personal data: using omics profiling and big data to...
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Personal Data: Using Omics Profiling and Big Data to Manage Health and Disease
Michael Snyder
Stanford University November 16, 2016
Conflicts: Personalis, Genapsys, SensOmics
Precision Medicine is of High Interest
January 2015
Food
Health Disease
Genome
Pathogens Stress
Exercise
Health Is a Product of Genome & Exposome
Drivers of Big Data
http://www.genome.gov/
Human Genome Cost <$2K
126.90 127.00 127.10 127.20 m/z
0
10
20
30
40
Rel
ativ
e Ab
unda
nce
127.0613
DNA Sequencing
Mass Spectrometry
Genome
Transcriptome
Proteome
Metabolome Lipidomics
Personal Omics Profile
Autoantibody-ome
Personal “Omics” Profiling (POP)
Cytokines
Epigenome
Initially 40K
Molecules/ Measurements
Now Billions!
Microbiome (Gut, Urine, Nasal, Tongue, Skin)
Medical Tests Questioniares
Sensors
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
6
Year 2 … Year 1 Viral infection
912
Adenovirus Infection
694 679 683 688 700 680
711 735
796 840
Adenovirus Infection
944 948 984 945 959 966
HRV Infection
1030 1038 1029 1032 1045 1051 1060
400 186 185
255
116
369
380
329 322
Day from 1st HRV Infection (D)
RSV Infection
297 301 289 292 294 307 311 290
HRV Infection
4 21 0
476 546 532
HRV Infection
625 615 618 620 630 616
602 647 -123
Day from 1st HRV Infection (D)
1415 1453 1487
1516 1526
HRV Infection
1714
1720 1743 1716 1723 1729
Infection
1908 1906
1109 1124 1164 1200 1227 1284 1316 1319 1323 1331 1338 1360 1381 1536
1564
1589
1612
1628
1631
1643
1680
1695
1702
1709
1750
1757
1764
1771
1774
1779
1792
1815
1828
1835
1842
1849
1852
1859
1871
1894
Personal Omics Profile ~79 months; >200 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Chen et al., Cell 2012, unpublished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%
HbA1c (%) 6.4 6.7 4.9 5.4 5.3 4.7 (Day Number) (329) (369) (476) (532) (546) (602)
RSV HRV LIFESTYLE CHANGE
Glucose levels
*
*
* *
* Previously known
HRV
Exercise
RSV SkinRash/Itch
HRV
Adenovirus HRV HRV
Changed life style
}
Norm
al Range 3.8-5.7%
{
Nor
mal
Ran
ge 7
0-99
mg/
dL
Extended Time Line
Molecules and Biochemical Pathways that Change During Acquisition of Diabetes
george mias RSV 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 C Inactivated by
mutation
Inactivated by DNA
Methylation M M M
Methylated CpGs
Few RNAs
Lots of RNA
PDE4 DIP Gene
Gene Inactivation by Mutation and Methylation: PDE4 involved in eosinophilia
Kim Kukurba
Your Microbiome is Important for Your Health
• We have 10 Trillion human cells, but 10X more bacteria
• Helps digest food you eat • Makes essential vitamins, eg, B12
• Implicated in Inflammatory Bowel Disease (Crohn’s and ulcerative colitis), Diabetes, Obesity, MI
912
Adenovirus Infection
694 679 683 688 700 680
711 735
796 840
Adenovirus Infection
944 948 984 945 959 966
HRV Infection**
1030 1038 1029 1032 1045 1051 1060
400 186 185
255
116
369
380
329 322
Day from 1st HRV Infection (D)
RSV Infection
297 301 289 292 294 307 311 290
HRV Infection
4 21 0
476 546 532
HRV Infection
625 615 618 620 630 616
602 647 -123
Day from 1st HRV Infection (D)
1415 1453 1487
1516 1526
HRV Infection
1714
1720 1743 1716 1723 1729
Infection
1908 1906
1109 1124 1164 1200 1227 1284 1316 1319 1323 1331 1338 1360 1381 1536
1564
1589
1612
1628
1631
1643
1680
1695
1702
1709
1750
1757
1764
1771
1774
1779
1792
1815
1828
1835
1842
1849
1852
1859
1871
1894
Personal Omics Profile 79 months; >200 Timepoints; 10 Viral Infections
Chen et al., Cell 2012, unpublished
Skin Rash
Nasal microbes --Top 25 most abundant microbial species
Healthy
Fever Recovery
Streptococcus pneumoniae
Identifying the Microbe Causing Illness
Wenyu Zhou George Weinstock
57 58 58b 59 43
43_2
Healthy Fever Recovery
Gut microbiome temporal profiles -- At the family level analyzed by RTG
Healthy
Wenyu Zhou, George Weinstock
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
>1300 collections thus far
148
Most Datasets are Open Access!
