DIYgenomics: An Open Platform for Democratizing the Genome
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Transcript of DIYgenomics: An Open Platform for Democratizing the Genome
DIYgenomics: An Open Platform for Democratizing the Genome
Melanie Swan, MBA Founder
DIYgenomics+1-650-681-9482
@DIYgenomics www.DIYgenomics.org
March 7, 2011, San Francisco CA
Slides: http://slideshare.net/LaBlogga
Personal genome appsCrowd-sourced clinical trials
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Agenda
Introduction Application of personal genomic data Patient-organized genomic studies Mobile and web apps
Image credit: Getty Images
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About Melanie Swan
Founder, DIYgenomics Hedge fund manager, futurist MBA, Wharton; BA, Georgetown Univ.;
Faculty, Singularity University Sample publications
Swan, M. Multigenic Condition Risk Assessment in Direct-to-Consumer Genomic Services. Genet. Med. 2010, May;12(5):279-88.
Swan, M. Translational antiaging research. Rejuvenation Res. 2010, Feb;13(1):115-7.
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.
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Information transmission eras
Painting
Transistor(1/0)
DNA(ACTG)
Analog Digital Life code ?
011011000110111110111011001100101 – I love you011011000110111110111010001100101 – I hate you
2010-21001950-201017,300 years ago
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DNA sequencing: 10x/yr improvement
April 1, 2010 Image credit: http://pubs.acs.org/cen/_img/87/i50/8750cover2_law.gif Dec. 14, 2009
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Era of 3rd and 4th generation sequencing3rd Gen: Sequencing by Synthesis
2nd Gen: Parallelized sequencing
1st Gen: Sanger Sequencing
4th Gen: Electronic Sequencing
Sources: http://www.genomicseducation.ca/files/images/information_articles/sequencing.gif, http://www.wellcome.ac.uk/News/2009/Features/WTX056032.htm,http://www.pacificbiosciences.com/video_lg.html, http://www.nanoporetech.com/sequences
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Agenda
Introduction Application of personal genomic data Patient-organized genomic studies Mobile and web apps
Image credit: Getty Images
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Numerous useful applications of genomics
1. Traditional Ancestry Carrier status Identity (paternity, forensics)
2. Maturing Health condition risk Pharmaceutical response
3. Novel Athletic performance OTC product response Toxin processing
4. Predictive wellness profiling Aging, cancer, immune response, organ health
Image credit: http://bit.ly/fovpJc
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Interpreting DNA information
Image credit: http://123RF.com
Example: rs1801133 AG AA, AG, GG
Allele, variant, SNP (single nucleotide polymorphism); “typo” in red; normal in green
A C G T
Example: rs7412 CT CC, CT, TT
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Personal genome results
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Example: what to do with your data
Check if you have the risk allele for the BDNF gene Determine related SNP/rsID#, rs6265 (neuroplasticity) Search genomic data for rs6265 genotype (e.g., CC) Determine the risk allele (which letter?) (e.g.; G1) Opportunity: genomic database with three minimal fields:
gene, rsid# and risk allele Current genomics search resources
PharmGKB, dbSNP, GWAS catalog, SNPedia
Source: http://www.wired.com/wiredscience/2009/10/genetically-bad-driving1Ribeiro, L. et. Al., The brain-derived neurotrophic factor rs6265 (Val66Met) polymorphism and depression in
Mexican-Americans. Cellular, Molecular and Developmental Neuroscience. May 8, 2007.
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Finding your BDNF data, variant rs6265
Consumer genomic services genotype 600,000-1m variants but only map a few up to the annotation browser
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Agenda
Image credit: Getty Images
Introduction Application of personal genomic data Patient-organized genomic studies Mobile and web apps
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DIYgenomics philosophy
Goal: preventive medicine Realize preventive medicine by establishing baseline markers
of wellness and pre-clinical interventions
Generalized hypothesis One or more polymorphisms may result in out-of-bounds
baseline levels of phenotypic markers. These levels may be improved through personalized intervention.
Source: http://diygenomics.pbworks.com/MTHFR
Genotype Phenotype Intervention Outcome+ + =
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DIYgenomics study design template
Source: http://diygenomics.pbworks.com/http://diygenomics.pbworks.com/w/file/36469280/DIYgenomics+study+design+template+blank.doc
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DIYgenomics study #1: MTHFR
Do common mutations in the MTHFR gene prevent vitamin B from working correctly?
Test whether 2 variations in the MTHFR gene (methylenetetrahydrofolate reductase) keep vitamin B9 (folic acid) from being metabolized into its active form (folate) rs1801133/C677T rs1801131/A1298C
Without this form of vitamin B, homocysteine may accumulate Risk: nutritional deficiencies and symptoms
associated with diabetes complications, including vascular damage and nerve damage
50% or more of the population may have some form of MTHFR polymorphism
Source: http://diygenomics.pbworks.com/MTHFR
CyanocobalaminImage credit: http://wikimedia.org
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Homocysteine metabolism pathway
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.
