Genes and Environment in Personalized Medicine

Atul Butte, MD, PhDChief, Division of Systems Medicine,

Department of Pediatrics,Department of Medicine, and, by courtesy, Computer Science

Center for Pediatric Bioinformatics, LPCHStanford University

abutte@stanford.edu @atulbutte

Disclosures• Scientific founder and

advisory board membership– Genstruct– NuMedii– Personalis– Carmenta

• Past or present consultancy– Lilly– Johnson and Johnson– Roche– NuMedii– Genstruct– Tercica– Ansh Labs– Prevendia– Samsung

• Honoraria for speaking at– Lilly– Pfizer– Siemens– Bristol Myers Squibb

• Speakers’ bureau– None

• Companies started by students– Carmenta– Serendipity– NuMedii– Stimulomics– NunaHealth– Praedicat– Flipora

3

Published online August 10, 2009

Lancet, 375:1525, May 1, 2010.

6

Patient zero40 year old male in

good health presents to his doctor with his whole genome

No symptomsExercises regularlyTakes no medicationsFamily history of

aortic aneurysmFamily history of

sudden death

Presents with 2.8 million SNPs752 copy number variants

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

• 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

Rong ChenOptra Systems

• 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

• 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

• 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

• 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

What are the alleles for rs1004819?

Alleles for rs1004819 are C and T

~11% of records reported genotypes in the negative strand

Number of papers curated

Distinct SNPs

Diseases and phenotypes

~12,000 ~192,000 ~4,400

Rong ChenOptra Systems

Personalis

VARIMED: Variants Informing Medicine

Chen R, Davydov EV, Sirota M, Butte AJ. PLoS One. 2010 October: 5(10): e13574.

Moving from OR to LR

Odds ratioRatio 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-casesSensitivity / (1 – Specificity)

Very similar, but different...

Morgan A, Chen R, Butte AJ. Genomic Medicine, 2010.

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

Morgan A, Chen R, Butte AJ. Genomic Medicine, 2010.

Can chain likelihood ratios from independent tests

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

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.

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.

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.

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.

Current Medical Diagnosis and Treatment, 2007.

Current Medical Diagnosis and Treatment, 2007.

Current Medical Diagnosis and Treatment, 2007.

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.

Rong ChenAlex Morgan

Rong ChenAlex Morgan

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?

Erik Corona

Pre-publication, embargoed for press. No tweets please.

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?

Rong ChenAlex Morgan

Joel Dudley

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

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

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

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

Two Major Colliding Directives in Medicine

Are Personalized Medicine and Quality Improvement heading on a collision course?

How are we going to treat each patient in their own special way, when we need to treat each patient in a standard way?

PersonalizedMedicine

QualityImprovement

Data-drivenSystems Medicine

Take Home Points

• Genome-wide sequencing is here: managing this data and relating to medicine is the challenge.

• Teaching interns, residents, and physicians in all disciplines will be the future rate-limiting challenge.

• Personalized medicine ≥ DNA. Needs to include diversity, and other clinical, molecular, and environment measures.

Funded post-doctoral positions in Translational Bioinformatics available

Faculty openings for two Assistant or Associate Professors

Contact Atul Butteabutte@stanford.edu

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

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• Stanford Cancer Center, Bio-X

• Tarangini Deshpande• Alan Krensky, Harvey Cohen• Hugh O’Brodovich• Isaac Kohane

Admin and Tech Staff• Susan Aptekar• Rhonda Pisk• Alex Skrenchuk