Genes => Traits => Behaviors => Fitness => Genes Guest lecture @ University of Sydney, May 2014

1. © 2014 MapR Technologies 1

2. © 2014 MapR Technologies 2
Biomedical Research Goal: Improve Fitness
Therapeutics => Diagnostics => Prognostics
• Therapeutics => traditional medicine
• Diagnostics => personalized medicine
– NextGen public health
– Requires hi-res mechanical knowledge
– Reverse engineer how genetic variation leads to (un)desired traits
• Prognostics => GATTACA (dys/eu)topia
– Managed populations / NextGen eugenics

3. © 2014 MapR Technologies 3
Biomedical & Advertising Tech Overarching Themes*
*Obligatory movie references… shout-out to my hometown LA
Eugenics & Determinism Free will vs. Determinism Media Tech & Privacy

4. © 2014 MapR Technologies 4Star Wars III: Revenge of the Sith

5. © 2014 MapR Technologies 5Star Wars V: The Empire Strikes Back

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Health ~ Fitness
Genes => Traits => Behaviors => Fitness

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Human Genetics & Big Data
Human Genetics & Ethics
Today we talk about technology

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Me, Us
• Allen Day, Principal Data Scientist, MapR
5yr Hadoop Dev, R project contributor
PhD, Human Genetics, UCLA Medicine
• MapR
Distributes open source components for Hadoop
Adds major technology for performance, HA, industry standard API’s
• See Also
– “allenday” most places (twitter, github, etc.)
– @mapR

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Genetic Basis of Facial Features
self-reported values of {sex, ancestry}
+ observer scores [race, sex]}
+ 3D facial scan
+ genome scan
______________________________
Allelic model of 20 genes that
determine facial characteristics
Claes, et al. 2014. Modeling 3D Facial Shape from DNA

11. © 2014 MapR Technologies 11
Genetic Basis of Facial Features
Claes, et al. 2014. Modeling 3D Facial Shape from DNA

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So Get Ready…
www.theness.com

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DTRA102-007 – Forensic DNA
Analysis Kit for Genetic Intelligence
• Sex
• Blood type
• Ancestry
• Hair morphology
• Dimples
• Freckles
• Shoe size
• Flat-footedness
• Vision correction
• Ear lobe attachment
• Ear lobe crease
• 5th digit clinodactyly
• Eye color, hair color, skin
color
• Height, handedness
• Etc
https://sbirsource.com/grantiq#/topics/85383

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DTRA102-007: Sex and Ancestry

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Trends & Events

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Trends and Events: Even Moore’s Law
Stein. 2010. The case for cloud computing in genome informatics
“Even Moore’s” begins in 2004
with Solexa (acquired by ILMN 2007)
Storage:MB/$
DNA:bp/$
ILMN HiSeq XTen
(Jan 2014)
$1000 Genome

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NIH Research Funding Trends.
http://www.faseb.org/Policy-and-Government-Affairs/Data-Compilations/NIH-Research-Funding-Trends.aspx
Trends and Events: US Federal Funding You are here

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More Data
Less Federal $

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Trends and Events: The $1000 Genome
• Physicians want to use patient genomes to improve care
• Scientists say personalized medicine breakthroughs require
100Ks to MMs of genomes
• Healthcare mandates efficacy and efficiency (early majority)
These forces converge at $1000 for a clinically usable genome

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Trends and Events: ILMN HiSeq XTen Specs
• Sold in sets of 10 units ONLY (XTen =10 sequencers)
~ $10 million/XTen, shipments began in Jan 2014
• XTen produces 600 GBases/day @ 30x oversampling
= 1.8 TBases per 3-day cycle
= 54 TBytes per 3-day cycle
= $1000 per genome
= 18,000 genomes/year/XTen
~ 4,000,000 births/year (US, 2012)
 Neonatal sequencing is a reality (with 200 of today’s systems)

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Summary: Major Impact on Social Fabric
Soon to be gone:
• Muscular dystrophy
• Cystic fibrosis
• Albinism
• PKU (phenylketonuria)
• Paternity Tests =>
http://pandawhale.com/post/13851/my-report-card-came-in-my-paternity-test-came-in
http://www.nature.com/scitable/topicpage/rare-genetic-disorders-learning-about-genetic-disease-979
• Hemophilia
• Huntington’s Disease (keep?)
Fact: US paternity fraud
rate is 1 in 25

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Summary: A Perfect Storm
• LESS public funding (NIH)
• MORE DNA sequencing efficiency (HiSeq XTen)
• Predicted DNA sequencing demand VALIDATED (medicine)
• MORE VC investment ($1000/genome force confluence)
• DNA sequencing capacity consolidating into genome “factories” (e.g.
Broad, ILMN) => REQUIRES new infrastructure

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The Evolving Genomics Workload
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
<= 1º analytics
“current high ROI use cases”
<= 2º analytics
“next-gen high ROI use cases”

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The Evolving Genomics Workload
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
<= 1º analytics
“current high ROI use cases”
<= 2º analytics
“next-gen high ROI use cases”

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Clinical Application of Human Genetics

