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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Chen et al., Cell 2012, unpublished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
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