Renaissance in Genomic Medicine

© 2014 MapR Technologies 1
“…all men are created equal…”

All Men Are Created Equal
• Data Scientist:
“depends on the distance metric”
DLaw(xa, xb)
DGenetic(xa, xb)

© 2014 © 201 M4 aMpaRp RTe Tcehcnhonloogloiegsies 5
DGenetic(x, x) = ???
Copenhagen, Denmark
1911

An Unusual Observation
• Wilhelm Johannsen. 1911,
Copenhagen
– Coins the word “gene”
• Discovery: sizes are not identical for
genetically identical peas.
The Genotype Concept of Heredity. Johannsen, 1911

Copenhagen
DGenetic(x, x) != 0
WTF?

Copenhagen
• Introduces new concept:
– hidden and causal genotype distinct
from
– observed phenotype

Copenhagen
genetically identical peas. Further, size
follows a Gaussian process, where
P ~ G + E
Var(P) ~ Var(G) + Var(E) + 2Cov(G, E)
• This is the basis of quantitative
genetics
https://www.youtube.com/watch?v=AjI_LcQOOs4

© 2014 MapR Techno©lo 2g0ie1s4 MapR Technologies 12
Renaissance In Medicine:
Next Generation BigData Workloads

1st Renaissance In Medicine: 1400-1700, Europe
Enabling Factor
• Movable Type
1450, Gutenberg
• Weak Church
• Compound Microscope
1624, Galileo
• Math-driven Hypotheses
1687, Newton
Effect
• Rapid diffusion of ideas
• New data sources
Human dissection
• Dense, precise data
Diagrams
• Paradigm shift in
reasoning

1st Renaissance In Medicine: 1400-1700, Europe
Enabling Factor
• Movable Type
1450, Gutenberg
• Weak Church
• Compound Microscope
1624, Galileo
• Math-driven Hypotheses
1687, Newton’s H0
Effect
Human dissection
• Dense, precise metrics
Diagrams
discovery

2nd Renaissance In Medicine: 1900-
Enabling Factor
• Telecom Networks
1969, ARPANET. 2008
• Globalization
• Next-Gen DNA Sequencer
1992, Lynx Therapeutics
• Data-driven Hypotheses
4th scientific paradigm
Effect
GMOs, stem cells
• Dense, precise metrics
Human genomes
discovery

DNA Sequencer – Dense, Precise Metrics

Current Generation “Next-Generation” Sequencer
• Jan 2014 – launch of Illumina XTen @ $10M (cap cost)
• Produces 6T basepairs / day @ $20K (ops cost)
Here’s how it works…

Current Generation “Next-Generation” Sequencer
• Jan 2014 – launch of Illumina XTen @ $10M (cap cost)
• Produces 6T basepairs / day @ $20K (ops cost)
A few other facts…
★ 300G basepairs / human @ $1000 (medical grade genome)
★ 4M births / year (2012, United States)

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

© 2014 MapR Technologies, confidential
Impact of XTen on Genomic Medicine
6T 7K
HUMANS / YEAR
$4B
ALL BABIES’ DNA
At a cost of $20K At medical grade of
100x oversampling,
$1000 / human
Operating cost of 500
XTen,
Capital cost of $5B
BASEPAIRS / DAY
…and remember, throughput doubles every 5 months

Ion Torrent
Technology

Nanopore Tech
* Roche
* Oxford Nanopore

Mobile Devices?
IoT DNA Sensors?

Social Impact

“The genetic engineers gave him that birthmark
as part of a sponsorship deal.”
© 2014 ww wMap.Rt Thechenonlogeiesss.com 30

Problem Prevention
Genetically deterministic diseases
• Muscular dystrophy
• Cystic fibrosis
• Albinism
• Phenylketonuria
• Hemophilia
• These are all completely preventable with pre-conception and
pre-natal screening
http://www.nature.com/scitable/topicpage/rare-genetic-disorders-learning-about-genetic-disease-979

Problem Prevention?
• FACT: US paternity fraud rate is 1 in 20
http://pandawhale.com/post/13851/my-report-card-came-in-my-paternity-test-came-in
http://en.wikipedia.org/wiki/Paternity_fraud