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Clinical Sequencing Business Process Workflow
PhysicianPatient
Clinic
blood/saliva
Clinical Lab
Analytics
extract

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One Bad MTHFR
MTHFR C677T
Methylfolate helps make neurotransmitters in
your brain. When methylfolate levels are low,
so are your neurotransmitters. Low production
of neurotransmitters may cause conditions of
addictive behavior, depression, anxiety,
ADHD, mania, irritability, insomnia, learning
disorders and others.
Everyone should get tested. Why? Because 1
in 2 people are affected and if one knows they
have a MTHFR polymorphism, they know they
have to be very proactive in taking care of
themselves.
http://thyroid.about.com/od/MTHFR-Gene-Mutations-and-Polymorphisms/fl/The-
Link-Between-MTHFR-Gene-Mutations-and-Disease-Including-Thyroid-
Health.htm

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One Bad MTHFR
MTHFR C677T
Methylfolate helps make neurotransmitters in
your brain. When methylfolate levels are low,
so are your neurotransmitters. Low production
of neurotransmitters may cause conditions of
addictive behavior, depression, anxiety,
ADHD, mania, irritability, insomnia, learning
disorders and others.
Everyone should get tested. Why? Because 1
in 2 people are affected and if one knows they
have a MTHFR polymorphism, they know they
have to be very proactive in taking care of
themselves.
http://thyroid.about.com/od/MTHFR-Gene-Mutations-and-Polymorphisms/fl/The-
Link-Between-MTHFR-Gene-Mutations-and-Disease-Including-Thyroid-
Health.htm

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Clinical Sequencing Business Process Workflow
PhysicianPatient
Clinic
blood/saliva
Clinical Lab
Analytics
extract

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Clinical Genomics, Information Systems Perspective
Compressed Structured
Base4 Data
Uncompressed Unstructured
Base2 Data
extract
Base4=>Base2
Converter
[[ DE-STRUCTURES ]]
“BI” Reporting and
Visualization tools
PhysicianPatient
AnalystStakeholder

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Clinical Genomics, Information Systems Perspective
PhysicianPatient
AnalystStakeholder
ETL
Reporting and Viz
Data Store
Analytics

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Clinical Genomics, Information Systems Perspective
PhysicianPatient
AnalystStakeholder
ETL
Reporting and Viz
Data Store
Analytics
1º analytics
2º analytics
Not much in this presentation,
see also:
http://slidesha.re/1sC2BOX

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1º Analytics: Why MapReduce?

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Clinical Genomics, Information Systems Perspective
PhysicianPatient
AnalystStakeholder
ETL
Reporting and Viz
Data Store
Analytics
1º analytics
2º analytics
see also:
http://slidesha.re/1sC2BOX

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The Essence of the Problem:
What is the (Probable) Color of Each Column?

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Next-Gen Human Genetics – Population Scale

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The Evolving Genomics Workload
Sboner, et al, 2011. The real cost of sequencing: higher than you think!
<= 2º analytics
“next-gen high ROI use cases”

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MapR Data Platform Advantage, Clinical Genomics
Epidemiological,
Actuarial Analyses
Denormalization for
Search, Viz, Research
ETL
Clinical
Reporting
WEB TIERClinical
Reporting
Systems
CLINICAL
TREATMENT
OF PATIENTS
RESEARCHERS
National Pop.
Database
INDEX SHARDSPrognostic
Capability

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Co-expression (10K samples) and Linkage
Gene Annotation / Set CompletionBMP6
BMP2
MMP3
LIF
NOS2A
MMP13
CSPG4
ACAN
ACAN
ACAN
COL11A2
COL11A2
COL9A1
MATN1
LECT1
MATN4
HAPLN1
HAPLN1
ITGA10
EDIL3
NGF
MAST4
MATN3
EPYC
COL11A1
COL11A1
COL10A1
COL10A1
THBS3
C1QTNF3
WISP1
PDPN
PDLIM4
CHST3
MIA
SOX5
CYTL1
TNMD
AKR1C1
MMP12
ETNK1
RELA
FOSL1
EIF2C2
NUPL1
RLF
RELB
SOD2
RNF24
RNF24
XYLT1
HAS2
BDKRB1
HSPC159
SLC28A3
FZD10
SLC28A3
HSPC159
BDKRB1
HAS2
XYLT1
RNF24
RNF24
SOD2
RELB
RLF
NUPL1
EIF2C2
FOSL1
RELA
ETNK1
MMP12
AKR1C1
TNMD
CYTL1
SOX5
MIA
CHST3
PDLIM4
PDPN
FZD10
WISP1
C1QTNF3
THBS3
COL10A1
COL10A1
COL11A1
COL11A1
EPYC
MATN3
MAST4
NGF
EDIL3
ITGA10
HAPLN1
HAPLN1
MATN4
ACAN
ACAN
ACAN
LECT1
MATN1
COL9A1
COL11A2
COL11A2
CSPG4
MMP13
NOS2A
LIF
MMP3
BMP2
BMP6
Disease gene characterization through large-scale co-expression analysis.
http://www.ncbi.nlm.nih.gov/pubmed/20046828
+ =>

