DOCSLIB.ORG

The Views of Participants in DNA Biobanks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Views of Participants in DNA Biobanks The Views of Participants in DNA Biobanks Kelly E. Ormond,I Maureen E. Smith,II & Wendy A. WolfIII Abstract Biobanks are generally created with the long-term goal of establishing genotype-phenotype correlations. These resources collect and link DNA with health information for use in future genetic research studies. The biobanking process can vary with regard to specific characteristics in study design. Biobanks may extract DNA by using DNA from leftover samples or obtaining new DNA samples specifically for the biobank. Biobanks also vary in whether they use an opt- in or opt-out informed consent process. Some biobanks collect health information at a single point in time, while others “re-access” such information at designated points in the future to categorize subjects accurately into affected/unaffected status. These study design differences can influence the perception of participants and affect their willingness to participate. This paper will address the following three key issues: (1) why people decide to participate in DNA biobanks, (2) what enrollees understand about their participation in DNA biobanks, and (3) how participants feel about the possibility that biobanks will re-contact them, either to obtain new information for future studies or to share potential study results. Finally, we will suggest how information related to these three key issues ought to inform future study design for biobanks. I. INTRODUCTION.................................................................................................................81 II. WHY DO PEOPLE DECIDE TO PARTICIPATE IN DNA BIOBANKS?...........................82 III. WHAT DO ENROLLEES UNDERSTAND ABOUT THEIR PARTICIPATION IN A DNA BIOBANK? ..............................................................................................................................83 IV. HOW DO PARTICIPANTS FEEL ABOUT BIOBANK RE-CONTACT, AND HOW SHOULD THIS INFORMATION INFORM STUDY DESIGN?..............................................84 V. CONCLUSION....................................................................................................................87 I Stanford University, Department of Genetics, Stanford, CA. II Northwestern University, Center for Genetic Medicine, Chicago, IL. III Northwestern University, Center for Genetic Medicine, Chicago, IL. 81 STANFORD JOURNAL OF LAW, SCIENCE & POLICY Vol. 1 I. INTRODUCTION DNA biobanks are created with the long-term goal of establishing genotype-phenotype correlations through collecting and linking DNA with health information. Researchers can use these correlations in research studies that are typically not yet conceived when the biobanks are created. These biobanks may utilize the stored DNA for discovery research, which may include targeted genomic tests, genome-wide association studies,1 and potentially whole genome sequencing studies in the future. The ethical issues that surround DNA biobanks depend largely on the study design and methods utilized by each biobank, and it is therefore important to understand the variety of ways that biobanks can differ in study design and methods. First, biobanks may extract DNA from leftover clinical samples2 or obtain new samples specifically intended for usage in a specific biobank.3 Second, biobanks may use different subject recruitment processes; biobanks can be population-based, narrowly focused on specific medical conditions with a “control” population, or broadly focused on a range of disorders varying in frequency, treatability, severity and social stigma.4 The subject recruitment process may either request subjects to “opt in” (so that a potential subject makes an active decision to participate subsequent to an informed consent process), or to “opt out” (so that a potential subject is assumed to enroll unless the subject actively declines). The “opt out” option is characteristic of a “presumed consent” model, where participants are presumed to enroll in biobanks after obtaining healthcare in certain settings.5 Finally, the last study method where biobanks differ is in their timing for collecting health information; they may collect health information at a single point in time or may “re-access” such information at designated points in the future to categorize subjects accurately as affected or unaffected. Some biobanks choose to have re-contact