Problem? Prevention – More troubling diseases
• Huntingtons disease – autosomal dominant
• Inherited cancers – 10% of occurrences (!!!)
• Total annual cancer spending is $50B over 1M
people
http://www.nature.com/scitable/topicpage/rare-genetic-disorders-learning-about-genetic-disease-979
http://meps.ahrq.gov/mepsweb/data_files/publications/st80/stat80.pdf

Genetic Architecture of Cancer Risk
Inherited Cancer:
Large proportion of
incidence and cost
http://www.cancer.gov/cancertopics/pdq/genetics/overview/healthprofessional/page1/AllPages

people
Metrics => Diagnostics => Prognostics (domestication)

Metrics => Diagnostics => Prognostics (domestication)
Metrics => Diagnostics => Therapeutics (traditional medicine)
people

Singapore: Global Leader of Social Engineering
“Our government wants smart ladies to meet
smart guys to get smart children.”
–Director, Club2040 (Singapore matchmaking agency)
Domestication: the process whereby a population of living
organisms is changed at the genetic level, through generations of
selective breeding, to accentuate traits that ultimately benefit the
interests of humans.
http://www.nytimes.com/2008/04/29/world/asia/29iht-sing.1.12428974.html

Science Fiction-like
Unprecedented Control
Dystopia / Eutopia
Scenarios are Possible

Many DNA-Based Apps Coming…
25
20
15
10
5
0
2014 2020
• 2014: US$ 2B, mostly
research, mostly
chemical costs
• 2020: US$ 20B,
mostly clinical, mostly
analytics costs
Macquarie Capital, 2014. Genomics 2.0: It’s just the beginning
Cinical
Non-Clinical

DNA Diagnostics

Traditional Therapeutics
Trial & Error
http://www.taylorwessing.com/synapse/regulatory_personalised_medicines.html

Unsuitable
Therapy
Suitable
Therapy
Personalized Therapeutics
Trial & Error

Let’s look at some real data…
http://blog.covance.com/tag/companion-diagnostics/

Folate (aka vitamin B9, aka glutamate)
Yes, of Monosodium
Glutamate (MSG) fame…
Essential for fetal
development and brain
Function
Also used to detoxify

Folate (aka vitamin B9, aka glutamate) rich foods
http://www.easytolovebut.com/?p=2782

MTHFR is Frequently Mutated
(50%)
=> Dysfunction

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

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
Pulp Fiction
Pulp Fiction &
BMFwallets.com

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

Clinical Genomics, Information Systems Perspective
Stakeholder Analyst
Patient Physician
Compressed
Structured
Base4 Data
Unstructured
Base2 Data
extract
Base4=>Base2
Converter
Reporting and Viz

Stakeholder Analyst
Patient Physician
Compressed
Structured
Base4 Data
Unstructured
Base2 Data
extract
Base4=>Base2
Converter
Reporting and Viz
1º analytics
Base4=>Base2
Destroys Structure

Base4 => Base2 conversion, DNA fragmentation
Genome
(base4)
Sequencer
DNA Fragments
(base2)

2º analytics
Recovers Structure
Stakeholder Analyst
Patient Physician
Compressed
Structured
Base4 Data
Unstructured
Base2 Data
extract
Base4=>Base2
Converter
Reporting and Viz
1º analytics
Base4=>Base2
Destroys Structure

Stakeholder Analyst
Patient Physician
ETL
Reporting and Viz
Data Store

Data Scientist
Clinical Genomics, Data Science Process
Stakeholder Analyst
Patient Physician
ETL
Reporting and Viz
Data Store
Analytics
x
Improve Tests

Improve Tests
Data Scientist
Clinical Genomics, Political Process
Stakeholder Analyst
Patient Physician
ETL
Reporting and Viz
Data Store
Analytics
x
Improve Policy
Public Health System

Many DNA-Based Apps Coming…
25
20
15
10
5
0
2014 2020
• 2014: US$ 2B, mostly
research, mostly
chemical costs
• 2020: US$ 20B,
mostly clinical, mostly
analytics costs
Macquarie Capital, 2014. Genomics 2.0: It’s just the beginning
Cinical
Non-Clinical

Clinical Genomics, Data Science Process
Stakeholder Analyst
Patient Physician
ETL
Reporting and Viz
Data Store
Analytics
Secondary
Analytics
Downstream
Analytics
Experiment
Design
DNA
Sequencing

DNA Sequencing Value Chain
100
% Effort
0
Pre-NGS
~2000
Future
~2020
Now
Experiment
Design
DNA
Sequencing
Secondary
Analytics
Downstream
Analytics
Sboner, et al, 2011. The real cost of sequencing: higher than you think!

