Share and share alike: deciding how to distribute the scientific and social benefits of genomic data.

Department of Anthropology, University of Oklahoma, Norman, Oklahoma 73019, USA.
Nature Reviews Genetics (Impact Factor: 39.79). 09/2007; 8(8):633-9. DOI: 10.1038/nrg2124
Source: PubMed

ABSTRACT Emerging technologies make genomic analyses more efficient and less expensive, enabling genome-wide association and gene-environment interaction studies. In anticipation of their results, funding agencies such as the US National Institutes of Health and the Wellcome Trust are formulating guidelines for sharing the large amounts of genomic data that are generated by the projects that they sponsor. Data-sharing policies can have varying implications for how disease susceptibility and drug-response research will be pursued by the scientific community, and for who will benefit from the resulting medical discoveries. We suggest that the complex interplay of stakeholders and their interests, rather than single-issue and single-stakeholder perspectives, should be considered when deciding genomic data-sharing policies.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Aim: Introducing data sharing practices into the genomic research arena has challenged the current mechanisms established to protect rights of individuals and triggered policy considerations. To inform such policy deliberations, soliciting public and research participants' attitudes with respect to genomic data sharing is a necessity. Method: The main electronic databases were searched in order to retrieve empirical studies, investigating the attitudes of research participants and the public towards genomic data sharing through public databases. Results: In the 15 included studies, participants' attitudes towards genomic data sharing revealed the influence of a constellation of interrelated factors, including the personal perceptions of controllability and sensitivity of data, potential risks and benefits of data sharing at individual and social level and also governance level considerations. Conclusion: This analysis indicates that future policy responses and recruitment practices should be attentive to a wide variety of concerns in order to promote both responsible and progressive research.
    Expert Review of Molecular Diagnostics 09/2014; 14(8). DOI:10.1586/14737159.2014.961917 · 4.27 Impact Factor
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Very large sample sizes are required for estimating effects which are known to be small, and for addressing intricate or complex statistical questions. This is often only achievable by pooling data from multiple stu-dies, especially in genetic epidemiology where associations between individual genetic variants and phenotypes of interest are generally weak. However, the physical pooling of experimental data across a consortium is frequently prohibited by the ethico-legal constraints that govern agreements and consents for individual studies. Study level meta-analyses are frequently used so that data from multiple studies need not be pooled to conduct an analysis, though the resulting analysis is necessarily restricted by the available summary statis-tics. The idea of maintaining data security is also of importance in other areas and approaches to carrying out 'secure analyses' that do not require sharing of data from different sources have been proposed in the technometrics literature. Crucially, the algorithms for fitting certain statistical models can be manipulated so that an individual level meta-analysis can essentially be performed without the need for pooling individual-level data by combining particular summary statistics obtained individually from each study. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual levEL Databases) is a tool to coordinate analyses of data that cannot be pooled. In this paper, we focus on explaining why a DataSHIELD approach yields identical results to an indivi-dual level meta-analysis in the case of a generalised linear model, by simply using summary statistics from each study. It is also an efficient approach to carrying out a study level meta-analysis when this is appropri-ate and when the analysis can be pre-planned. We briefly comment on the IT requirements, together with the ethical and legal challenges which must be addressed.


Available from