Public Genomics Enabling Responsible
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PGP: A Structural Overview
Enabling Responsible Public Genomics: A Roadmap Technological Development � Rich Datasets 1. 2. Expanding Genomic Research Space Novel Ethical Issues � Novel Legal Issues, 3. Uncertainty 4. PGP as a Model for Responsible Public Genomics? 5. Goal: Ethical Thoughtfulness & Self-Regulation 6. Result: Enabling Responsible Public Genomics
Target: The $1,000 Genome
Understanding Phenotypic Complexity: G x E Individual Phenotype Genetics Environment Epigenetics
An Expanding Genomics Research Space public public enrollment enrollment private public private public data data data data 1000 Genomes A Deep Catalog of Human Genetic Variation private private enrollment enrollment
Defining “Public Genomics” • Globally Available . • Individually Identifiable . Misuse of data? Protect privacy while acknowledging limitations, risks? • Portable and Decentralized . • Voluntary and Informed Participation . Pillaging instead of seeding the genomic commons? Limited / biased population?
ELSI in Public Genomics • Ethical – Ensure privacy, veracity and other norms are upheld given new technology, richer datasets (privacy, de-identification) – Use thoughtful, truly “informed” consent and risk disclosure as a means to investigate and address known and unknown risks • Legal – Understand existing landscape and anticipate new regulation (DTC, privacy) and legal duties (incidental findings) – Resolve and develop uncertain legal framework in a way that enables public genomics • Social – Expand access to participation, address sample bias, pursue participant-/patient-driven agendas – Consider benefits and drawbacks of “open” and “public” genomics models
An Expanding Genomics Space w w a a L L � � � � e e c c r r e e m m m m o o C C & & e e c c n n e e i i c c S S � � � � y y g g o o l l o o n n h h c c e e T T
Public Genomics: From Research to Regulation public public Self-Regulation enrollment enrollment Federal Regulation State Regulation private private public public data 1000 Genomes data data data A Deep Catalog of Human Genetic Variation 1000 Genomes A Deep Catalog of Human Genetic Variation Int’l Norms private private enrollment enrollment
Federal Regulation Common CLIA HIPAA Rule Congress Legislates GPMA? FDCA GINA Agencies Interpret and Enforce
State Positive Law • Unauthorized Practice of Medicine – Prohibited by all 50 states; 50 different standards – Corporate practice of medicine issues • Privacy – Genetic privacy statutes overlap with existing federal and state regulations of medical information – Fact specific application varies with use, e.g., clinical vs. criminal/forensic • Direct to Consumer Testing – Regulated by roughly half of the states – Significant differences in substantive terms, application and enforcement
State Common Law • Negligence: Duties Owed to Research Subjects − Duty to warn of risks − Duty to disclose information (completely and accurately) − Duty to prevent unauthorized disclosure − Use Informed Consent to mitigate risk • Medical Malpractice – Dr/Patient Relationship − Duties to patients much greater than duties to research subjects • Evolving Standards − Duties to third parties? − Duty to report, interpret “incidental” findings?
Traps for the Unwary & A Need for Self-Regulation • Criminal and civil penalties • $$$ $$$ damages • • Activities prohibited • Costs of compliance • Enforcement/Litigation a certainty • 50 State Legislatures, 50 State AGs • 300+ million potential plaintiffs
Participatory Research Study: 100,000 Human Subjects Tissue Samples Online Trait Questionnaires • disease history • lifestyle + habits • medications • environmental exposures • allergies • Blood, skin (tissue • 3-D facial photos • nutrition biopsy), saliva, hair, etc. Biological Analyses Longitudinal Follow-Up • DNA sequencing • Adverse experience reporting • RNA expression • Clinical follow-up? • Creation of Cell Lines • Microbiomics
The “PGP-10” 1. George Church , Ph.D. Professor of Genetics, 1 6 1 6 Harvard Medical School 2. John Halamka , M.D. M.S. CIO and Dean for Technology, Harvard Medical School 3. Misha Angrist , Ph.D. Science editor, Duke University Institute for Genome Sciences & Policy 2 7 2 7 4. Keith Batchelder , M.D. Founder and CEO, Genomic Healthcare Strategies 5. Kirk Maxey , M.D., former CEO, Cayman Chemical Esther Dyson . Genomic and biotechnology 6. 3 8 3 8 company investor and board member 7. James Sherley , M.D. Ph.D. Senior Scientist, Boston Biomedical Research Institute 8. Stephen Pinker , Ph.D. Professor of Psychology, 4 4 9 9 Harvard University 9. Rosalynn Gill , Ph.D. Founder and Chief Science Officer, Sciona. 10. Stanley Lapidus . Chairman and CEO, Helicos 5 5 10 10 BioSciences Corp.
PGP Primary Objectives • Rich, Public Dataset − Enrollment: PGP-10 (2008), PGP-100 (ongoing) − Publication of genotype/phenotype data at http://wwww.personalgenomes.org/ • Public Education − Separate pre-enrollment consent, educational tools and screening (100% req); PGP study guide − Robust informed consent and risk disclosure • Affordable Technology, Interpretation − Open-source wetware (POLONATOR) and bioinformatics (Traitomatic)
Is Genomic Privacy Possible? Examples of Privacy Imperfections : • Re-identification using publicly available data (dbGaP, inferred SSNs) • Inferring phenotype from genotype by identifying information in DNA • Identification through the DNA of a 1st-degree relative • Surreptitious sequencing • Theft, loss of data or hacking of storage devices with de-identified data As genomic data transitions from SNPs to sequence, and as genotypes are increasingly correlated to phenotypes, this problem will worsen. “Any amount of DNA data in the public domain with a name allows for identification within any anonymized data set.” Lunshof J, Chadwick R, Vorhaus D, Church G. (2008) From Genetic Privacy to Open Consent. Nature Reviews Genetics “Though counter-intuitive, our findings show a clear path for identifying whether specific individuals are within a study based on summary-level statistics.” Homer N, Szelinger S, et. al. (2008) Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays. PLoS Genetics 4(8)
Reevaluating Privacy Promises A Comparison of Study Consent Language: “No personal identifying information about you will be released to anyone outside of the CPMC study without your authorization…” -- CPMC Informed Consent Agreement (not publicly available ) “If you are enrolled in the PGP, genetic and trait information will not be maintained or made available in a confidential or anonymous fashion. Your genetic and trait information will be made available via a publicly accessible website and database…” -- PGP Informed Consent Agreement (http://www.personalgenomes.org/consent/PGP_Consent_Approved03242009.pdf)
PGP’s Objective: “Open” Consent Structural Features • Research participants are active “co-investigators” • Entrance exam to ensure highly informed consent • Ongoing review of consent protocols (IRB, DSMB, questionnaires, etc.) Open Consent • Do not over-promise privacy or confidentiality protections; promote veracity and participant autonomy • Acknowledge and accept both known and unknown risks “we cannot always foresee the results of research, so new risks may come up in the future 1000 Genomes that we cannot predict now.” A Deep Catalog of Human Genetic Variation From 1,000 Genomes Informed Consent Form Template (http://www.1000genomes.org/)
Enabling Responsible Public Genomics � Responsible and Thoughtful Self-Regulation � Public Sharing, Dialogue and Guidance � Necessary and Informed Regulation � Development of Common Law
QUESTIONS OR COMMENTS? dvorhaus@rbh.com or Genomics Law Report http://www.genomicslawreport.com/
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