1. Genes and Environment in
Personalized Medicine
Atul Butte, MD, PhD abutte@stanford.edu
Chief, Division of Systems Medicine, @atulbutte
Department of Pediatrics,
Department of Medicine, and, by courtesy,
Computer Science
Center for Pediatric Bioinformatics, LPCH
Stanford University

2. Disclosures
• Scientific founder and • Honoraria for speaking at
advisory board membership – Lilly
– Genstruct – Pfizer
– NuMedii – Siemens
– Personalis – Bristol Myers Squibb
– Carmenta • Speakers’ bureau
– None
• Past or present consultancy
– Lilly • Companies started by students
– Johnson and Johnson – Carmenta
– Roche – Serendipity
– NuMedii – NuMedii
– Genstruct – Stimulomics
– Tercica – NunaHealth
– Ansh Labs – Praedicat
– Prevendia – Flipora
– Samsung

3. Published online August 10, 2009
3

4. Lancet, 375:1525, May 1, 2010.

5. Patient zero
40 year old male in
good health presents to his
doctor with his
whole genome
No symptoms
Exercises regularly
Takes no medications
Family history of
aortic aneurysm
Family history of
sudden death
Presents with 2.8 million SNPs
752 copy number variants
6

6. Existing SNP-disease databases are too limited
for application to a human genome
Genome-wide association studies
• NHGRI GWAS Catalog
– 1032 papers  5050 SNPs for 557 diseases (6280 records),
but 26% without OR, 33% without risk/protective alleles
Individual candidate-gene associations
• NIH Genetic Association Database
– 56,000 papers, 130,000 records, ~2000 genes, only 4% with
dbSNP ids, 1706 with alleles, none with risk/protective
• Online Mendelian Inheritance in Man
– Moving to dbSNP ids, monogenic
• Human Genome Mutation Database
– 113247 mutations, most Mendelian disease, few SNPs, no
genotypes, or odds ratios

7. • Study published in 2008 in
Inflammatory Bowel
Disease
• Crohn’s Disease and
Ulcerative Colitis
• Investigated 9 loci in 700
Finnish IBD patients
• We record 100+ items
– GWAS, non-GWAS papers
– Disease, Phenotype
– Population, Gender
– Alleles and Genotypes
– p-value (and confidence)
– Odds ratio (and confidence)
– Technology, Study design
– Genetic model
Rong Chen • Mapped to UMLS concepts
Optra Systems

8. • Study published in 2008 in
Inflammatory Bowel
Disease
• Crohn’s Disease and
Ulcerative Colitis
• Investigated 9 loci in 700
Finnish IBD patients
• We record 100+ items
– GWAS, non-GWAS papers
– Disease, Phenotype
– Population, Gender
– Alleles and Genotypes
– p-value (and confidence)
– Odds ratio (and confidence)
– Technology, Study design
– Genetic model
• Mapped to UMLS concepts

9. • Study published in
2009 in
Rheumatology
• Ankylosing
spondylitis
• Investigated 8
SNPs in IL23R in
2000 UK case-
control patients
• Tables can be rotated
• NLP is hard

10. • Study published in
2009 in
Rheumatology
• Ankylosing
spondylitis
• Investigated 8
SNPs in IL23R in
2000 UK case-
control patients
• Tables can be rotated
• NLP is hard

11. • Study published in
2009 in
Rheumatology
• Ankylosing
spondylitis
• Investigated 8
SNPs in IL23R in
2000 UK case-
control patients
• Tables can be rotated
• NLP is hard

13. What are the alleles for rs1004819?

14. Alleles for rs1004819 are C and T
~11% of records reported genotypes in the negative strand

18. VARIMED: Variants Informing Medicine
Number of Distinct Diseases and
papers SNPs phenotypes
curated
~12,000 ~192,000 ~4,400
Chen R, Davydov EV, Sirota M, Butte AJ. Rong Chen
PLoS One. Optra Systems
2010 October: 5(10): e13574.
Personalis

21. Moving from OR to LR
Odds ratio
Ratio of odds of test positivity in cases over
odds of test positivity in non-cases
Likelihood ratio (+)
The probability of test positive in cases, over the
probability of test positive in non-cases
Sensitivity / (1 – Specificity)
Very similar, but different...
Morgan A, Chen R, Butte AJ. Genomic Medicine, 2010.

22. Post-test probability is calculated
with likelihood ratio
Pre-test odds x likelihood ratio  Post-test odds
Pre-test odds x LR1 x LR2 x LR3  Post-test odds
Can chain likelihood ratios
from independent tests
Morgan A, Chen R, Butte AJ. Genomic Medicine, 2010.

23. Kohane, Masys, Altman. JAMA 2006, 296:212.

24. Fagan TJ. Nomogram for Bayes
theorem. N Engl J Med.
1975 Jul 31;293(5): 257.
Morgan, Chen, Butte. Likelihood
ratios for genomic medicine.
Genome Medicine. 2010; 2:30.

25. Fagan TJ. Nomogram for Bayes
theorem. N Engl J Med.
1975 Jul 31;293(5): 257.
Morgan, Chen, Butte. Likelihood
ratios for genomic medicine.
Genome Medicine. 2010; 2:30.