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If they were unlabeled, would you know which is which?
Friend. 2010. The Need for Precompetitive
Integrative Bionetwork Disease Model Building
NPR. 2011. The Search For Analysts To Make Sense Of
'Big Data’
http://www.npr.org/2011/11/30/142893065

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If they were unlabeled, would you know which is which?
Friend. 2010. The Need for Precompetitive
Integrative Bionetwork Disease Model Building
• Identify network structures
• Label them
• Observe
stimulus=>response
space mapping
• Purposefully target
• $$$$ Twitter’s Business
Model

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These are Linear Algebra / Machine Learning Problems

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A Quick Digression: Recommender Systems

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HOW RECOMMENDATIONS WORK
Behavior of a crowd
helps us understand
what individuals will do

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History Matrix (A)
Alice
Bob
Charles
✔ ✔ ✔
✔ ✔
✔ ✔

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Co-occurrence Matrix (ATA)
1 2
1 1
1
1
2 1

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<Normalize>
(filter to identify only unusual co-occurences)

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HOW CROSS-RECOMMENDATIONS
WORK
Behavior of a crowd
helps us understand
what individuals will do

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Example Multi-modal Inputs
• Overlap in restaurant visits is useful
• Big spender cues
• Cuisine as an indicator
• Review text as an indicator

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People do more than one kind of thing
• Different kinds of behaviors give different quality, quantity and
kind of information
– Restaurant visits
– Movie reviews
• We don’t have to do co-occurrence
• We can do cross-occurrence
• Result is cross-recommendation

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For example
• Users enter queries (A)
– (actor = user, item=query)
• Users view videos (B)
– (actor = user, item=video)
• ATA gives query recommendation
– “did you mean to ask for”
• BTB gives video recommendation
– “you might like these videos”

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The punch-line
• BTA recommends videos in response to a query
– (isn’t that a search engine?)
– (not quite, it doesn’t look at content or meta-data)

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Real-life example
• Query: “Paco de Lucia”
• Conventional meta-data search results:
– “hombres del paco” times 400
– not much else
• Recommendation based search:
– Flamenco guitar and dancers
– Spanish and classical guitar
– Van Halen doing a classical/flamenco riff

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Real-life example

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Previous Click Histories
user1
user2
user3
user4
user5
1 2 3 4 5 6 7 8

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Detect similar content: 2 & 8
user1
user2
user3
user4
user5
1 2 3 4 5 6 7 8

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Call to Action – Request Clicks
user1
user2
user3
user4
user5
Show me more:
sports
comedy
technology
1 2 3 4 5 6 7 8
“Under
Construction”

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Build Navigational Ontology (estimate content labels):
4=sports ; 2 & 7=comedy
user1
user2
user3
user4
user5
Show me more:
sports
comedy
technology
1 2 3 4 5 6 7 8
4
2 & 7
Under
construction

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Matrices A (U*Q) and B (U*V)
Query Term = Clicked Term
Users
Query Terms
Users
Clicked Videos

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Relate Q to V
Users
Query Terms

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Relate Q to V
Users
Query Terms

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Users
Query Terms

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Relate Q to V: it’s a Cross-Recommender
QueryTerms
Videos

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Population-level Inference

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Typical Dimensions in Genetics/Medicine
• Genotype
• Gene Expression
• Samples
• Phenotypes (traits/behavior)

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Typical Dimensions in Behavioral Data
• Genotype
• Gene Expression
• Samples Individuals
• Phenotype
– Traits
– Behaviors

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Incidence/Co-occurrence in Behavioral Data
• Individual * Individual
– Genealogy
• Trait * Behavior => [Netflix]
– User/Content Topic Modeling
• Genotype * Behavior => [Psychometrics]
– Genetics of personality, intelligence, aptitude
• Behavior * Outcome => [Korn-Ferry]
– Job effectiveness
• Phenotype (trait/behavior) * Outcome => [eHarmony]
– Reproductive fitness

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Traits and Behaviors:
Content Topic Modeling / UX Personalization

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Behaviors and Outcomes:
Economic Fitness (Korn/Ferry)
Korn/Ferry ProSpective
http://linkedin.kornferry.com
Allen
=>

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Genes
Job
Performance

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(Traits/Behaviors) and Outcomes
Reproductive Fitness (eHarmony)
eHarmony @ Hadoop World: Data Science of Love
http://eharmony.com

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Genes
Reproductive
Outcomes

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Genes => Traits => Behaviors => Fitness
Job Performance
Psychometrics
Movie Preferences
Medicine
Forensics

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Genes => Traits => Behaviors => Fitness
Job Performance
Psychometrics
Movie Preferences
Medicine
Forensics
Fitness
Reproductive Outcomes

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ENCODE
http://www.nature.com/news/encode-the-human-encyclopaedia-1.11312

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Robot Scientist
Sparkes, et al. 2010. Towards Robot Scientists for autonomous scientific discovery

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Robot (Data?) Scientist
Sparkes, et al. 2010. Towards Robot Scientists for autonomous scientific discovery

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Thanks