To take full advantage of the large amount of genetic data collected, it is imperative that data are shared among researchers. Not only is the sharing of genetic data essential for forming larger datasets for analysis, but it also makes resource allocation more efficient by reducing the number of duplicate experiments, and supports reproducibility and scientific discovery. NIH's action of limiting access to aggregated human genomic data has spurred interest in the development of methods for confidentiality and privacy protection of GWAS databases. The most significant methods to date have risen from interdisciplinary research that combines statistical notions of utility with algorithmic thinking and risk measures from computer sciences. Whether this is going to be the most useful framework remains to be seen. But the problem of more broadly sharing useful human-genomic data for research purposes and clinical discovery while maintaining individuals' privacy is not going away any time soon.
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