Using Omics and Big Data to Manage Health and

Disease

Michael Snyder

Stanford University

May 19, 2017

Conflicts: Personalis, Genapsys, SensOmics, QbioFood

Health Disease

Genome

PathogensStress

Exercise

Health Is a Product of Genome & Exposome

Drivers of Big Data

Human Genome Cost <$2K

126.90 127.00 127.10 127.20m/z

0

10

20

30

40

Rela

tive A

bu

nd

an

ce

127.0613

1) DNA Sequencing

2) Mass Spectrometry 3) Wearables

Genome

Transcriptome

Proteome

Metabolome

Lipidomics

Autoantibody-ome

Personal Omics Profiling

Cytokines

Epigenome

Billions of

Measurements!

Microbiome (Gut, Urine, Nasal,

Tongue, Skin)

Medical Tests, Questionaires

Omics

Measurements

4

Year 2 …Year 1 Viral infection

Biosensors

1) Understand how individuals change over time and

during periods of health and disease at high resolution

1) Understand how different “omes” (microbiome,

metabolome, proteome, genome) relate to one another

dynamically

1) Understand how individual responses are similar and

differ from one another when faced with specific

perturbations

2) Identify factors that can affect and help manage the

health of an individual

General Goals

5

Year 2 …Year 1Viral infection

912

Adenovirus Infection

694679 683 688 700680

711 735

796 840

Adenovirus Infection

944 948 984945 959 966

HRV Infection

1030 10381029 1032 1045 1051 1060

400186185

255

116

369

380

329322

Day from 1st HRV

Infection (D)

RSV Infection

297 301289 292 294 307 311290

HRV Infection

4 210

476 546532

HRV Infection

625615 618 620 630616

602 647-123

Day from 1st HRV

Infection (D)

14151453

1487

15161526

HRV Infection

1714

1720 17431716 1723 1729

Infection

19081906

110911241164 12001227128413161319 132313311338136013811536

1564

1589

1612

1628

1631

1643

1680

1695

1702

1709

17

50

17

57

17

64

17

71

17

74

17

79

17

92

18

15

18

28

18

35

18

42

18

49

18

52

18

59

18

71

18

94

Personal Omics Profile

84 months; >250 Timepoints; 10 Viral Infections

Chen et al., Cell 2012, unpublished

Skin Rash

Genome Sequence (Ilumina, Complete Genomics)

Predict Type 2

Diabetes

Rong Chen

and Atul Butte

0% 100%

+)"# "# )"# %""# %)"# 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)

Glu

co

se

(m

g/d

L)

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)HbA1c (%) 6.4 6.7 4.9 5.4 5.3 4.7

(Day Number) (329) (369) (476) (532) (546) (602)

RSVHRVLIFESTYLE

CHANGE

Glucose levels

*

*

**

*Previously known

HRV

Exercise

RSVSkinRash/Itch

HRV

Adenovirus

HRVHRV

Changed life style

}

Norm

al R

ange 3

.8-5

.7%

{

Norm

al R

ange 7

0-9

9 m

g/d

L

Extended Time Line

Molecules and Biochemical Pathways that Change

During Acquisition of Diabetes

george miasRSV 18 days

Platelet Plug

Formation

Glucose Regulation

of Insulin Secretion

Insulin

Biosynthetic

Pathway• Affected by nutrition, lifestyle factors,

aging, and environment

• Causes gene silencing

Map all the methylated sites using

whole genome bisulfite sequencing

Epigenetics: DNA Methylation

5 methylC

Father

Mother

T CInactivated by

mutation

Inactivated by

DNA

MethylationM MM

Methylated

CpGs

Few RNAs

Lots of RNAPDE4 DIP Gene

Gene Inactivation by Mutation and

Methylation: PDE4 involved in eosinophilia

Kim Kukurba

Longitudinal Profiling of 100 individuals (Prediabetics &

Healthy) over periods of health, stress and disease

Year 2 …Year 1

Viral infection

Stress

Diet change

Cell Host & Microbiome 2014

100>1500 collections thus far

Most Datasets are Open Access!

Stu

dy p

art

icip

an

t

Genome Sequencing – First 60 People

• Eleven have important pathogenic mutations:• SHBD (2X): high freq. of neuroendocrine tumors• PROC: Affects coagulation• HNF1A: MODY mutation• ABCC8: Hyperinsulinemic hypoglycemia• MUTYH: Colon cancer• SLC7A9: Cystinuria• RBM20: Dilated cardiomyopathy• CHEK2: Breast cancer• APC: Colon cancer• BRCA1: Breast & ovarian cancer

• All have reportable carrier mutations and/or pharmacogenetic variants

Personalis, IncShannon Rego et al.

A subset of individuals undergo a dietary

perturbation.

