In 2013, Melissa Gymrek, Amy McGuire, David Golan, Eran Halperin, and Yaniv Erlich published an article describing how they re-identified almost 50 individuals from “anonymized” data in a genomic database from the 1000 Genomes Project. Their goal was to show the vulnerability of genomic databases to this sort of manipulation. The case opens discussion of sharing genomic data and protecting privacy.
In a study published in Science in 2013, researchers outlined how they were able to re-identify almost 50 individuals from “anonymized” data in a genomic database from the 1000 Genomes Project (Gymrek et al. 2013). Their intention was to “demonstrate end-to-end identification of individuals with only public information,” using simple computational search tools and an internet connection (Gymrek et al. 2013, 321).
In general, researchers can re-identify specific individuals or small groups by using “quasi-identifiers” to cross-reference certain data included in the genetic databases that are also available in other databases (Kupersmith 2013). These “quasi-identifiers” can come from a variety of public and non-public databases, such as hospital data, ICD-9 codes, social security database, vehicular databases, voter registration lists, house sales, and other public records’ search engines (Kupersmith 2013). To re-identity anonymized data, then, researchers can use computational approaches to match data from a candidate anonymized database with the data from one or more reference databases, using their shared elements such as zip codes.
Gymrek et al. used data from individuals who had been sequenced for the Center for Study of Human Polymorphisms (CEPH) family collection, and were stored in the 1000 Genome Project. The participants of the research were informed that the database provided broad and open access to the data for genomic analyses, and that there was a slight risk that re-identification was possible. Privacy was not promised to the participants. Still, it was assumed that the risk of re-identification was low (Rodriguez et al. 2013, 275).
In their study, the researchers leveraged information about patrilineal relations from databases to re-identify individuals by surnames. They used sequence data to identify single nucleotide polymorphisms (SNPs) on the Y chromosome in the genomes of individuals (Gymrek et al 2013, 323; Kupersmith 2013). These SNPs, referred to as Y-STR (short tandem repeats) markers, are used to identify patrilineal lineages. They then used this information to search databases which included the surnames of 40,000 individuals and their pedigrees. Next, they matched that information with other public sources of information from the National Institute of
General Medical Sciences (NIGMS) Human Genetic Cell Repository at the Coriell Institute. That database included information about obituaries, as well as information from the biological materials gathered for the CEPH. As a result of this search procedure, which took only a few hours to complete, the researchers were able to identify almost 50 individuals, although they did not disclose any individual names in the publication of their research.
Before the publication of the study, the researchers contacted the National Institutes of Health (NIH), whose staff members then consulted with the editors of Science and the staff working for the CEPH study, to discuss what to do about the privacy breach they demonstrated in their study (Rodriguez et al. 2013, 275-276). Changes were made to the publicly-accessible repository, including the removal of any information indicating the age of the participants. But, none of the methods that the researchers used violated the existing laws or regulations designed to protect individuals’ genetic privacy and prevent genetic discrimination.
Genomic and genetic data about individuals or groups are particularly sensitive because they can have stigmatizing consequences, such as “employment discrimination, denial of life insurance, and inappropriate marketing” (Kupersmith 2013). Consequently, this study triggered many questions about how best to ensure the privacy of research participants and promises of confidentiality and, more importantly, how to balance the competing goals of scientific research in genomics with respect for individual autonomy.
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IGSR: The International Genome Sample Resource: http://www.internationalgenome.org
 ICD-9 codes stand for the “International Classification of Diseases, Ninth Revision” of the World Health Organization.
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