Sequence is Becoming Free (Even Moore’s Law)
Commoditization Pattern
Huge influx of inexpensive data
Creates new medical and biotech use-cases
100
% Effort
0
Pre-NGS
~2000
Future
~
Now

Experiment Design and Downstream Analytics
Specialization will grow to 100% effort
This is the desirable scenario
Biologists ought to be doing biology
100
% Effort
0
Pre-NGS
~2000
Future
~
Now
Downstream
Analytics

BigData Bottleneck
Time currently being spent on BigData problems
Not ideal
100
% Effort
0
Pre-NGS
~2000
Future
~
Now
Physicians & Biologists need help from CS & SW Engineers

Optimize
This
BigData Bottleneck
Time currently being spent on BigData problems
Not ideal
100
% Effort
0
Pre-NGS
~2000
Future
~
Now
Physicians & Biologists need help from CS & SW Engineers

2º Analytics (fragment re-assembly), Quick Overview
Some of these differences occur in the personal DNA… http://www.bioinfor.© 2014 com/MapR zoom/Technologies support/tutorials.html
70

Some Errors come from the Sequencer
• This is why a “medical grade” genome is 100x oversampled
http://www.medicinalgenomics.com/dna-assembly/

Goal: Find the Real Differences
Some of these differences occur in the personal DNA… http://www.bioinfor.com/zoom/support/tutorials.html

1. What is the (Probable) Color of Each Column?

2. Is the Probable Color Surprising?
Expected

Row-major strategies: inefficient with CPU or Mem
Expected

Strategy 1: foreach column, foreach row
O(rows*cols) + Expected
O(1 col) mem

Strategy 2: foreach row, keep running tallies
O(rows) + Expected
O(rows*cols) mem

Strategy 3: rotate => foreach (transposed) column
Expected
O(rows log rows) +
O(cols) +
O(1 col) mem
REQUIRES SORT
Personal variants

Fortunately, Hadoop is Really Good at Sorting

Comparison of Strategies
Strategy 1 Strategy 2 Strategy 3
O(rows*cols)
+ O(1 col) mem
O(rows)
+ O(rows*cols) mem
O(rows log rows)
+ O(cols)
+ O(1 col) mem
Compute Intense
Memory Efficient
Compute Efficient
Memory Intense
Compute Efficient
Memory Efficient

O(rows*cols)
+ O(1 col) mem
O(rows)
+ O(rows*cols) mem
O(rows log rows) ÷ shards
+ O(cols) ÷ shards
+ O(1 col) mem
OK for small matrixes
Infinitely attractive for
infinitely large matrixes

O(rows*cols)
+ O(1 col) mem
O(rows)
+ O(rows*cols) mem
O(rows log rows) ÷ shards
+ O(cols) ÷ shards
+ O(1 col) mem
Distributed matrix math techniques
are common in tech, but not
common in genomics
Some commercial leaders: Seven
Bridges Genomics, Ion Flux
Twitter Algebird:
Matrix math on Hadoop

Paradigm Shift in Discovery

All next-gen drugs will require a “companion diagnostic”…
…as part of Rx to determine your personal response segment
Patient Physician
Patient Physician
ETL
Reporting and Viz
Data Store

Personalized Therapeutics
Unsuitable
Therapy
Suitable
Therapy

How to Determine Response Segment? EHR Mining
Building the graph of medicine from millions of clinical narratives. Finlayson, 2014

Metrics => Diagnostics => Prognostics
Metrics => Diagnostics => Therapeutics

Personal Genome in EHR => Better Therapeutics
Personal Genome

Personal Genome in EHR => Prognostic Medicine
Personal Genome
>Predict personal outcome
>Preventative action

How to Improve?

