Patient-Organized Genomic Research Studies

Patient-Organized Genomic Research Studies

Melanie Swan, MBA Founder

DIYgenomics+1-650-681-9482

@DIYgenomics www.DIYgenomics.org

[email protected]

March 3, 2011, Scripps, La Jolla CA

The Future of Genomic Medicine IV conference

Slides: http://slideshare.net/LaBlogga

Personal genome appsCrowd-sourced clinical trials

2March 3, 2011DIYgenomics.org

Agenda

Introduction Patient-organized genomic studies Translational research Mobile and web apps

Image credit: Getty Images


About Melanie Swan

Founder, DIYgenomics Finance, entrepreneurship, technology MBA, Wharton; BA, Georgetown Univ.;

Instructor, 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.


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


Agenda




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+ + =


DIYgenomics study design template: MTHFR

Source: http://diygenomics.pbworks.com/http://diygenomics.pbworks.com/w/file/36469280/DIYgenomics+study+design+template+blank.doc

CyanocobalaminImage credit: http://wikimedia.org


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.


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


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


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


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


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)


Source: DIYgenomics Aging Study, http://diygenomics.pbworks.com/w/page/30326105/Aging


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)


Source: DIYgenomics Aging Study, http://diygenomics.pbworks.com/w/page/30326105/Aging


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


Genomic study platform: Genomera


Agenda




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


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.


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


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


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


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


Agenda




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”


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

Technology

Patient-Organized Genomic Research Studies