30

days

60

days7

days

23 participants:

• 13 Insulin resistant

• 10 healthy controls (BMI matched)

Brian Piening, Wenyu Zhou, Gucci Gu, Kevin Contrepois

Metabolic differences between IR and IS

Univariate analysis: Wilcoxon ttest pvalue < 0.05 and fold change > 1.5

Me

tab

olite

s

Participants at T1

Example data: Short-term weight gain

bio

mo

lecu

le

s

Integrative c-means clustering: pattern

recognition across RNA-seq, proteome,

metabolome, microbiome, cytokines

PATTERN1: UP AT PEAK

WEIGHT THEN DOWN

KEGG: HYPERTROPHIC

CARDIOMYOPATHY (q<0.001)

Normalized log2 plasma concentration

Blood cytokine profiles: 20 subjects at baseline

Insulin Sensitive

Insulin Resistant

Microbial abundance

pattern group by

individual, not by dietary

supplement

-- Distance matrix by

Manhattan methods and

Hierarchical clustering by Ward

method

Basis B1

Basis Peak

Apple Watch

Dexcom Constant Glucose Monitor

Withthings Smart Scale

Qardio Blood Pressure Cuff

Scanadu Scout

Autographer – Life Logger

iHealth Pulse Oximeter

Athos – Smart Shorts

RadtargeRadiation

Sensors: Measure Many

ThingsOverview of the project

Li, Dunn et al.

PloS Biol 2017

Circadian and diurnal patterns

Li, Dunn et al.

PloS Biol 2017

Δ S

kin

Tem

p (

Nig

ht –

Day

)

Δ H

R

(Nig

ht –

Day

)

43 People

Activity Phenotypes: 4 Patterns

h

Accele

rom

ete

r M

ag

nit

ud

e

Hour of Day

Li, Dunn et al.

PloS Biol 2017

SpO2

Levels

Drops During

Airline Flights

Finger device

91-96%: 65.4%

90 or less: 5.1%

SpO2 levels drops on airplane flights:

18 Subjects

Li, Dunn et al.

PloS Biol 2017

SpO2

Measurements

Associated

With Fatigue

Li, Dunn et al.

PloS Biol 2017

SpO2

Measurements

Can Adapt on

Flights >7hrs

SpO2 Measurements: Unusually

Low on One Flight to Norway

Li, Dunn et al.

PloS Biol 2017

Elevated Heart Rate and Skin

Temperature During Lyme DiseaseLyme Disease

Early detection of Lyme disease

% o

f o

utlie

r (H

R)

% o

f o

utlie

r (S

kin

te

mp

.)

458 470 474462 466 478 482 486 490 494Day

Detects All Days of IllnessChange-of-Heart

Algorithm

Detects Periods of Illness

at High Resolution using

Wearable Device

HR differences in IR and Insulin Sensitivity Smart Phone = Control Center

Share Information With

Physician

The Future?

Genomic Sequencing

1. Predict risk

2. Early Diagnose

3. Monitor

4. Treat

GGTTCCAAAAGTTTATTGGATGCCGT

TTCAGTACATTTATCGTTTGCTTTGG

ATGCCCTAATTAAAAGTGACCCTTTC

AAACTGAAATTCATGATACACCAATG

GATATCCTTAGTCGATAAAATTTGCG

AGTACTTTCAAAGCCAAATGAAATTA

TCTATGGTAGACAAAACATTGACCAA

TTTCATATCGATCCTCCTGAATTTAT

TGGCGTTAGACACAGTTGGTATATTT

A….

Amanda Mills

Omes & Sensors: Personal Devices

Overall Summary

1) Personal genome sequencing is here. It can be used to predict disease risk and manage health

2) Multi-omics analyses are valuable for determining pathways and biochemical activities involved in human disease.

3) Longitudinal profiles are very valuable for understanding personal disease states

4) Everyone’s profile is different

5) Wearables will be useful for managing health

6) Individuals will be responsible for their own health

Acknowledgements

40

Snyder Lab

Wenyu Zhou

Brian Piening

Kevin Contrepois

Tejaswini Mishra

Kim Kukurba

Shannon Rego

Jessica Sibal

Hannes Rost

Varsha Rao

Liang Liang

Tejas Mishra

Christine Yeh

Hassan Chaib

Eric Wei

Wearables

Xiao Li

Jessie Dunn

Sophia Miryam …

Denis Salins

Heather Hall

Weinstock Lab

George Weinstock

Erica Sodergren

Shana Leopold

Daniel Spakowicz

Blake Hanson

Eddy Bautista

Lauren Petersen

Lei Chen

Benjamin Leopold

Sai Lek

Purva Vats

Jon Bernstein

NIH

Lita Proctor

Salvatore Sechi

Jon LoTempio

And otherMcLaughlin Lab

Tracy McLaughlin

Colleen Craig

Candice Allister

Dalia Perelman

Elizabeth Colbert

Genomics and

Personalized Medicine

What Everyone Needs to

Know®

Michael Snyder

Available from Amazon