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The current emphasis on broad sharing of human genomic data generated in research in order to maximize utility and public benefit is a significant legacy of the Human Genome Project. Concerns about privacy and discrimination have led to policy responses that restrict access to genomic data as the means for protecting research participants. Our research and experience show, however, that a considerable number of research participants agree to open access sharing of their genomic data when given the choice. General policies that limit access to all genomic data fail to respect the autonomy of these participants and, at the same time, unnecessarily limit the utility of the data. We advocate instead a more balanced approach that allows for individual choice and encourages informed decision making, while protecting against the misuse of genomic data through enhanced legislation.

Abstract
Population genomics can reveal cryptic biological diversity that may impact fitness while simultaneously serving to delineate relevant conservation units. Here, we leverage the power of whole-genome resequencing for conservation by studying 433 individual lesser prairie-chicken (Tympanuchus pallidicinctus; LEPC, a federally endangered species of conservation concern in the United States) and greater prairie-chicken (Tympanuchus cupido; GRPC, a legally huntable species throughout much of its range). The genomic diversity of two formally recognized distinct population segments (DPSs) of LEPCs is similar, but they are genetically distinct. Neither DPS is depleted of its genomic diversity, neither is especially inbred, and temporal diversity is relatively stable in both conservation units. Interspecific differentiation between the two species was only slightly higher than that observed between LEPC DPSs, due largely to bidirectional introgression. The high resolution provided by our dataset identified a genomic continuum between the two species such that individuals sampled from the hybrid zone were imperfectly assigned to their presumptive species when considering only their physical characteristics. The admixture between the two species is reflected in the spectrum of individual ancestry coefficients, which has legal implications for the "take" of individuals under the Endangered Species Act. Overall, our data highlight the recurring dissonance between static policies and dynamic species boundaries that are increasingly obvious in the population genomic era.

Critical illness clinical trials that entail genomic data collection pose unique challenges. In this qualitative study, we found that surrogate decision makers (SDMs) for critically ill individuals, such as those who would be approached for study participation, appeared to have a limited grasp of genomic principles. We argue that low levels of genomic literacy should neither preclude nor be in conflict with the conduct of ethically rigorous clinical trials.

Following the development of highly effective direct acting antiviral (DAA) compounds for the treatment of the hepatitis C virus (HCV), WHO has set out plans for disease eradication by 2030. Many barriers must be surmounted before this can be achieved, including buy-in from governments and policy makers, reduced drug costs, and improved infrastructure for the pathway from diagnosis to treatment. A comprehensive set of guidelines was produced by WHO in 2014, updated in 2016, and they are due to be revised later this year.

An article from the Information as Medicine symposium.

The whole-genome response of Arabidopsis (Arabidopsis thaliana) exposed to different types and durations of abiotic stress has now been described by a wealth of publicly available microarray data. When combined with studies of how gene expression is affected in mutant and transgenic Arabidopsis with altered ability to transduce the low temperature signal, these data can be used to test the interactions between various low temperature-associated transcription factors and their regulons. We quantized a collection of Affymetrix microarray data so that each gene in a particular regulon could vote on whether a cis-element found in its promoter conferred induction (+1), repression (−1), or no transcriptional change (0) during cold stress. By statistically comparing these election results with the voting behavior of all genes on the same gene chip, we verified the bioactivity of novel cis-elements and defined whether they were inductive or repressive. Using in silico mutagenesis we identified functional binding consensus variants for the transcription factors studied. Our results suggest that the previously identified ICEr1 (induction of CBF expression region 1) consensus does not correlate with cold gene induction, while the ICEr3/ICEr4 consensuses identified using our algorithms are present in regulons of genes that were induced coordinate with observed ICE1 transcript accumulation and temporally preceding genes containing the dehydration response element. Statistical analysis of overlap and cis-element enrichment in the ICE1, CBF2, ZAT12, HOS9, and PHYA regulons enabled us to construct a regulatory network supported by multiple lines of evidence that can be used for future hypothesis testing.

Data sharing models designed to facilitate global business provide insights for improving transborder genomic data sharing. We argue that a flexible, externally endorsed, multilateral arrangement, combined with an objective third-party assurance mechanism, can effectively balance privacy with the need to share genomic data globally.

