Genomics and Privacy: Implications of the New Reality of Closed Data for the Field

• Published in: PLoS computational biology Vol. 7; no. 12; p. e1002278
• Main Authors: Greenbaum, Dov, Sboner, Andrea, Mu, Xinmeng Jasmine, Gerstein, Mark
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.12.2011; Public Library of Science (PLoS)
• Subjects: Bioinformatics; Biology; Cloud computing; Computational biology; Computer Science; Computer Security; Data mining; DNA sequencing; Gene expression; Genetic Privacy; Genomes; Genomics; Humans; Medical research; Nucleotide sequencing; Physiological aspects; Privacy; R&D; Research & development; Review; Science Policy; Software; United States; Genetic Privacy; Computer Security; Genomics; Humans
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1002278

Open source and open data have been driving forces in bioinformatics in the past. However, privacy concerns may soon change the landscape, limiting future access to important data sets, including personal genomics data. Here we survey this situation in some detail, describing, in particular, how the large scale of the data from personal genomic sequencing makes it especially hard to share data, exacerbating the privacy problem. We also go over various aspects of genomic privacy: first, there is basic identifiability of subjects having their genome sequenced. However, even for individuals who have consented to be identified, there is the prospect of very detailed future characterization of their genotype, which, unanticipated at the time of their consent, may be more personal and invasive than the release of their medical records. We go over various computational strategies for dealing with the issue of genomic privacy. One can "slice" and reformat datasets to allow them to be partially shared while securing the most private variants. This is particularly applicable to functional genomics information, which can be largely processed without variant information. For handling the most private data there are a number of legal and technological approaches-for example, modifying the informed consent procedure to acknowledge that privacy cannot be guaranteed, and/or employing a secure cloud computing environment. Cloud computing in particular may allow access to the data in a more controlled fashion than the current practice of downloading and computing on large datasets. Furthermore, it may be particularly advantageous for small labs, given that the burden of many privacy issues falls disproportionately on them in comparison to large corporations and genome centers. Finally, we discuss how education of future genetics researchers will be important, with curriculums emphasizing privacy and data security. However, teaching personal genomics with identifiable subjects in the university setting will, in turn, create additional privacy issues and social conundrums.