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In this Commentary, we argue that in line with the dramatic increase in the collection, storage, and curation of human genomic data for biomedical research, genomic data repositories and consortia have adopted governance frameworks to address the dual objectives of enabling wide access while protecting against possible harms. However, there are ongoing debates in the scientific community about the merits and limitations of different governance frameworks in achieving these twin aims; and indeed, best practices and points for consideration are notably absent when it comes to devising a governance framework for genomic databases. Based on our collective experience of devising and assessing governance frameworks, our Commentary identifies five key functions of “good governance” (or what makes “better governance”) and three areas where trade-offs should be considered when specifying policies within those functions. We apply these functions as a benchmark to describe, as an example, the governance frameworks of six large-scale international genomic projects.

Precision or personalized medicine through clinical genome and exome sequencing has been described by some as a revolution that could transform healthcare delivery, yet it is currently used in only a small fraction of patients, principally for the diagnosis of suspected Mendelian conditions and for targeting cancer treatments. Given the burden of illness in our society, it is of interest to ask how clinical genome and exome sequencing can be constructively integrated more broadly into the routine practice of medicine for the betterment of public health. In November 2014, 46 experts from academia, industry, policy and patient advocacy gathered in a conference sponsored by Illumina, Inc. to discuss this question, share viewpoints and propose recommendations. This perspective summarizes that work and identifies some of the obstacles and opportunities that must be considered in translating advances in genomics more widely into the practice of medicine.

This article discusses the Genetic Information Nondiscrimination Act (GINA) which President George W. Bush officially signed in 2008. The law prohibits employers from making adverse employment decisions based on a person's genetic information, including family health history. It also forbids insurance companies from discriminating against individuals by reducing their coverage or increasing premiums. In addition, employers and health insurers are not allowed to request or demand a genetic test under the law. Because of this legislation, Americans will be free to undergo genetic testing for diseases such as cancer, heart disease, diabetes, and Alzheimer's without fearing for their job or health insurance. It is important that Americans understand what protections the law offers as well as its limitations. GINA is not applicable to members of the military, federal employees enrolled in the Federal Employees Health Benefits program (FEHB), veterans obtaining healthcare through the Veteran's Administration and individuals covered under the Indian Health Service, although many of these individuals have similar protections under other laws and policies. In addition, employers with fewer than 15 employees are exempt. Finally, although GINA applies to health insurance, its protections do not currently extend to life, long-term care, or disability insurance.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

Rare diseases (RD) refer to a collection of approximately 5,000-8,000 individual diseases that have a low prevalence and are often genetic in origin. While RD can manifest throughout life, they frequently affect children and newborns. Common characteristics include being severe, disabling, life-threatening, degenerative and affecting different organ systems. The burden of RD is often exacerbated by a lack of specific treatments. Whilst there is etiological heterogeneity, there is overlap in cellular and molecular pathways. Amongst specialists, there is legitimate hope that based on genetic knowledge and pathway definition, a new medical classification system, currently called "precision medicine", will be developed, which may change our view on how to apply shared therapeutic targets. Thus, collection of clinical and genetic data and biospecimens (in biobanks) will play an increasing role in diagnoses and development of therapies for RD. Biobanks are maintained collaboratively by researchers or their institutions, and involve a delicate balance between health policy objectives, academic research, public good outcomes, and community trust. Due to the nature of RD, international cooperation is critical for sharing limited numbers of RD samples and achieving a critical mass. Here we review the current and future direction of RD biobanks and discuss research and development stemming from the use of biospecimens to improve management of RD. [ABSTRACT FROM AUTHOR]

The aim of this article is to describe the methods and effectiveness of the Public Engagement in Genetic Variation and Haplotype Mapping Issues (PEGV) Project, which engaged a community in policy discussion about genetic variation research. The project implemented a 6-stage community engagement model in New Rochelle, New York. First, researchers recruited community partners. Second, the project team created community oversight. Third, focus groups discussed concerns generated by genetic variation research. Fourth, community dialogue sessions addressed focus group findings and developed policy recommendations. Fifth, a conference was held to present these policy recommendations and to provide a forum for HapMap (haplotype mapping) researchers to dialogue directly with residents. Finally, findings were disseminated via presentations and papers to the participants and to the wider community beyond. The project generated a list of proposed guidelines for genetic variation research that addressed the concerns of New Rochelle residents. Project team members expressed satisfaction with the engagement model overall but expressed concerns about how well community groups were utilized and what segment of the community actually engaged in the project. The PEGV Project represents a model for researchers to engage the general public in policy development about genetic research. There are benefits of such a process beyond the desired genetic research. ( Population Health Management 2012;15:78-89) [ABSTRACT FROM AUTHOR]

Background: Pseudoxanthoma elasticum (PXE), an autosomal recessive disorder with considerable phenotypic variability, mainly affects the eyes, skin and cardiovascular system, characterised by dystrophic mineralization of connective tissues. It is caused by mutations in the ABCC6 (ATP binding cassette family C member 6) gene, which encodes MRP6 (multidrug resistance-associated protein 6). Objective: To investigate the mutation spectrum of ABCC6 and possible genotype-phenotype correlations. Methods: Mutation data were collected on an international case series of 270 patients with PXE (239 probands, 31 affected family members). A denaturing high-performance liquid chromatography-based assay was developed to screen for mutations in all 31 exons, eliminating pseudogene coamplification. In 134 patients with a known phenotype and both mutations identified, genotype-phenotype correlations were assessed. Results: In total, 316 mutant alleles in ABCC6, including 39 novel mutations, were identified in 239 probands. Mutations were found to cluster in exons 24 and 28, corresponding to the second nucleotide- binding fold and the last intracellular domain of the protein. Together with the recurrent R1141 X and del23- 29 mutations, these mutations accounted for 71 .5% of the total individual mutations identified. Genotype- phenotype analysis failed to reveal a significant correlation between the types of mutations identified or their predicted effect on the expression of the protein and the age of onset and severity of the disease. Conclusions: This study emphasises the principal role of ABCC6 mutations in the pathogenesis of PXE, but the reasons for phenotypic variability remain to be explored. [ABSTRACT FROM AUTHOR]

Sex and gender are two intertwined concepts pervasive in ongoing conversations around science and society. Although sex verification and testing are definitive detriments on the health of gender nonconforming and transgender individuals, this does not mean science should be halted within the community. [Extracted from the article]

The author discusses the role of the U.S. Genetic Information Nondiscrimination Act (GINA) and the U.S. Americans with Disabilities Act (ADA) on the protection of individuals against inquiries into their genetic disposition for diseases and other disabilities. It mentions the decision of the Equal Employment Opportunity Commission (EEOC) to amend the regulations in the GINA related on emoployers incentives for the participation of employees into the employer-sponsored wellness programs.