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Patient-Organized Genomic Research Studies


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DIYgenomics has developed a methodology for the conduct of patient-organized genomic research studies, obtaining outcomes by linking genomic data to phenotypic data and intervention. The general hypothesis is that individuals with one or more polymorphisms in the main variants associated with conditions may be more likely to have baseline out-of-bounds phenotypic biomarker levels, and could benefit the most from targeted intervention.

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Patient-Organized Genomic Research Studies

  1. 1. Patient-Organized Genomic Research Studies Melanie Swan, MBA Founder DIYgenomics +1-650-681-9482 @DIYgenomics [email_address] March 3, 2011, Scripps, La Jolla CA The Future of Genomic Medicine IV conference Slides: Personal genome apps Crowd-sourced clinical trials
  2. 2. Agenda <ul><li>Introduction </li></ul><ul><li>Patient-organized genomic studies </li></ul><ul><li>Translational research </li></ul><ul><li>Mobile and web apps </li></ul>March 3, 2011 Image credit: Getty Images
  3. 3. About Melanie Swan <ul><li>Founder, DIYgenomics </li></ul><ul><li>Finance, entrepreneurship, technology </li></ul><ul><li>MBA, Wharton; BA, Georgetown Univ.; Instructor, Singularity University </li></ul><ul><li>Sample publications </li></ul><ul><ul><li>Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet. Med. 2010 , May;12(5):279-88. </li></ul></ul><ul><ul><li>Swan, M. Translational antiaging research. Rejuvenation Res. 2010 , Feb;13(1):115-7. </li></ul></ul><ul><ul><li>Swan, M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 2009 , 2, 492-525. </li></ul></ul>March 3, 2011
  4. 4. Numerous useful applications of genomics <ul><li>Traditional </li></ul><ul><ul><li>Ancestry </li></ul></ul><ul><ul><li>Carrier status </li></ul></ul><ul><ul><li>Identity (paternity, forensics) </li></ul></ul><ul><li>Maturing </li></ul><ul><ul><li>Health condition risk </li></ul></ul><ul><ul><li>Pharmaceutical response </li></ul></ul><ul><li>Novel </li></ul><ul><ul><li>Athletic performance </li></ul></ul><ul><ul><li>OTC product response </li></ul></ul><ul><ul><li>Toxin processing </li></ul></ul><ul><li>Predictive wellness profiling </li></ul><ul><ul><li>Aging, cancer, immune response, organ health </li></ul></ul>March 3, 2011 Image credit:
  5. 5. Agenda <ul><li>Introduction </li></ul><ul><li>Patient-organized genomic studies </li></ul><ul><li>Translational research </li></ul><ul><li>Mobile and web apps </li></ul>March 3, 2011 Image credit: Getty Images
  6. 6. DIYgenomics philosophy March 3, 2011 <ul><li>Goal: preventive medicine </li></ul><ul><ul><li>Realize preventive medicine by establishing baseline markers of wellness and pre-clinical interventions </li></ul></ul><ul><li>Generalized hypothesis </li></ul><ul><ul><li>One or more polymorphisms may result in out-of-bounds baseline levels of phenotypic markers. These levels may be improved through personalized intervention. </li></ul></ul>Source: Genotype Phenotype Intervention Outcome + + =
  7. 7. DIYgenomics study design template: MTHFR March 3, 2011 Source: Cyanocobalamin Image credit:
  8. 8. Homocysteine metabolism pathway March 3, 2011 Source: Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced preventive medicine research. J Participat Med. 2010 Dec 23; 2:e20.