Stu
dy p
artic
ipan
t
Genome Sequencing – First 48 People • Eight have important mutations to know about
• SHBD (2X): high freq. of paraganglioma • PROC: Affects coagulation • RBM20: cardiomyopathy • HNF1A: MODY mutation • SLC7A9: Cystinuria • ABCC8: Hyperinsulinemic hypoglycemia • MUTYH: Colon cancer
• All have carrier mutations and
pharmacogenetic variants
Personalis, Inc
Shannon Rego et al.
A subset of individuals undergo a dietary perturbation.
30 days
60 days
7 days
24 participants: • 13 Insulin resistant • 11 healthy controls (BMI matched)
Brian Piening, Wenyu Zhou, Gucci Gu, Kevin Contrepois
Baseline gene expression differences between IR and IS in blood PBMCs
tophat->cufflinks->rankGSEA
Insulin Resistant Insulin Sensitive
Maturity Onset Diabetes of the Young (e.g. HHEX) q<0.0001
-3 -2 -1 0 1 2 3
Oxidative Phoshorylation (e.g. COX5A, COX5B) q<0.0001
Defensins (e.g. CCR2, CCR6) q<0.0001 Platelet-Specific Genes (e.g. CXCL5, PF4V1) , q<0.001
Ribosome (e.g. RPL9, RPS7) q<0.0001
Olfactory Signaling (e.g. OR10AD1, OR1K1) q<0.0001 FGFR Binding and Activation (e.g. FGFR1, KLB) q<0.015 EGF Signaling (e.g. EGR2, EGR3, FOSL1, JUN) q<0.017
test statistic
Metabolic differences between IR and IS Univariate analysis: Wilcoxon ttest pvalue < 0.05 and fold change > 1.5
Metabolites
Participants at T1
Example data: Short-term weight gain bi
omol
ecul
es
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
Participants’ fecal 16s microbial profiles stratified by Gender
-- For all quarterly visits that had 16s profiling completed
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
Healthy
Overweight
Sick
Perturbation Outcome
Understanding effects at an individual level
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
Radtarge Radiation
Sensors: Measure Many Things
Early Detection of Lyme Disease
Heart Rate
Skin Temp.
Skin Temp.
The Future? Genomic Sequencing
1. Predict risk 2. Early Diagnose 3. Monitor 4. Treat
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTA….
Amanda Mills
Omes & Sensors: Personal Device
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) Individuals will be responsible for their own health
Acknowledgements
33
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 other
McLaughlin 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
Dexcom - CGM
Device to Measure Glucose Levels —Useful for Diabetics
Breakfast
Raisin Bran Milk
Coffee
Lunch Salmon on Rye
Bagel and cheese
Dinner Pasta w/ Clams
Wine
Banana Bacon
Tall Latte
Chicken Ravioli Greens
Appetizers wine
Different Foods Cause Different Glucose Spikes
Subject A
Subject B
Corn flakes
Bread and PB
ProBar