options so that they can follow up with participants with results from past studies or with requests to obtain consent for future specific studies.6 The health informatics required for these options may require varying levels of de- identification. Coded samples typically involve the efforts of biobank investigators to link the sample with a code rather than with personally identifiable information, such as a name or social security number. (However, investigators may maintain some links to participants in order to re- deposit genomic information and to re-contact participants in the future). Researchers who 1 These studies may include, for example, the assessment of assessing low penetrance genes for common diseases or the search for modifier genes that impact phenotype. 2 See Dan Roden et al., Development of a Large-Scale De-Identified DNA Biobank to Enable Personalized Medicine, 84 CLINICAL PHARMACOLOGY AND THERAPEUTICS 362, 364, 367 (2008) (discussing how a majority of the samples was collected from leftover blood samples). 3 See Catherine A. McCarty et al., Marshfield Clinic Personalized Medicine Research Project (PMRP): Design, Methods and Recruitment for a Large Population-Based Biobank, 2 PERSONALIZED MED. 49, 52 (2005) (discussing how DNA was extracted from each patient). 4 See Lyle J. Palmer, UK Biobank: Bank on It, 369 THE LANCET 1980 (2007); Greta Lee Splansky et al., The Third Generation Cohort of the National Heart, Lung, and Blood Institute’s Framingham Heart Study: Design, Recruitment, and Initial Examination, 165 AM. J. EPIDEMIOLOGY 1328 (2007). 5 See Melissa A. Austin et al., Genebanks: A Comparison of Eight Proposed International Genetic Databases, 6 COMMUNITY GENETICS 37, 39 (2003) (describing presumed consent and the opt-out option in Icelandic Health Center Database); Roden et al., supra note 2, at 365–66 (describing the advantages and limits of opt-out study design). 6 Kelly E. Ormond et al., Assessing the Understanding of Biobank Participants, 149 AM. J. MED. GENETICS 188, 189–90 (2009). 2010 THE VIEW OF PARTICIPANTS IN DNA BIOBANKS 82 utilize the samples typically receive only the coded samples and do not have access to the identification links. De-identified data sets include none of the eighteen HIPAA protected data points, while limited data sets may include either dates, geographic data, or both. Samples that have no link to identifiable information preclude biobanks from updating participants with results or re-contacting again to discover further health information. All of these different study methods influence the ethical issues related to biobank participation. This paper will focus on three key issues relevant to biobanks and their related ethical considerations: (1) why people decide to participate in DNA biobanks, (2) what enrollees understand about their participation in a DNA biobank, and (3) how participants feel about biobank re-contact, whether to give new information for future studies or to receive aggregate or individual study results. After addressing these concerns, we will conclude by proposing how information about these issues should inform study design for DNA biobanks. II. WHY DO PEOPLE DECIDE TO PARTICIPATE IN DNA BIOBANKS? Several studies have addressed the reasons why enrollees do or would participate in DNA biobanks7 or other genetic research studies.8 One important stated reason is altruism;9 participants report that they feel good “helping mankind” or want to give back after facing illness in themselves or family members. Less frequently, participants express that they want to contribute to the general knowledge for science, medicine, and genetics, or that they want to assist researchers with building a database that will aid the discovery of specific disease genes or genotype/phenotype correlations and subsequent research on treatments or cures. Other factors that appear to influence participation in DNA biobanks include general interest in science, genetics, or research participation, trust in the sponsoring institution,10 ease of participation (especially if no additional blood sample or time is required), and the perceived low risk of having a blood test. Finally, as will be discussed later in the paper, some participants report that they chose to participate because of the potential to gain personal or family benefits, such as improved testing or treatment.11 Several studies suggest that a majority of potential enrollees are comfortable with the study concepts and purposes12 and would consider participation in a DNA biobank.13 With that said, actual enrollment in biobanks varies significantly based on each biobank’s study design and 7 See Laura M. Beskow & Elizabeth Dean, Informed Consent for Biorepositories: Assessing Prospective Participants’ Understanding and Opinions, 17 CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION 1440 (2008); Catherine A. McCarty et al., Informed Consent and Subject Motivation to Participate in a Large, Population-Based Genomics Study: The Marshfield Clinic Personalized Medicine Research Project, 10 COMMUNITY GENETICS 2 (2007); Ormond et al., supra note 6, at 191. 