With the increasing abundance of genetic data, the usefulness of a genetic dataset now depends in part on the possibility of productively linking it with other datasets. One issue that arises in combining multiple datasets is the record-matching problem, i.e., the identification of dataset entries that, although labeled differently in separate datasets, represent the same underlying entity. In a genetic context, record-matching involves the identification of the same individual genome across multiple datasets when unique identifiers, such as participant names, are unavailable. By using correlations among genetic markers close to one another in the genome, the method proposed in this report can succeed even when the datasets contain no overlapping marker. The authors show that the method can link a dataset similar to those used in genomic studies with another dataset containing markers used for forensics. The proposed approach can assist in maintaining backward compatibility with databases of existing forensic genetic profiles as systems move to new marker types. This approach illustrates that the privacy risks that can arise from the cross-linking of databases are inherent even for a small number of markers. 4 figures, 1 tables, and 39 references
Linkage Disequilibrium Matches Forensic Genetic Records to Disjoint Genomic Marker Sets
NCJ Number
250767
Journal
PNAS Volume: 114 Issue: 22 Dated: May 2017 Pages: 5671-5676
Date Published
April 2017
Length
6 pages
Annotation
This report describes a method for identifying in distinct genetic datasets observations that represent the same person.
Abstract
Date Published: April 1, 2017