Genomic Data Sharing: Case Studies, Challenges, and Opportunities for Precision Medicine provides a comprehensive overview of current and emerging issues in genomic data sharing. In this book, international leaders in genomic data examine these issues in-depth, offering practical case studies that highlight key successes, challenges and opportunities. Sections discuss the eMERGE Network, Undiagnosed Disease Network, Vanderbilt Biobank, Marshfield Clinic Biobank, Minnesota Authorization, Rochester Epidemiology Project, NIH sponsored biobanks, GINA, and Global Alliance for Genomics and Health (GA4GH). In addition to these perspectives from the frontlines, the book also provides succinct overviews of ethical, legal, social and IT challenges. Clinician investigators, clinicians affiliated with academic medical centers, policymakers and regulators will also gain insights that will allow them to navigate the increasingly complex ethical, social and clinical landscape of genomic data sharing. Covers both technical and ELSI (ethical, legal, and social implications) perspectives on genomic data sharing Includes applied case studies of existing genomic data sharing consortia, including the eMERGE Network, Undiagnosed Disease Network, and the Global Alliance for Genomics and Health (GA4GH), among others Features chapter contributions from international leaders in genomic data sharing

Responsible Genomic Data Sharing: Challenges and Approaches brings together international experts in genomics research, bioinformatics and digital security who analyze common challenges in genomic data sharing, privacy preserving technologies, and best practices for large-scale genomic data sharing. Practical case studies, including the Global Alliance for Genomics and Health, the Beacon Network, and the Matchmaker Exchange, are discussed in-depth, illuminating pathways forward for new genomic data sharing efforts across research and clinical practice, industry and academia. Addresses privacy preserving technologies and how they can be applied to enable responsible genomic data sharing Employs illustrative case studies and analyzes emerging genomic data sharing efforts, common challenges and lessons learned Features chapter contributions from international experts in responsible approaches to genomic data sharing

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.

In the early 1970s, the federal government began recognizing self-determination for American Indian nations. As sovereign entities, Indian nations have been able to establish policies concerning health care, education, religious freedom, law enforcement, gaming, and taxation. David E. Wilkins and K. Tsianina Lomawaima discuss how the political rights and sovereign status of Indian nations have variously been respected, ignored, terminated, and unilaterally modified by federal lawmakers as a result of the ambivalent political and legal status of tribes under western law.

In 2000, President Bill Clinton signaled the completion of the Human Genome Project at a cost in excess of $2 billion. A decade later, the price for any of us to order our own personal genome sequence--a comprehensive map of the 3 billion letters in our DNA--is rapidly and inevitably dropping to just $1,000. Dozens of men and women--scientists, entrepreneurs, celebrities, and patients--have already been sequenced, pioneers in a bold new era of personalized genomic medicine. The $1,000 genome has long been considered the tipping point that would open the floodgates to this revolution. Do you have gene variants associated with Alzheimer's or diabetes, heart disease or cancer? Which drugs should you consider taking for various diseases, and at what dosage? In the years to come, doctors will likely be able to tackle all of these questions--and many more--by using a computer in their offices to call up your unique genome sequence, which will become as much a part of your medical record as your blood pressure.

The long-awaited story of the science, the business, the politics, the intrigue behind the scenes of the most ferocious competition in the history of modern science—the race to map the human genome. On May 10, 1998, biologist Craig Venter, director of the Institute for Genomic Research, announced that he was forming a private company that within three years would unravel the complete genetic code of human life—seven years before the projected finish of the U.S. government’s Human Genome Project. Venter hoped that by decoding the genome ahead of schedule, he would speed up the pace of biomedical research and save the lives of thousands of people. He also hoped to become very famous and very rich. Calling his company Celera (from the Latin for “speed”), he assembled a small group of scientists in an empty building in Rockville, Maryland, and set to work. At the same time, the leaders of the government program, under the direction of Francis Collins, head of the National Human Genome Research Institute at the National Institutes of Health, began to mobilize an unexpectedly unified effort to beat Venter to the prize—knowledge that had the potential to revolutionize medicine and society. The stage was set for one of the most thrilling—and important—dramas in the history of science. The Genome War is the definitive account of that drama—the race for the greatest prize biology has had to offer, told by a writer with exclusive access to Venter’s operation from start to finish. It is also the story of how one man’s ambition created a scientific Camelot where, for a moment, it seemed that the competing interests of pure science and commercial profit might be gloriously reconciled—and the national repercussions that resulted when that dream went awry.

Updated as of August 2014, this practical book will demonstrate proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets. Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors’ experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others. Understand different methods for working with cross-sectional and longitudinal datasets Assess the risk of adversaries who attempt to re-identify patients in anonymized datasets Reduce the size and complexity of massive datasets without losing key information or jeopardizing privacy Use methods to anonymize unstructured free-form text data Minimize the risks inherent in geospatial data, without omitting critical location-based health information Look at ways to anonymize coding information in health data Learn the challenge of anonymously linking related datasets