2013 03 genomic medicine slides

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  • 2013 03 genomic medicine slides

    1. 1. Genes and Environment inPersonalized MedicineAtul Butte, MD, PhD abutte@stanford.eduChief, Division of Systems Medicine, @atulbutte Department of Pediatrics, Department of Medicine, and, by courtesy, Computer ScienceCenter for Pediatric Bioinformatics, LPCHStanford University
    2. 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. 3. Published online August 10, 2009 3
    4. 4. Lancet, 375:1525, May 1, 2010.
    5. 5. Patient zero40 year old male in good health presents to his doctor with his whole genomeNo symptomsExercises regularlyTakes no medicationsFamily history of aortic aneurysmFamily history of sudden deathPresents with 2.8 million SNPs752 copy number variants 6
    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. 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 modelRong Chen • Mapped to UMLS conceptsOptra Systems
    8. 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. 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. 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. 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
    12. 12. What are the alleles for rs1004819?
    13. 13. Alleles for rs1004819 are C and T~11% of records reported genotypes in the negative strand
    14. 14. VARIMED: Variants Informing Medicine Number of Distinct Diseases and papers SNPs phenotypes curated ~12,000 ~192,000 ~4,400Chen R, Davydov EV, Sirota M, Butte AJ. Rong ChenPLoS One. Optra Systems2010 October: 5(10): e13574. Personalis
    15. 15. Moving from OR to LROdds ratioRatio of odds of test positivity in cases over odds of test positivity in non-casesLikelihood 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.
    16. 16. Post-test probability is calculated with likelihood ratioPre-test odds x likelihood ratio  Post-test oddsPre-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.
    17. 17. Kohane, Masys, Altman. JAMA 2006, 296:212.
    18. 18. Fagan TJ. Nomogram for Bayestheorem. N Engl J Med.1975 Jul 31;293(5): 257.Morgan, Chen, Butte. Likelihoodratios for genomic medicine.Genome Medicine. 2010; 2:30.
    19. 19. Fagan TJ. Nomogram for Bayestheorem. N Engl J Med.1975 Jul 31;293(5): 257.Morgan, Chen, Butte. Likelihoodratios for genomic medicine.Genome Medicine. 2010; 2:30.
    20. 20. Fagan TJ. Nomogram for Bayestheorem. N Engl J Med.1975 Jul 31;293(5): 257.Morgan, Chen, Butte. Likelihoodratios for genomic medicine.Genome Medicine. 2010; 2:30.
    21. 21. Fagan TJ. Nomogram for Bayestheorem. N Engl J Med.1975 Jul 31;293(5): 257.Morgan, Chen, Butte. Likelihoodratios for genomic medicine.Genome Medicine. 2010; 2:30.
    22. 22. Current Medical Diagnosisand Treatment, 2007.
    23. 23. Current Medical Diagnosisand Treatment, 2007.
    24. 24. Current Medical Diagnosisand Treatment, 2007.
    25. 25. Rong ChenAlex 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 evaluationincorporating a personal genome. Lancet, 2010.
    26. 26. Rong ChenAlex Morgan
    27. 27. 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?
    28. 28. Erik CoronaPre-publication, embargoed for press. No tweets please.
    29. 29. 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?
    30. 30. Rong ChenAlex Morgan Joel Dudley
    31. 31. How can weexpect physiciansto review6 gigabases ina 15 minuteencounter?
    32. 32. We already askphysicians toreview 1 GB ofdata in 15minutes…
    33. 33. We already askphysicians toreview 1 GB ofdata in 15minutes…
    34. 34. We already askphysicians toreview 1 GB ofdata in 15minutes…… but we givethem tools to helpthem do this!
    35. 35. 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
    36. 36. Data-drivenSystems Medicine
    37. 37. 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.
    38. 38. Funded post-doctoral positions in Translational Bioinformatics availableFaculty openings for two Assistant or Associate ProfessorsContact Atul Butteabutte@stanford.edu
    39. 39. 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
    40. 40. Support• Lucile Packard Foundation for Childrens 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

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