Big Data and Health

Michael Snyder

November 3, 2018

Conflicts: Personalis, Genapsys, SensOmics, Qbio, January

• Focused on Health

• Proactive

• Measure many things

• Frequent?

• Individual based

• Focus on Illness

• Reactive

• Measure very few things

• Infrequent

• Population based

Medicine

Presently Should be

Precision Health

Importance in Individual

Variation from “Normal”

Sund-Levander M. Scand J Caring Sci 2002;122-8. & Souissi N. Chronobio Int 2007;24:739 -48

Oral temp in 2749 healthy individuals

100.8F

99.1F

97.5F

94.6F

92.0F

Food

Health Disease

Genome

Pathogens

Stress

Exercise

Health Is a Product of Genome & Exposome

Enironmental Exposures

Drivers of Big Data

Human Genome Cost <$1K

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)

Omics

Measurements

6

Year 2 …Year 1 Viral infection

Biosensors

Clinical Tests

Questionaires

Stress Echos

Glucose Control

1. Focused on what is healthy and early detection of

disease at an individual level---not population level

2. Understand how individual responses are similar and

differ from one another when faced with specific

perturbations

3. Identify factors that can affect and help manage the

health of an individual

General Goals

7

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

11091124 1164 12001227128413161319 13231331133813601381153

6

156

4

158

9

161

2

162

8

163

1

164

3

168

0

169

5

170

2

170

9

1

7

5

0

1

7

5

7

1

7

6

4

1

7

7

1

1

7

7

4

1

7

7

9

1

7

9

2

1

8

1

5

1

8

2

8

1

8

3

5

1

8

4

2

1

8

4

9

1

8

5

2

1

8

5

9

1

8

7

1

1

8

9

4

Personal Omics Profile

101 months; >230 Timepoints; 12 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Chen et al., Cell 2012, unpublished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

}

No

rma

l Ra

ng

e 3

.8-5

.7%

{

No

rma

l R

an

ge

70

-99

mg

/dL

Extended Time Line

• 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

12Longitudinal Personal DNA Methylome Reveals Epigenomic Signatures of a Chronic

Condition

Transcriptome Changes Many Times

Especially at Viral Infections

Methylome Changes Twice: At Glucose

Misregulation Times

Glucose Homeostasis

Longitudinal Profiling of 107 individuals (Prediabetics &

Healthy) over periods of health, stress and disease

Year 2 …Year 1

Viral infection

Stress

Diet change

Cell Host & Microbiome 2014

Genome Sequencing – First 70 People

• Twelve 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 (2X): Colon cancer• BRCA1: Breast & ovarian cancer

• All have reportable carrier mutations and/or pharmacogenetic variants

Personalis, IncShannon Rego et al.

Metabolic

Infectious

OtherHeme/Onc

Cardiovascular

7 Oncologic Risk Genes

(Thyroid Cancer in 1)

1 Lymphoma (Imaging)

1 MGUS (IgM)

1 Smoldering Myeloma (IgM)

1 α Thalassemia (Clinical)

1 β Thalassemia (Gene/Clinical)

1 Pros1 Mutation (gene)

1 Lyme Disease (wearable)

6 Carotid Plaques (imaging)

1 Atrial Fib. (wearable)

1 RMB20 mutation (gene)

1 Reduced LVEF/GLS (imaging)

3 Dilated L. Atrium (imaging)

1 Pharmagenomic (gene)

1 Sleep Apnea (wearable)

1 SLC7A9 mutation

(cystinuria risk)

2 Macroalbuminuria

1 MODY mutation (gene)

1 ABCC8 Mutation (gene)

14 New Diabetes

47 Major Health Discoveries

Discovery of B-Cell Lymphoma

MGUS PreCancer: High IgM

6 people with

Carotid

Plaque

8.4% 10.3%22.4%9.3%

46.7%

50.5%

4.7%

1.9%

1.9%

77.6%

41.1%25.2%

Self-Report Study Entry Study CourseDiabetes Prediabetes Gestational Only No Diabetes/Normoglycemic

Prediabetes/Diabetes Discovery

Longitudinal Diabetes Trajectories

Initial Abnormality: OGTT Initial Abnormality: FPG

Initial Abnormality: HbA1C Improve then Progress

−20

0

20

40

−60 −40 −20 0 20 40

MDS1

MD

S2

−5.0

−2.5

0.0

2.5

5.0

−8 −4 0 4

MDS1

MD

S2

Cytokines

ClinicLabs

−1000

0

1000

−1000 0 1000

MDS1

MD

S2

−4000

0

4000

8000

−20000 −10000 0 10000

MDS1

MD

S2

Metabolome

Transcriptome

Strong Personal Characteristics

MultiDimensional Scaling for 12 subjects with at least 10 healthy baseline visits

Family Composition

Shana Leopold + George Weinstock

Microbiome clusters by subject, not by IR/IS

status or perturbation

Smart Phone = Control Center

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

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

Acknowledgements

27

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

Monica Avina

Wearables

Xiao Li

Jessie Dunn

Denis Salins

Sophia Miryam …

Heather Hall

Weinstock Lab

George Weinstock

Erica Sodergren

Yanjiao Zhou

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 other

McLaughlin Lab

Tracy McLaughlin

Colleen Craig

Candice Allister

Dalia Perelman

Elizabeth Colbert

Exposome

Chao Jiang

Xin Wang

Jingga Inlora

Ting Wang

Xiyan Li

Genomics and

Personalized Medicine

What Everyone Needs to

Know®

Michael Snyder

Available from Amazon