AATTAATTTTGAAGTGCCATTGA….
Family History
Medical Tests:
Few Tests (<20)

19. Personalized Medicine: Combine Genomic
and Other Omic Information
Genomic Transcriptomic, Proteomic,
Metabolomic
1. Predict risk
2. Diagnose
3. Monitor
4. Treat &
5. Understand
Disease States
GGTTCCAAAAGTTTATTGGATGCCGTTTCA
GTACATTTATCGTTTGCTTTGGATGCCCTA
ATTAAAAGTGACCCTTTCAAACTGAAATTC
ATGATACACCAATGGATATCCTTAGTCGAT
AAAATTTGCGAGTACTTTCAAAGCCAAATG
AAATTATCTATGGTAGACAAAACATTGACC
AATTTCATATCGATCCTCCTGAATTTATTG
GCGTTAGACACAGTTGGTATATTTCAAGTG
ACAAGGACAATTACTTGGACCGTAATAGAT
TTTTTGAGGCTCAGCAAAAAAGAAAATGGA
AATTAATTTTGAAGTGCCATTGA….

20. Personal “Omics” Profiling (POP)
Genome
Epigenome
Transcriptome
(mRNA, miRNA, isoforms, edits)
Proteome
Cytokines
Metabolome
Personal
Omics
Profile
Autoantibody-ome
Microbiome (Gut, Urine,
Nasal, Tongue, Skin)

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

22. Personal Omics Profile
53 months; 80 Timepoints; 6 Viral Infections
/
/
Chen et al., Cell 2012

23. Genome Sequence (Ilumina, Complete Genomics)
Predict Type 2
Diabetes
0% 100%
Rong Chen
and Atul Butte
160
150
140
130
120
110
100
90
++)%"#)"# +"%# ""#)"+#)"#%""0# %)")#"#200%""2#50%)"3#00&"3"5#0 &)4"0#0 '""4#50 ')5"#00 ("5"5#0 (6)"0#0 )6"5"0#
HRV Infection
RSV Infection
Life Style Change
Glucose (mg/dL)
Day Number (Relative to 1st Infection)
80
-150
Glycated HgA1c (%):
(Day Number)
6.4
(329)
6.7
(369)
4.9
(476)
5.4
(532)
5.3
(546)
4.7
HbA1c (%) 6.4 6.7 4.9 5.4 5.3 ( 6 402.7)
(Day Number) (329) (369) (476) (532) (546) (602)
HRV RSV
LIFESTYLE
CHANGE
Glucose levels

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

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

26. Study of 12 Healthy People
7 Asian, 5 European
Median 5 reportable disease risk associations
(ACMG + others) per individual (range 2-6)
3 Followup diagnostic tests (range 0-10)
Cost $400-$1400 per individual
54 minutes per variant
One individual had a BRCA1 deletion/frameshift
mutation—no known family history!
Dewey et al JAMA 2014

27. Many Unaddressed Challenges
1) Interpreting regulatory/non protein coding
regions
2) DNA Methylation
3) Microbiome
4) Exposome

28. Possible Phenotypic Consequences of
Differentially Methylated Regions?
DNA Methylation: Epigenetic Changes

29. Personal Omics Profile
53 months; 80 Timepoints; 6 Viral Infections
/
/
*
Chen et al., Cell 2012

30. Nasal microbes
--Top 25 most abundant microbial
species
Fever Recovery Healthy

31. Gut microbiome temporal profiles
18%
2%
1%
13%
26%
14%
10%
11%
5%
-- At the family level analyzed by RTG
26%
1%
15% 0%
12%
20%
3%
11%
3% 9%
50%
6%
14%
1% 1%
3%
1%
6%
15%
3%
46%
9%
9%
7%
1% 1%
2%
11%
10%
4%
38%
1%
0%
7%
1% 6%
28%
9%
6%
4%
33%
0%
1%
1% 7%
33% 8%
9%
5%
3%
43 57 58 58b 59
43_2
Fever Recovery Healthy
18%
2%
1%
13%
26%
14%
10%
11%
5%
Bacteroidaceae
Clostridiaceae
Erysipelotrichaceae
Eubacteriaceae
Lachnospiraceae
Porphyromonadaceae
Prevotellaceae
Rikenellaceae
Ruminococcaceae
2%
18% Others
1%
13%
26%
14%
10%
11%
5%
Bacteroidaceae
Clostridiaceae
Erysipelotrichaceae
Eubacteriaceae
Lachnospiraceae
Porphyromonadaceae
Prevotellaceae
Healthy
Wenyu Zhao, George Weinstock