8 See Donald J. Willison et al., Patient Consent Preferences for Research Uses of Information
Load more

Recommended publications
  • Estimation of DNA Contamination and Its Sources in Genotyped Samples
    Gregory Zajac ORCID iD: 0000-0001-6411-9666 Estimation of DNA contamination and its sources in genotyped samples Gregory J. M. Zajac1; Lars G. Fritsche1; Joshua S. Weinstock1; Susan L. Dagenais2; Robert H. Lyons2; Chad M. Brummett3; Gonçalo. R. Abecasis1 1) Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI; MI; 2) Department of Biological Chemistry and DNA Sequencing Core, University of Michigan, Ann Arbor, MI; 3) Department of Anesthesiology, Division of Pain Medicine, University of Michigan Medical School, Ann Arbor, MI. Corresponding author: Gregory JM Zajac 734-763-5315 [email protected] University of Michigan School of Public Health - Department of Biostatistics 1415 Washington Heights Ann Arbor, MI 48109-2029 Grant Numbers: HG007022 Author Manuscript This is the author manuscript accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/gepi.22257. This article is protected by copyright. All rights reserved. Abstract Array genotyping is a cost-effective and widely used tool that enables assessment of up to millions of genetic markers in hundreds of thousands of individuals. Genotyping array data are typically highly accurate but sensitive to mixing of DNA samples from multiple individuals prior to or during genotyping. Contaminated samples can lead to genotyping errors and consequently cause false positive signals or reduce power of association analyses. Here, we propose a new method to identify contaminated samples and the sources of contamination within a genotyping batch.
    [Show full text]
  • Mycode®: Engineering the Perfect Biobank
    Linking the Research Community • Spring 2015 A message from MyCode®: Engineering the David H. Ledbetter, PhD Perfect Biobank Geisinger’s biobank and the MyCode® Community Health This issue of Research Initiative began in 2004 with a phone call from Glenn D. Connections is devoted to the Steele Jr., MD, PhD, Geisinger’s former president and CEO, to MyCode® Community Health David H. Ledbetter, PhD, then at Emory University’s School Initiative, a project with the of Medicine. Speaking genetically, not meteorologically, Steele potential to change significantly described Geisinger’s Pennsylvania footprint and resources to health and healthcare. MyCode Ledbetter as being, “as close to Iceland as you’ll ever find in the will uncover new connections United States.”* between genes and disease, inform and improve the health Ledbetter, now Geisinger’s Chief Scientific Officer, found many of Geisinger’s patients and re- similarities between Iceland and Geisinger’s central Pennsylvania envision how we approach our own well-being and that of location. Like Iceland, Geisinger’s regional population was stable, our families. often with three generations in the area. In addition, Geisinger‘s healthcare was centralized with a robust electronic health record This project is possible because of Geisinger’s specific (EHR) in place since 1996. combination of assets: an established biobank; an extensive and robust electronic health record (EHR); and According to Ledbetter, the qualities listed above, along with international expertise in bioethics, medical genetics, Geisinger’s tradition of innovation, creates, “a healthcare genetic counseling and genomics. Our partnership with laboratory and an ideal model to learn when and how genetic Regeneron, an innovative biotechnology company with information can improve health.” Ledbetter described Geisinger’s extensive resources for whole exome sequencing, is key service area as, “the ideal place in the United States to do to positioning this project to make groundbreaking longitudinal, large scale, genomic medicine research.” discoveries.