  9. 9. MTHFR pilot study results March 3, 2011 <ul><li>Drug store vitamin (Centrum) reduced homocysteine levels for 6/7 participants </li></ul>Blood Test # 2. Homocysteine levels DIYgenomics MTHFR Vitamin B deficiency study 1 1. Genotype profiles Baseline LMF Source: Swan, M., Hathaway, K., Hogg, C., McCauley, R., Vollrath, A. Citizen science genomics as a model for crowdsourced preventive medicine research. J Participat Med. 2010 Dec 23; 2:e20. 1 Results are not statistically significant and are intended as a pilot demonstration of patient-organized genomic studies Baseline + LMF Centrum Homocysteine umol/l Centrum LMF = L-methylfolate
  10. 10. DIYgenomics studies March 3, 2011 Image credit: Study Status 1. MTHFR/Vitamin B Pilot complete, open enrollment 2. Memory filtering In design (Q1/Q2) 3. Aging Open enrollment 4. Vitamin D In design (Q2) 5. Mental performance In design (Q3) 6. Metabolism/cholesterol mgt In design (Q3) 7. Citizen scientist proposed… Ongoing
  11. 11. Memory filtering study March 3, 2011 <ul><li>Dopaminergic modulation of memory filtering </li></ul><ul><li>Collaboration with the Laboratory of Cognitive Neuro-Rehabilitation, University Hospitals of Geneva </li></ul><ul><li>IRB and informed consent </li></ul><ul><li>Goal: 100 member healthy control cohort </li></ul><ul><ul><li>Genotype: COMT, DRD2, SLC6A3 (~5 SNPs) (neurotransmitter modulation) </li></ul></ul><ul><ul><li>Phenotype: memory filtering test (24 minutes) and reversal learning task (10 minutes) </li></ul></ul><ul><ul><li>Background questionnaire: neurological or psychiatric antecedents, medications, demographic information, and IQ (WAIS, Raven’s Matrices) </li></ul></ul>Image credit:
  12. 12. Aging (genomic markers) <ul><li>Top twenty genomic mechanisms of aging (1,000 variants in DIYgenomics Aging GWAS database) </li></ul><ul><ul><li>Aging-specific genetics (overall profile, IGF-1/insulin signaling, inflammation, immune system, DNA damage repair, cell cycle, telomere length, mitochondrial health) </li></ul></ul><ul><ul><li>Diabetes and metabolic disease (cholesterol, obesity, adiposity, fat distribution) </li></ul></ul><ul><ul><li>Catabolism (waste removal) and other (Alzheimer’s disease, macular degeneration, rheumatoid arthritis, osteoporosis, sarcopenia, kidney and liver disease) </li></ul></ul><ul><ul><li>Heart disease and blood operations (cardiovascular disease, atherosclerosis, myocardial infarction, atrial fibrillation) </li></ul></ul><ul><ul><li>Cancer (profile for twenty cancers including breast, prostate, colorectal, lung, melanoma, glioma, ovarian, pancreatic) </li></ul></ul>March 3, 2011 Image credit: Source: DIYgenomics Aging GWAS Database:
  13. 13. Aging (phenotypic markers) <ul><li>Top twenty phenotypic biomarkers of aging </li></ul><ul><ul><li>Aging-specific markers (telomere length, lymphocyte regeneration, CD levels, inflammation, hormone levels) </li></ul></ul><ul><ul><li>Diabetes and metabolic disease (BMI, cholesterol (HDL/LDL/triglycerides; LDL particle size), Framingham Risk Score, fasting glucose, non-fasting glucose, albumin, uric acid) </li></ul></ul><ul><ul><li>Catabolism and other (VO2 max, bone mineral density, muscle mass, GOT, GPT, creatinine, eGFR) </li></ul></ul><ul><ul><li>Heart disease and blood operations (blood pressure, hematocrit, hemoglobin, RBC, WBC, CRP, platelets, erythrocyte glycoslyation) </li></ul></ul><ul><ul><li>Cancer (granulocyte strength, blood-assay) </li></ul></ul>March 3, 2011 Image credit: Source: DIYgenomics Aging Study,
  14. 14. Aging (interventions) <ul><li>Top twenty aging interventions </li></ul><ul><ul><li>Traditional (exercise, nutrition, sleep, vitamins, stress-reduction) </li></ul></ul><ul><ul><li>Novel (brain fitness programs and mid-life cholesterol management for Alzheimer’s disease, cholesterol management with CETP-inhibitor a nacetrapib, TA-65 telomerase activation for telomere length management, resistance weight lifting for sarcopenia, interval training and aerobic exercise for VO2 max improvement, blood-based assays for early detection of cancer, rheumatoid arthritis, macular degeneration, kidney and liver disease) </li></ul></ul>March 3, 2011 Image credit: Source: DIYgenomics Aging Study,
  15. 15. Innovating the research model March 3, 2011 Institutional PI (principal investigator) Traditional Research Model Patient-organized Research Model Research subjects Citizen scientists* Investigators = Participants *Self-selection bias: 100,000 consumer genomics customers Institutional Review Board (IRB) IRBs, FAQs, Citizen ethicists Grant funding Journal publication Self publishing Patient advocacy groups Research foundations Social VC Crowd-sourcing
  16. 16. Genomic study platform: Genomera March 3, 2011