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MTHFR pilot study results
Drug store vitamin (Centrum) reduced homocysteine levels for 6/7 participants
Blood Test #
2. Homocysteine levels
DIYgenomics MTHFR Vitamin B deficiency study1
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.
1Results 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
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DIYgenomics studies
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
Image credit: http://www.narsad.org/?q=node/11245
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Memory filtering study
Dopaminergic modulation of memory filtering Collaboration with the Laboratory of Cognitive Neuro-
Rehabilitation, University Hospitals of Geneva IRB and informed consent Goal: 100 member healthy control cohort
Genotype: COMT, DRD2, SLC6A3 (~5 SNPs) (neurotransmitter modulation)
Phenotype: memory filtering test (24 minutes) and reversal learning task (10 minutes)
Background questionnaire: neurological or psychiatric antecedents, medications, demographic information, and IQ (WAIS, Raven’s Matrices)
Image credit: http://bit.ly/g2DIcW
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DIYgenomics aging study
Genomics + physical biomarkers + intervention Top twenty genomic mechanisms of aging
1. Aging-specific genetics (IGF signaling, DNA repair, etc.)
2. Diabetes and metabolic disease
3. Catabolism (waste removal) and other
4. Heart disease and blood operations
5. Cancer
Top twenty phenotypic biomarkers of aging Interventions
Traditional Novel (example: telomerase activation therapies)
More information: http://diygenomics.pbworks.com/Aging
Image credit: http://www-nmr.cabm.rutgers.edu
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Innovating the research model
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
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Genomic study platform: Genomera
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Agenda
Introduction Application of personal genomic data Patient-organized genomic studies Mobile and web apps
Image credit: Getty Images
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4,000+ mobile app downloads
Health condition, drug response, athletic performance
23andMe data upload
Android
iPhone
Android development: Michael Kolb, Lawrence S. Wong, Laura Klemme, Melanie SwaniPhone development: Ted Odet, Greg Smith, Laura Klemme, Melanie Swan
“genomics”
“genomics”
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Multi-view web app with private data upload
Private data upload: Marat Nepomnyashy; https://addons.mozilla.org/en-US/firefox/addon/156946
Thank you!
Melanie SwanFounder
DIYgenomics+1-650-681-9482
@[email protected]: http://slideshare.net/LaBloggaCreative 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
Personal genome appsCrowd-sourced clinical trials
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
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Consumer genomic comparison
Three companies cover colorectal cancer Different genes and variants are reviewed
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Drug response
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Ranking variant quality
Background NIH plan to develop a Genetic Testing Registry (2011) Criteria proposed by Ioannidis JP et al. Int J Epidemiol. (2008):
amount of evidence, replication, protection from bias
Methodology: assign a composite score to each variant per the number of cases and controls, p-value, odds ratio, and journal ranking
Source: DIYgenomics
Ex: Alzheimer's disease Case Ctrl Repli- Protectn p-value Odds Journal Composite cation fr Bias ratio rank rank (1-5)
CLU rs11136000 3,941 7,848 Y Y 2.00E-157 2.53 22 5APOE rs429358 664 422 Y 1E-39 4.01 3151 4APOE rs7412 5,930 8,607 0 4.38 86 4
Image credit: http://www.evexiscy.com
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Athletic performanceCategory 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
Image credit: http://www.istockphoto.com
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.
