The prevailing model for ethical review of multicenter clinical trials is a distributed system of reviews conducted by the Institutional Review Board (IRB) of each institution engaged in the conduct of the trial. Conducting multiple reviews of a single protocol can delay the commencement of multicenter research and delay patient access to potentially beneficial treatments.

Advances in psychiatric genetics are likely to offer major diagnostic and therapeutic benefits, but also legal and social-related risks, to individuals who were diagnosed with, or have a proclivity for, psychiatric disorders. In response, courts and policy-makers will have to ensure that psychiatric genetic data are used to promote, and not to obstruct, equality, justice, and social inclusion.

A big data ecosystem is evolving in our society in which people may have, or feel they have, little control over the flow of their personal health information, and thus their privacy. Further, although there has been significant discussion related to big data and privacy at the highest levels of government, there is little consensus among scholars and stakeholders as to what privacy actually is, not to mention a lack of data from individuals as to personal conceptions of privacy.

Genomic literacy plays a critical role in informed decision-making for genomic testing, in the implementation of the test and the accurate interpretation of the results, and in our policy making process as a society. The National Human Genome Research Institute's 2011 vision for the future of genomic medicine specifically cites the need for both providers and consumers to achieve genomic literacy. Yet despite its importance, there is no effective tool for assessing genomic literacy.

This 3-year R01 based at the University of Minnesota and Vanderbilt University will convene a national Working Group of top legal and scientific experts to analyze current U.S. federal and state law, regulation, and guidance on translational genomics, and to generate consensus recommendations on what the law should be, to optimize successful translation of genomics into clinical use. The law underlying genomics is currently unclear, poorly understood, and contested.

Non-invasive prenatal testing (NIPT), a cell-free DNA screening test of fetal chromosomal abnormalities using maternal plasma, has been heralded as ?revolutionizing prenatal screening and diagnosis. Introduced commercially in late 2011, the rapid clinical adoption of NIPT highlights genomic medicine?s growth and enormous promise for patient care. The research landscape stemming from genomic and precision medicine endeavors, however, necessitates concomitant efforts to monitor population health impact and assure equity in access to these advances.

Individual institutions across the country have worked to support research in a wide variety of areas, including precision medicine research, by developing large biorepositories comprised of biospecimens and health data collected from local patients and controls. However, these local cohorts rarely provide the diversity and size needed to identify and study subsets of patients who share biological mechanisms for their disease, and are thus more likely to respond to the same targeted therapies.

This project will identify and address ethical and practical barriers to qualitative data sharing (QDS) in health sciences research. Qualitative research has unique value in understanding health behaviors and traits that are stigmatized and hidden such as risk factors for HIV or a genetic propensity to addiction. Accordingly, a lot of qualitative data are sensitive, and the data are provided within relationships of trust.

The emergence of genomic medicine has not been without unique concerns that continue to challenge clinicians working in this arena. Genomic test results have implications for individuals other than the person tested. Related to this, interpretation of genomic data often requires several related individuals; and even then what can be known about the meaning of an individual's specific variants is, and will continue to be, a function of big data analysis of the genomic data of many thousands (or even millions) of individuals.

The advent of clinical genome sequencing to identify patients at risk for serious diseases and to tailor treatments promises to greatly improve health outcomes and provide a foundation for the delivery of Precision Medicine. However, even as laboratory methods to perform sequencing become highly efficient, uncertainty around the optimal breadth and economic value of sequencing as well as ambiguity around which individuals should be tested presents a critical barrier to wider use.