Strategy 1: Improve Diagnostics & Therapeutics
Improve Health with
Personalized Therapy

Strategy 2: Improve Prognostics => Domesticate
Reshape / Remove

Strategy 2: Improve Prognostics => Domestication
http://www.fao.org/docrep/field/009/v8720e/v8720e04.htm

Strategy 2: Improve Prognostics => Domestication
Beware:
Law of Unintended
Consequences
From:
The Unanticipated
Consequences of
Purposive Social
Action”
Merton, 1936
http://www.fao.org/docrep/field/009/v8720e/v8720e04.htm

“When Racism was Science”, 10/13 in
• Current Exhibit at NYU (thru March 2015)
• Reproduction of the “Eugenics Record Office”
• @ Cold Spring Harbor Laboratory (30min from NYC)
• Supported by many social progressives (Carnegie Institute,
Rockefeller family, Harvard)
• “There were many prominent New Yorkers involved in eugenics […] It
was initially about how to become more efficient as a modern
society.
• Today, viewed as a scientific disgrace by Americans. What about
later?
http://www.nytimes.com/2014/10/14/science/haunted-files-the-eugenics-record-office-recreates-a-dark-time-in-a-laboratorys-past.html

How to Architect a Solution?

Personal Genome
Predict personal outcome

Personal Genome
Predict personal outcome
Let’s look at a
existing system
that’s similar…

Largest Biometric Database in the World
1.2B
PEOPLE
PEOPLE

Why Create Aadhaar?
• India: 1.2 billion residents
– 640,000 villages, ~60% lives under $2/day
– ~75% literacy, <3% pay income tax, <20% have bank accounts
– ~800 million mobile, ~200-300 million migrant workers
• Govt. spends about $25-40 billion on direct subsidies
– Residents have no standard identity document
– Most programs plagued with ghost and multiple identities causing
leakage of 30-40%

Why Create Aadhaar?
• India: 1.2 billion residents
– 640,000 villages, ~60% lives under $2/day
– ~75% literacy, <3% pay income tax, <20% have bank accounts
– ~800 million mobile, ~200-300 million migrant workers
• Govt. spends about $25-40 billion on direct subsidies
– Residents have no standard identity document
– Most programs plagued with ghost and multiple identities causing
leakage of 30-40%
Standardize identity => Stop leakage

Aadhaar Biometric Capture & Index
Raw
Digital
Fingerprint

Aadhaar Biometric ID Creation
F(x): unique features
G(x): uncommon features
H(x): other features
• 600 to 800 million people
loaded in 4 years
• In production
– 1 million registrations/day
– 200+ trillion lookups/day
• All built on MapR-DB (HBase)

Aadhaar Biometric ID Creation
• 600 to 800 million people
loaded in 4 years
• In production
– 1 million registrations/day
– 200+ trillion lookups/day
• All built on MapR-DB (HBase)

How Does this Relate to Genomics?
Same data shape and size
• Aadhaar: 1B humans, 5MB minutia
• Genome: 6B humans, ~3M variants

Same data set operations
F-1(x): common features
Same data shape and size
• Aadhaar: 1B humans, 5MB minutia
• Genome: 6B humans, ~3M variants
• Genome: variant × phenotype
• Common variant => causal gene
F-1(x) !

Same data set operations
F-1(x): common features
Same set operations
Same data access pattern
Same algorithm / memory complexity

Join and Regress
Phenotype ~ Genotype
Generate new Hypotheses

There’s Something Bigger Happening…

Medical Renaissance is Part of a Paradigm Shift
• The Structure of Scientific Revolutions, Kuhn, 1962
http://edtosavetheworld.com/2014/05/01/change-theory-do-we-need-a-new-paradigm/

1st Paradigm: Observational (pre)Science
measurement
1st paradigm: observational science
description
observation theory

simulation
2nd Paradigm: Theoretical (pre)Science
measurement
description
observation theory
explanation
2nd paradigm: theoretical science
mathematics
computing

simulation
3rd Paradigm: Experimental Science
measurement
description
observation theory
explanation
2nd paradigm: theoretical science
mathematics
computing
directed control channel
observation
3rd paradigm:
experimental science

Medical Renaissance is Part of a Paradigm Shift
• The Structure of Scientific Revolutions, Kuhn, 1962

4th Paradigm: eScience, enabled by BigData
explore
simulate
explain
4th paradigm: eScience
accumulated knowledge
computing
big data engine => “ escape velocity”

Thanks!
Questions?
@allenday, @mapr
linkedin.com/in/allenday
aday@mapr.com
“Good news – I hear the paradigm is shifting”
~ New Yorker, Charles Barsotti

Renaissance in Genomic Medicine

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