Enabling Privacy Preserving GWAS in Heterogeneous Human Populations

By Joshua D. Welch, Ziqing Liu, Li Wang, Junjie Lu, Paul Lerou, Jeremy E. Purvis, Li Qian, Alexander J. Hartemink, Jan F. Prins. 2016

107 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Travers Ching
2. Alexandr A Kalinin
3. Brett K. Beaulieu-Jones
4. Daniel S. Himmelstein
5. Amr M. Alexandari
6. G. Way
7. Casey S. Greene
8. Brian T Do

Citations in the corpus

• Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “Other tools implement algorithms targeting specific applications like: location data [35], genomic data [27, 47], mobility data [37], and browser error reports [26].”
• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[118] present the ability to perform genome-wide association studies (GWASs) in a differentially private manner, and Abadi et al.”