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OTC product response, toxin processing
OTC product response Skin (premature aging, wrinkles, sun damage, eczema,
irritation, antioxidant treatment, anti-aging treatment) Hair (hair loss, premature greying, male pattern baldness) Esophagus (reflux, bile acid response) Teeth (periodontitis) Sleep (daytime sleepiness, caffeine-induced insomnia)
Environmental exposure: toxin processing Benzene Quinone oxidoreductase PAHs metabolism Arylarene metabolism Mercury and lead exposure Liver and kidney health (general)
Source: DIYgenomics
Image credit: http://sciencephoto.com
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Predictive wellness profiling: cancer
Proto-oncogene/tumor suppressor gene polymorphisms
Source: DIYgenomics
Image credit: http://utmb.edu
Alleles 23andMe alleles
Gene RSID Poss Unf Fav Poss Fav Ex p-value OR Case Ctrl Citation
TP531 rs1042522 CG C G CG G CG 0.77 1.23 685 778 Joshi 2010
TP53 rs1860746 GT T G n/a n/a n/a 0.04 1.47 6,127 5,197 Liu 2009
MDM22 rs2279744 GT G T GT T GT 0.91 1.27 685 778 Joshi 2010
MDM41 rs1380576 CG G C n/a n/a n/a 0.95 1.03 4,073 n/a Sun 2010
HAUSP1 rs1529916 AG G A n/a n/a n/a 0.07 1.05 4,073 n/a Sun 2010
PTEN1 rs701848 CT C T CT T CT 0.00 0.12 53 107 Hosgood 2010
PTEN1 rs1903858 AG G A AG A AA 0.01 0.13 53 107 Hosgood 2010
BCL22 938C>A AC A C n/a n/a n/a 0.05 n/a 40 40 Fingas 2010
GNB32 rs5443 CT T C CT C CC 0.05 n/a 40 40 Fingas 2010
MYC2 rs6983267 GT G T GT T TT 0.00 1.21 930 960 Tomlinson 2007
MYC rs1050477 AC A C GT G GG 0.00 1.17 7,480 7,779 Zanke 2007 MYC rs7014346 AG A G AG G GG 0.00 1.19 14,500 13,294 Tenesa 2008
1Tumor Suppressor, 2Proto-oncogene
TP53: cell cycle arrest, PTEN: cell cycle progression modulator, MYC: cell cycle regulator
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Lung cancer risk and drug response
Risk and drug response for specific cancers
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: http://www.xianet.net
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Wellness profiling: immune system
Immune system genomic wellness profiling Immune response: T-cell activation
CTLA4, CD226, CD86, IL3
Source: DIYgenomics
Alleles 23andMe alleles
Gene RSID Poss Unf Fav Poss Fav Ex p-value OR Case Ctrl Citation CTLA4 rs231775 A/G A G AG G AA 0.007 0.642 172 145 Duan 2010 CTLA4 rs5742909 C/T C T CT T CC 0.098 0.67 172 145 Duan 2010 CTLA4 rs733618 C/T C T CT T TT 0.041 4.62 269 395 DallaCosta 2010 CD226 rs763361 C/T T C CT C CC 0.000 1.22 1,990 1,642 Dieudé 2010 CD86 rs1129055 A/G G A AG A GG 0.006 0.51 269 395 DallaCosta 2010 IL3 rs181781 A/G A G AG G GG 0.041 0.55 60 270 Lee 2010 IL3 rs2073506 A/G A G CT C CC 0.009 0.32 60 270 Lee 2010 IL3 rs40401 C/T T C CT C CC 0.014 2.18 60 270 Lee 2010
Image credit: http://www.iayork.com
CTLA4: T-cell inhibition; IL3: growth-promoting cytokine
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Aging (genomic markers)
Top twenty genomic mechanisms of aging (1,000 variants in DIYgenomics Aging GWAS database)
1. Aging-specific genetics (overall profile, IGF-1/insulin signaling, inflammation, immune system, DNA damage repair, cell cycle, telomere length, mitochondrial health)
2. Diabetes and metabolic disease (cholesterol, obesity, adiposity, fat distribution)
3. Catabolism (waste removal) and other (Alzheimer’s disease, macular degeneration, rheumatoid arthritis, osteoporosis, sarcopenia, kidney and liver disease)
4. Heart disease and blood operations (cardiovascular disease, atherosclerosis, myocardial infarction, atrial fibrillation)
5. Cancer (profile for twenty cancers including breast, prostate, colorectal, lung, melanoma, glioma, ovarian, pancreatic)
Image credit: http://reisearch.net/biotech_page/dna_horizontal.gif
Source: DIYgenomics Aging GWAS Database: http://bit.ly/fDobgO
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Aging (phenotypic markers)
Top twenty phenotypic biomarkers of aging 1. Aging-specific markers (telomere length, lymphocyte
regeneration, CD levels, inflammation, hormone levels)
2. Diabetes and metabolic disease (BMI, cholesterol (HDL/LDL/triglycerides; LDL particle size), Framingham Risk Score, fasting glucose, non-fasting glucose, albumin, uric acid)
3. Catabolism and other (VO2 max, bone mineral density, muscle mass, GOT, GPT, creatinine, eGFR)
4. Heart disease and blood operations (blood pressure, hematocrit, hemoglobin, RBC, WBC, CRP, platelets, erythrocyte glycoslyation)
5. Cancer (granulocyte strength, blood-assay)
Image credit: http://reisearch.net/biotech_page/dna_horizontal.gif
Source: DIYgenomics Aging Study, http://diygenomics.pbworks.com/w/page/30326105/Aging
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Aging (interventions)
Top twenty aging interventions1. Traditional (exercise, nutrition, sleep, vitamins, stress-
reduction)
2. Novel (brain fitness programs and mid-life cholesterol management for Alzheimer’s disease, cholesterol management with CETP-inhibitor anacetrapib, 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)
Image credit: http://reisearch.net/biotech_page/dna_horizontal.gif
Source: DIYgenomics Aging Study, http://diygenomics.pbworks.com/w/page/30326105/Aging
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Would you like to know… …if your vitamin supplements are effective …which vitamin supplements would be effective and
best for you …personalized interventionswhat conditions you are at
higher genetic susceptibility for and are …your predictive wellness profile for aging, cancer,
immune system response