    [Show full text]
  • Bioethics and Large-Scale Biobanking: Individualistic Ethics and Collective Projects*
    Genomics, Society and Policy 2005, Vol.1, No.2, pp.50–66. Bioethics and large-scale biobanking: individualistic ethics and collective projects* GARRATH WILLIAMS Abstract Like most bioethical discussion, examination of human biobanks has been largely framed in terms of research subjects’ rights, principally informed consent, with some gestures toward public benefits. However, informed consent is for the competent, rights-bearing individual: focussing on the individual, it thus neglects social, economic and even political matters; focussing on the competent rights-bearer, it does not serve situations where consent is plainly inappropriate (eg, the young child) or where coercion can obviously be justified (the criminal). Using the British experience of large-scale biobanking, I argue that the focus on consenting individuals distorts our ways of thinking about biobanks and has serious practical ramifications. This becomes clear if we contrast the case of adult biobanks intended for medical research with two other forms of biobanking. Thus child cohort studies – vital for sound scientific investigation of the interplay of genetics and environment in health – have been very badly funded next to adult studies. On the other hand, forensic databases have attracted massive investment, but little debate – partly owing to a sense that here, at least, is a case where consent is not relevant. Contrasting these central types of biobanking, I will suggest that there are powerful factors at work in limiting ‘ethics’ to individual rights. Projects of this size should direct our attention to more overtly political questions concerning priority setting and organisation of medical research. Introduction In this paper I wish to cast a critical eye at the way in which we – meaning both bioethicists and practitioners – frame ethical and bioethical discussion.
    [Show full text]
  • Fifty Years of Genetic Epidemiology, with Special Reference to Japan
    J Hum Genet (2006) 51:269–277 DOI 10.1007/s10038-006-0366-9 MINIREVIEW Newton E. Morton Fifty years of genetic epidemiology, with special reference to Japan Received: 13 December 2005 / Accepted: 18 December 2005 / Published online: 15 February 2006 Ó The Japan Society of Human Genetics and Springer-Verlag 2006 Abstract Genetic epidemiology deals with etiology, dis- Introduction tribution, and control of disease in groups of relatives and with inherited causes of disease in populations. It The Japan Society of Human Genetics (JSHG) was took its first steps before its recognition as a discipline, established at a time when the science it represented was and did not reach its present scope until the Human growing so explosively that Carlson (2004) signalised the Genome Project succeeded. The intimate relationship death of classical genetics. Compared to their predeces- between genetics and epidemiology was discussed by sors, geneticists in 1956 were more distrustful of eugenics Neel and Schull (1954), just a year after Watson and and more committed to cytogenetics, biochemistry, and Crick reported the DNA double helix, and 2 years be- medical genetics, but not yet prepared for the genomic fore human cytogenetics and the Japan Society of Hu- revolution that the double helix would ultimately bring man Genetics were founded. It is convenient to divide (Yanase 1997). Population genetics was beginning to the next half-century into three phases. The first of these split into two major branches, one concerned with events (1956–1979) was before DNA polymorphisms were that took place in the past under poorly known forces of typed, and so the focus was on segregation and linkage systematic pressure and chance (evolutionary genetics), of major genes, cytogenetics, population studies, and the other dealing with etiology, distribution, and control biochemical genetics.
    [Show full text]
  • Manual for Conducting a Large-Scale GWAS and Whole Genome
    (COVNET) NIH Lead Investigator: Stephen Chanock, M.D. ([email protected]) Director, Division of Cancer Epidemiology and Genetics, NCI NIH Investigators: Sharon Savage, M.D. ([email protected]) Lisa Mirabello, Ph.D. ([email protected]) Meredith Yeager, Ph.D. ([email protected]) Mitchell Machiela, Sc.D. ([email protected]) Joshua Sampson, Ph.D. ([email protected]) Amy Hutchinson, M.S. ([email protected]) Belynda Hicks, M.S. ([email protected]) Division of Cancer Epidemiology and Genetics, NCI Mary Carrington, Ph.D. ([email protected]) Center for Cancer Research, NCI Leslie Biesecker, M.D. ([email protected]) Teri Manolio, M.D., Ph.D. ([email protected]) National Human Genome Research Institute Steven Holland, M.D. ([email protected]) National Institute of Allergy and Infectious Diseases Project Manager Vibha Vij, M.S., MPH ([email protected]) Division of Cancer Epidemiology and Genetics, NCI Website: https://dceg.cancer.gov/research/how-we-study/genomic-studies/covnet Table of Contents Sections 1.0 Background and Rationale for a Large Crowdsourced GWAS of COVID19 Infection ...... 2 2.0 Biospecimens: genomic DNA, blood/blood products, buccal cells/saliva .......................... 6 3.0 Patient Phenotypes ............................................................................................................. 10 4.0 Genotyping and Imputation ............................................................................................... 10 5.0 Analysis Plan ....................................................................................................................