  17. 17. Agenda <ul><li>Introduction </li></ul><ul><li>Patient-organized genomic studies </li></ul><ul><li>Translational research </li></ul><ul><li>Mobile and web apps </li></ul>March 3, 2011 Image credit: Getty Images
  18. 18. Ranking variant quality <ul><li>Background </li></ul><ul><ul><li>NIH plan to develop a Genetic Testing Registry (2011) </li></ul></ul><ul><ul><li>Criteria proposed by Ioannidis JP et al. Int J Epidemiol. (2008): amount of evidence, replication, protection from bias </li></ul></ul><ul><li>Methodology: assign a composite score to each variant per the number of cases and controls, p-value, odds ratio, and journal ranking </li></ul>March 3, 2011 Source: DIYgenomics Image credit:
  19. 19. Athletic performance March 3, 2011 Image credit: V = number of variants; % = ratio of favorable polymorphisms to total alleles for a sample individual; S = number of studies Source: Swan, M. Applied genomics: personalized interpretation of athletic performance GWAS. 2011 Jan. Submitted. Category Genes V % S Endurance, power, and energy Endurance ACE, ACTN3, ADRB2/ ADRB3, BDKRB2, COL5A1, GNB3 7 50 22 Power ACE, ACTN3, AGT 3 50 8 Energy HIF1A, PPARGC1A 3 25 9 Musculature, and heart and lung capacity Muscle fatigue and repair HNF4A, NAT2 and IL-1B 5 40 4 Strength HFE, HIF1A, IGF1, MSTN GDF8 5 17 15 Heart and lung capacity CREB1, KIF5B, NOS3, NPY and ADRB1, APOE, NRF1 9 36 11 Metabolism, recovery, and other   Metabolism AMPD1, APOA1, PPARA, PPARD 5 50 9 Recovery CKMM/CKM, IL6 2 50 5 Ligament and tendon strength  Ligament strength COL1A1, COL5A1, CILP 3 50 4 Tendon strength COL1A1, COL5A1, GDF5, MMP3 7 63 5
  20. 20. OTC product response, toxin processing <ul><li>OTC product response </li></ul><ul><ul><li>Skin (premature aging, wrinkles, sun damage, eczema, irritation, antioxidant treatment, anti-aging treatment) </li></ul></ul><ul><ul><li>Hair (hair loss, premature greying, male pattern baldness) </li></ul></ul><ul><ul><li>Esophagus (reflux, bile acid response) </li></ul></ul><ul><ul><li>Teeth (periodontitis) </li></ul></ul><ul><ul><li>Sleep (daytime sleepiness, caffeine-induced insomnia) </li></ul></ul><ul><li>Environmental exposure: toxin processing </li></ul><ul><ul><li>Benzene </li></ul></ul><ul><ul><li>Quinone oxidoreductase </li></ul></ul><ul><ul><li>PAHs metabolism </li></ul></ul><ul><ul><li>Arylarene metabolism </li></ul></ul><ul><ul><li>Mercury and lead exposure </li></ul></ul><ul><ul><li>Liver and kidney health (general) </li></ul></ul>March 3, 2011 Source: DIYgenomics Image credit:
  21. 21. Predictive wellness profiling: cancer <ul><li>Proto-oncogene/tumor suppressor gene polymorphisms </li></ul>March 3, 2011 Source: DIYgenomics Image credit: TP53: cell cycle arrest, PTEN: cell cycle progression modulator, MYC: cell cycle regulator
  22. 22. Lung cancer risk and drug response <ul><li>Risk and drug response for specific cancers </li></ul>March 3, 2011 Source: Swan, M. Review of cancer risk prediction in direct-to-consumer genomic services. (poster) Canary Foundation Early Detection Symposium, May 25-27, 2010, Stanford University, Stanford CA. Image credit:
  23. 23. Wellness profiling: immune system <ul><li>Immune system genomic wellness profiling </li></ul><ul><li>Immune response: T-cell activation </li></ul><ul><ul><li>CTLA4, CD226, CD86, IL3 </li></ul></ul>March 3, 2011 Source: DIYgenomics Image credit: CTLA4: T-cell inhibition; IL3: growth-promoting cytokine
  24. 24. Agenda <ul><li>Introduction </li></ul><ul><li>Patient-organized genomic studies </li></ul><ul><li>Translational research </li></ul><ul><li>Mobile and web apps </li></ul>March 3, 2011 Image credit: Getty Images
  25. 25. 4,000+ mobile app downloads <ul><li>Health condition, drug response, athletic performance </li></ul><ul><li>23andMe data upload </li></ul><ul><li>Android </li></ul><ul><li>iPhone </li></ul>March 3, 2011 Android development: Michael Kolb, Lawrence S. Wong, Laura Klemme, Melanie Swan iPhone development: Ted Odet, Greg Smith, Laura Klemme, Melanie Swan “ genomics” “ genomics”
  26. 26. Multi-view web app with private data upload March 3, 2011 Private data upload: Marat Nepomnyashy;
  27. 27. Thank you! Melanie Swan Founder DIYgenomics +1-650-681-9482 @DIYgenomics [email_address] Slides: Creative Commons 3.0 license Collaborators: Lorenzo Albanello Cindy Chen John Furber Hong Guo Kristina Hathaway Laura Klemme Priya Kshirsagar Lucymarie Mantese Raymond McCauley Marat Nepomnyashy Ted Odet Roland Parnaso William Reinhardt Greg Smith Aaron Vollrath Lawrence S. Wong International collaborations: JST and Rikengenesis Takashi Kido Minae Kawashima Jin Yamanaka University Hospitals of Geneva Louis Nahum Armin Schnider Personal genome apps Crowd-sourced clinical trials