26. Fagan TJ. Nomogram for Bayes
theorem. N Engl J Med.
1975 Jul 31;293(5): 257.
Morgan, Chen, Butte. Likelihood
ratios for genomic medicine.
Genome Medicine. 2010; 2:30.

27. Fagan TJ. Nomogram for Bayes
theorem. N Engl J Med.
1975 Jul 31;293(5): 257.
Morgan, Chen, Butte. Likelihood
ratios for genomic medicine.
Genome Medicine. 2010; 2:30.

28. Current Medical Diagnosis
and Treatment, 2007.

29. Current Medical Diagnosis
and Treatment, 2007.

30. Current Medical Diagnosis
and Treatment, 2007.

31. Rong Chen
Alex Morgan
Ashley EA*, Butte AJ*,
Wheeler MT, Chen R,
Klein TE, Dewey FE,
Dudley JT, Ormond KE,
Pavlovic A, Hudgins L,
Gong L, Hodges LM,
Berlin DS, Thorn CF,
Sangkuhl K, Hebert JM,
Woon M, Sagreiya H,
Whaley R, Morgan AA,
Pushkarev D, Neff NF,
Knowles W, Chou M,
Thakuria J, Rosenbaum
A, Zaranek AW, Church
G, Greely HT*, Quake
SR*, Altman RB*.
Clinical evaluation
incorporating a personal
genome. Lancet, 2010.

32. Rong Chen
Alex Morgan

33. Why do we even have risk alleles?
• Humans are not a very old species
• But wouldn’t we expect disease risk alleles to be
selected against?
• Disease depends on the environment
– Sickle cell trait and malaria
– Cystic fibrosis and cholera
– Lactase and milk digestion
• Some risk alleles have positive effects in the
right environment
• So when (and why) might risk alleles have
entered the human genome?

34. Erik Corona
Pre-publication, embargoed for press. No tweets please.

35. So what can we do about the risk?
• Diseases with higher post-test probabilities
• How to alter the influence of genetics?
• Diseases are caused by genes and environment
• We need a simple “prescription” for environmental
change for a genome-enabled patient
• How do we compensate for our genomes?

36. Rong Chen
Alex Morgan
Joel Dudley

37. How can we
expect physicians
to review
6 gigabases in
a 15 minute
encounter?

38. We already ask
physicians to
review 1 GB of
data in 15
minutes…

39. We already ask
physicians to
review 1 GB of
data in 15
minutes…

40. We already ask
physicians to
review 1 GB of
data in 15
minutes…
… but we give
them tools to help
them do this!

41. Two Major Colliding Directives in Medicine
 Are Personalized Medicine and
Quality Improvement heading on Personalized
a collision course? Medicine
 How are we going to treat each
patient in their own special
way, when we need to treat each
patient in a standard way?
Quality
Improvement

48. Data-driven
Systems Medicine

49. Take Home Points
• Genome-wide sequencing is here: managing
this data and relating to medicine
is the challenge.
• Personalized medicine ≥ DNA. Needs to
include diversity, and other clinical, molecular,
and environment measures.
• Teaching interns, residents, and physicians in
all disciplines will be the future rate-limiting
challenge.

50. Funded post-doctoral
positions in
Translational
Bioinformatics
available
Faculty openings for
two Assistant or
Associate
Professors
Contact Atul Butte
abutte@stanford.edu

51. Collaborators
• Jeff Wiser, Patrick Dunn, Mike Atassi / Northrop Grumman
• Ashley Xia and Quan Chen / NIAID
• Takashi Kadowaki, Momoko Horikoshi, Kazuo Hara, Hiroshi Ohtsu / U Tokyo
• Kyoko Toda, Satoru Yamada, Junichiro Irie / Kitasato Univ and Hospital
• Shiro Maeda / RIKEN
• Alejandro Sweet-Cordero, Julien Sage / Pediatric Oncology
• Mark Davis, C. Garrison Fathman / Immunology
• Russ Altman, Steve Quake / Bioengineering
• Euan Ashley, Joseph Wu, Tom Quertermous / Cardiology
• Mike Snyder, Carlos Bustamante, Anne Brunet / Genetics
• Jay Pasricha / Gastroenterology
• Rob Tibshirani, Brad Efron / Statistics
• Hannah Valantine, Kiran Khush/ Cardiology
• Ken Weinberg / Pediatric Stem Cell Therapeutics
• Mark Musen, Nigam Shah / National Center for Biomedical Ontology
• Minnie Sarwal / Nephrology
• David Miklos / Oncology

52. Support
• Lucile Packard Foundation for Children's Health
• NIH: NIAID, NLM, NIGMS, NCI; NIDDK, NHGRI, NIA, NHLBI, NCATS
• March of Dimes
• Hewlett Packard
• Howard Hughes Medical Institute
• California Institute for Regenerative Medicine
• Scleroderma Research Foundation
• Clayville Research Fund
• PhRMA Foundation Admin and Tech Staff
• Stanford Cancer Center, Bio-X • Susan Aptekar
• Rhonda Pisk
• Alex Skrenchuk
• Tarangini Deshpande
• Alan Krensky, Harvey Cohen
• Hugh O’Brodovich
• Isaac Kohane