    [Show full text]
  • Genetic Data Aren't So Special: Causes and Implications of Re
    Genetic Data Aren’t So Special: Causes and Implications of Re-identification T.J. Kasperbauer & Peter H. Schwartz Indiana University Center for Bioethics Indiana University School of Medicine This is the pre-peer-reviewed version of the article published here: https://doi.org/10.1002/hast.1183 Full citation: Kasperbauer, T.J. & Schwartz, P.H. (2020). Genetic data aren’t so special: Causes and implications of re-identification. Hastings Center Report, 50, 30-39. Abstract: Genetic information is widely thought to pose unique risks of re-identifying individuals. Genetic data reveals a great deal about who we are, and, the standard view holds, should consequently be treated differently from other types of data. Contrary to this view, we argue that the dangers of re-identification for genetic and non-genetic data—including health, financial, and consumer information—are more similar than has been recognized. Before we impose different requirements on sharing genetic information, proponents of the standard view must show that they are in fact necessary. We further argue that the similarities between genetic and non-genetic information have important implications for communicating risks during consent for healthcare and research. While patients and research participants need to be more aware of pervasive data sharing practices, consent forms are the wrong place to provide this education. Instead, health systems should engage with patients throughout patient care to educate about data sharing practices. Introduction Genetic data and biological samples containing genetic material are widely thought to differ from other sorts of health data due to the impossibility of truly “de-identifying” genetic information.
    [Show full text]
  • Sample Data Sharing Consent Form
    PREAMBLE: NINDS has the expectation that investigators will use broad consent language that allows for the storing, sharing, and use of human subjects data and specimens for future research, even if such future research may be unrelated to the original study or disorder for which the data and/or specimens were collected. NINDS expects investigators to consider including such language (or similar language as mandated by your IRB) in your consent forms in order to ensure that samples and data are available for storing, sharing and use in future, unspecified research. This language should be included in the draft consent submitted with your application and submitted to NINDS in preparation for your start-up meeting if your application is funded (EXHIBIT 1). If genetic sample collection is a component of the study, then consent language is expected to include the sharing of large-scale genomic data and associated phenotypic data in the NIH Database of Genotypes and Phenotypes (dbGaP: http://www.ncbi.nlm.nih.gov/gap). If another database is chosen, this would require pre-approval by NINDS program staff. A final, IRB-approved consent that includes this or similar language (or an explanation for why it was not included) should be submitted to NINDS program staff prior to study activation. If you have questions or need assistance developing your consent language, please feel free to contact NINDS Clinical Research Liaison (any consents) and/or Ran Zhang (consents with genetic/genomic data language). *Please note that all studies that generate large-scale human genomic data , regardless of cost, should refer to the the NIH Genomic Data Sharing (GDS) Policy for additional requirements.