32. AliveCor
Other Data Types: Sensors
Measures ECG
71
Moves App
71
Basis
Measures
Heart Rate
Sleep
Stress

33. Personal “Omics” Profiling (POP)
~60 Prediabetics
Genome
Epigenome
Transcriptome
(mRNA, miRNA, isoforms, edits)
Proteome
Cytokines
Metabolome
Personal
Omics
Profile
Autoantibody-ome
Microbiome (Gut, Urine,
Nasal, Tongue, Skin)
Sensors

34. Overfeeding Study
20 Individuals
30
days
60
days
7
days
90
days
• 17 participants have completed study so far
• Gained an average of 7.1 pounds
• Blood sampling at indicated timepoints

35. Big Data Handling and Storage
Genome (1TB)
Epigenome (3TB)
Transcriptome (0.7TB)
(mRNA, miRNA, isoforms, edits)
Proteome (0.02 TB)
Cytokines
Metabolome (0.02 TB)
Personal
Omics
Profile
Total =
5.76TB/Sa
mple +
1 TB
Genome
Autoantibody-ome
Microbiome (2TB)
Devices (2 GB/year)

36. The Future?
Genomic Sequencing
Omes and Other Information:
Home Sensors
1. Predict risk
2. Early Diagnose
3. Monitor
4. Treat
GGTTCCAAAAGTTTATTGGATGCCGT
TTCAGTACATTTATCGTTTGCTTTGG
ATGCCCTAATTAAAAGTGACCCTTTC
AAACTGAAATTCATGATACACCAATG
GATATCCTTAGTCGATAAAATTTGCG
AGTACTTTCAAAGCCAAATGAAATTA
TCTATGGTAGACAAAACATTGACCAA
TTTCATATCGATCCTCCTGAATTTAT
TGGCGTTAGACACAGTTGGTATATTT
A….
iPS Cells
http://www.baby-connect.com/

37. Conclusions
1) Omics technologies are revolutionizing science
and medicine.
2) Personal sequencing and profiling can provide
valuable insights into medicine
3) Integrated omics analysis can provide a detailed
physiological perspective for what is occurring.
1) Every person’s complex disease profile is different
and following many components longitudinally
may provide valuable information.
2) You are responsible for your own health
Data at: snyderome.stanford.edu

38. New Online Professional
Certificate Program
For more information visit
http://geneticscertificate.stanford.edu

39. The Personal Omics Profiling Project
Rui Chen, George Mias, Hugo Lam, Jennifer Li-Pook-Than,
Lihua Jiang, …, Russ Altman, Atul Butte, Euan Ashley, Tom
Quertermous, Mark Gerstein, Kari Nadeau, Hua Tang, Phyllis
Snyder
GTEx and Human Variation
Linfeng Wu, Lihua Jiang, Maya Kasowski, Fabian Grubert, Anshul
Kundaje, Sophia K. Judith Zaugg
Phase 2 HMP:
Wenyu Zhou, Kim Kukurba, Brian Pienning,
Colleen Craig, Lihua Jiang, Sid Mitra, George
Weinstock, Tracey McLaughlin
Methylome:
Dan Xie, Volodymyr Kuleshov, Rui Chen, Dmitry
Pushkarev, Konrad Karczewski, Alan Boyle, Tim
Blauwkamp, Michael Kertesz

40. Further Information:
Stanford Genetics and Genomics Certificate
Program
Learn about genetics, genomics and personalized
medicine
http://geneticscertificate.stanford.edu/certificate-overview.
php

42. DNA Methylation
• Associated with gene silencing
• Affected by nutrition, aging etc.
Deep Sequencing: two time points analyzed
• ~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
5 methylC