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely.
ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data.
ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals.
ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

Funder: State Government of Victoria (Victorian Government); doi: https://doi.org/10.13039/501100004752 ; Funder: Victorian State Government ; Abstract: Public acceptance is critical for sharing of genomic data at scale. This paper examines how acceptance of data sharing pertains to the perceived similarities and differences between DNA and other forms of personal data. It explores the perceptions of representative publics from the USA, Canada, the UK and Australia (n = 8967) towards the donation of DNA and health data. Fifty-two percent of this public held 'exceptionalist' views about genetics (i.e., believed DNA is different or 'special' compared to other types of medical information). This group was more likely to be familiar with or have had personal experience with genomics and to perceive DNA information as having personal as well as clinical and scientific value. Those with personal experience with genetics and genetic exceptionalist views were nearly six times more likely to be willing to donate their anonymous DNA and medical information for research than other respondents. Perceived harms from re-identification did not appear to dissuade publics from being willing to participate in research. The interplay between exceptionalist views about genetics and the personal, scientific and clinical value attributed to data would be a valuable focus for future research.

ABSTRACT
ObjectivesTo build a searchable database for SNP array data from the GoDARTS data set, in which a combined view of genotype data derived from multiple assay platforms can be extracted for both candidate gene and GWA studies and to combine this with a database of phenotype descriptors which are saved as shareable, reusable database objects and which persist beyond the lifetime of any analysis script.
To build databases and software solutions which can be made readily available to laboratories and academic institutions which may not have the resources to adopt one of the larger Genotype / Phenotype integration solutions.
ApproachTwo databases were built. The first is a hybrid Genomics one in which variant and study subject data are stored in a database with variant detail data retained in Variant Call Format (VCF) files. The second database saves phenotype descriptors as shareable, modifiable database objects alongside a table of events derived from the set of available Electronic Health Records (EHRs). All detail from the EHRs is also retained in the database which is delivered on a project by project basis using virtual machines.
Both databases are accessed using web applications, allowing delivery of data to the users' desktops.
ResultsTraditionally the process of deriving genotype and phenotype data for epidemiological studies can be a laborious one with genotype data being retrieved from large, flat data files and phenotypes being defined by codes in flat EHR records which are tested and filtered in scripts, written for analysis in a statistical package such as Stata, SPSS or R.
In our solution, genotype data can be retrieved in seconds and delivered to the users' desktops. Similarly lists of cases and controls can be downloaded based on saved or transient phenotype descriptors. Phenotypes descriptors derived from codes in Electronic Health Records are saved as reusable, shareable and modifiable database objects objects, allowing rapid retrieval of phenotype data.
ConclusionThe ability to access Genomic data from multiple assay platforms and to use this in conjunction with shareable libraries of phenotype objects allows rapid access to data for analysis using both Genomic SNP Array data and linked Electronic Health Records. Analysis on data extracted from our linked databases should proceed more rapidly and should be more easily reproducible.

Recent advances in molecular genetics have provided social scientists with new tools with which to explore human behavior. By deploying genomic analysis, we can now explore long-term patterns of human migration and mating, explore the biological aspects of important sociological outcomes such as educational attainment, and, most importantly, model gene-by-environment interaction effects. The intuition motivating much socio-genomic research is that to have a more complete understanding of social life, scholars must take into consideration both nature and nurture as well as their interplay. Most promising is gene-by-environment research that deploys polygenic measures of genotype as a prism through which to refract and detect heterogenous treatment effects of plausibly exogenous environmental influences. This article reviews much recent work in this vein and argues for a broader integration of genomic data into social inquiry.

In the UK, genomic health data is being generated in three major contexts: the healthcare system (based on clinical indication), in large scale research programmes, and for purchasers of direct-to-consumer genetic tests. The recently delivered hybrid clinical/research programme, 100,000 Genomes Project set the scene for a new Genomic Medicine Service, through which the National Health Service aims to deliver consistent and equitable care informed by genomics, while providing data to inform academic and industry research and development. In parallel, a large scale research study, Our Future Health, has UK Government and Industry investment and aims to recruit 5 million volunteers to support research intended to improve early detection, risk stratification, and early intervention for chronic diseases. To explore how current models of genomic health data generation intersect, and to understand clinical, ethical, legal, policy and social issues arising from this intersection, we conducted a series of five multidisciplinary panel discussions attended by 28 invited stakeholders. Meetings were recorded and transcribed. We present a summary of issues identified: genomic test attributes; reasons for generating genomic health data; individuals' motivation to seek genomic data; health service impacts; role of genetic counseling; equity; data uses and security; consent; governance and regulation. We conclude with some suggestions for policy consideration.