    [Show full text]
  • M21-515 Fundamentals of Genetic Epidemiology Summer 2018
    M21-515 Fundamentals of Genetic Epidemiology Summer 2018 Revised: 7/13/2018 Course Masters Treva Rice, Ph.D. ([email protected]) Yun Ju Sung, Ph.D. ([email protected]) Lab Instructor Oyomoare Osazuwa-Peters ([email protected]) Grading Computer Lab Assignments 30% Daily Quiz 30% Midterm Exam (covers lectures / homework 1-5) 20% Final Project 20% Final Grade (+/– letter grades) Software Software packages include: 1. R (http://www.r-project.org/) 2. PEDSTATS, Merlin, QTDT (http://www.sph.umich.edu/csg/abecasis) 3. PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) Textbook Austin, MA. Genetic Epidemiology: Methods & Applications 2013, CABI: Oxfordshire, UK. Format 2-week INTENSIVE course Morning lecture: 9:00 am – 12:00 noon Afternoon computer lab: 1:30 pm – 4:00 pm 10-minute break ~ every hour 1st Homework Due first day of class, Textbook (Chapters 1-2) Assignment On-line genetics tutorial, Chapters 3, 4, and 6 (http://anthro.palomar.edu/tutorials/biological.htm) Prerequisite 1) Knowledge of R programming, either having taken the MSIBS summer R-course or experience in R-programming; 2) experience in Linux/Unix operating system. The core competency for the Fundamentals of Genetic Epidemiology course (M21-515) is for students to understand basic concepts, methods and analytical approaches in genetic epidemiology. Learning objectives are to • Understand familial resemblance, heritability and family study designs • Appreciate maximum likelihood methods and hypothesis testing • Be aware of selected molecular and population genetics principles, including Hardy-Weinberg Equilibrium • Grasp the basic concepts and principles underlying genetic linkage and association • Be able to perform analysis in heritability, linkage and association using selected software and critically evaluate and interpret the corresponding results Page 1 of 10 Additional Information 1.
    [Show full text]
  • Genetic Epidemiology of Health Disparities in Allergy and Clinical Immunology
    Molecular mechanisms in allergy and clinical immunology (Supported by an unrestricted educational grant from Genentech, Inc. and Novartis Pharmaceuticals Corporation) Series editors: William T. Shearer, MD, PhD, Lanny J. Rosenwasser, MD, and Bruce S. Bochner, MD Reviews and feature articles Genetic epidemiology of health disparities in allergy and clinical immunology Kathleen C. Barnes, PhD Baltimore, Md This activity is available for CME credit. See page 45A for important information. The striking racial and ethnic disparities in disease prevalence for common disorders, such as allergic asthma, cannot be Abbreviations used explained entirely by environmental, social, cultural, or CF: Cystic fibrosis economic factors, and genetic factors should not be ignored. CFTR: Cystic fibrosis transmembrane conductance Unfortunately, genetic studies in underserved minorities are regulator hampered by disagreements over the biologic construct of race CTLA4: Cytotoxic T lymphocyte–associated 4 and logistic issues, including admixture of different races and LD: Linkage disequilibrium ethnicities. Current observations suggest that the frequency NOS: Nitric oxide synthase of high-risk variants in candidate genes can differ between SNP: Single nucleotide polymorphism African Americans, Puerto Ricans, and Mexican Americans, and this might contribute to the differences in disease prevalence. Maintenance of certain allelic variants in the population over time might reflect selective pressures in influences (ie, gene-environment interactions). Con- previous generations.
    [Show full text]
  • Summary De-Identification Protocol V2 1. Introduction 1.1 This Note Sets
    Summary de-identification protocol V2 1. Introduction 1.1 This note sets out the UK Biobank policy for the de-identification of participant data (Participants and Participant Data) prior to its release to researchers. At the outset, two clear distinctions should be made: 1.1.1 This note does not specifically address the simple fact of an individual being identified as a Participant in UK Biobank, as many Participants choose (quite reasonably) to volunteer this information about themselves. However, UK Biobank does not itself release the identity or confirm the identity of any Participant. 1.1.2 This note does specifically address the release of Participant Data by UK Biobank to researchers where the manner in which the Participant Data is released could inadvertently identify a Participant (bearing in mind other information about the Participant which may already be in the public domain). 1.2 To illustrate: the fact that Mr Jones is a participant in UK Biobank is significant but not, of itself, an overtly significant item of information. Nevertheless, releasing information about the state of Mr Jones health to researchers in such a way that a) Mr Jones is (or can be) identified and then b) cross-referenced directly to the UK Biobank phenotypical or genotypical information which UK Biobank holds on him, would be highly problematic. The purpose of this note is to set out the steps that UK Biobank takes to, as far as UK Biobank possibly can, ensure that this does not happen. 1.3 This note also sets out certain details about the nature of the Participant Data which UK Biobank holds on Participants, UK Biobank's legal obligations, considerations around health-record linkage, practical considerations about identification and finally the actual de-identification protocols.
    [Show full text]
  • Bc Children's Hospital Biobank
    BC CHILDREN’S HOSPITAL BIOBANK Title Ethics Policy number POL 2 Effective Date 1 Dec 2014 Approved by Suzanne Vercauteren 1.0 BACKGROUND Biospecimens have been the basis of pathological inquiry for a very long time. However, with the advancement of molecular biology and genetic insights, scientists have greatly increased their use and demand for properly prepared and clinically annotated biospecimens that yield valuable insights into the mechanisms and pathways of human disease. Research on human biospecimens has not been always formally regulated or extensively harmonized by governing agencies. Existing guidelines for the protection of human subjects in clinical research continue to provide oversight for the use of biospecimens in basic and translational research in general. Biobanking of pediatric biospecimens and data for research purposes brings its own ethical dilemmas. Current ethical concerns in pediatric biobanking include consent of children participants and their parents; re-contacting children at age of consent; use of personal information by researchers and breach of security safeguards to name a few. Existing guidelines and best practices have been applied to dealing with issues related to collection, study, storage, transfer and disposal of biospecimens and associated patient/participant (pediatric and adult) data. As biospecimens are becoming a valuable and irreplaceable resource and society’s interest in the advancement of medical knowledge is increasing, this policy is intended to foster a consistent and coherent ethical framework that should govern biospecimen use in the BC Children’s Hospital BioBank (BCCHB). 2.0 PURPOSE The BC Children’s Hospital BioBank (BCCHB) is committed to high ethical standards and practices in the collection and storage of biospecimens for research purposes.
    [Show full text]
  • Establishment of a Biobank and Pharmacogenetics Database Of
    European Journal of Human Genetics (2008) 16, 780–785 & 2008 Nature Publishing Group All rights reserved 1018-4813/08 $30.00 www.nature.com/ejhg LETTER samples were collected from 50 to 100 adult volunteers from ethnic groups in Nigeria, Kenya, Tanzania, Zimbabwe Establishment of a and South Africa (Figure 1). Portions of each blood sample were used to prepare DNA, blot on filter paper or store at biobank and À801C. The biobank consists of 1488 DNA samples from nine ethnic groups (Yoruba, Hausa, Ibo, Luo, Kikuyu, pharmacogenetics Maasai, Shona, San and Venda). The utility of the biobank was illustrated by studying the frequency distribution of database of African some polymorphisms of known phenotype characteristics populations (www.imm.ki.se/CYPalleles, http://louisville.edu/medschool/ pharmacology/Human.NAT2.pdf) of six genes (Table 1) European Journal of Human Genetics (2008) 16, 780–783; important in drug metabolism (CYP2B6, 2C19, 2D6, GSTM, GSTT and NAT-2). Genotyping was carried out using doi:10.1038/ejhg.2008.49; published online 2 April 2008 established PCR–RFLP methods.4–10 A database cataloging the samples and the genotype results was designed using Microsoft Access and Visual Basics software packages Pharmacogenetics and genomics research has experienced (http://www.aibst.com/biobank.html) and access is currently great advances over the past decade as witnessed by the limited to authorized individuals involved in the research completion of the human genome in 2003 (www.genome. projects. gov/HGP). The field has been driven by the belief that The stratification of populations based on allele understanding the human genome, that of pathogens, and frequency data was evaluated using principal component interindividual genetic variability would result in radical analysis using the program SIMCA P þ (www.umetrics.
    [Show full text]