Realizing privacy preserving genome-wide association studies

By Sean K. Simmons, Bonnie Berger. 2016

64 citations recorded by the source corpus.

Motivation: As genomics moves into the clinic, there has been much interest in using this medical data for research. At the same time the use of such data raises many privacy concerns. These circumstances have led to the development of various methods to perform genome-wide association studies (GWAS) on patient records while ensuring privacy. In particular, there has been growing interest in applying differentially private techniques to this challenge. Unfortunately, up until now all methods for finding high scoring SNPs in a differentially private manner have had major drawbacks in terms of either accuracy or computational efficiency. Results: Here we overcome these limitations with a substantially modified version of the neighbor distance method for performing differentially private GWAS, and thus are able to produce a more viable mechanism. Specifically, we use input perturbation and an adaptive boundary method to overcome accuracy issues. We also design and implement a convex analysis based algorithm to calculate the neighbor distance for each SNP in constant time, overcoming the major computational bottleneck in the neighbor distance method. It is our hope that methods such as ours will pave the way for more widespread use of patient data in biomedical research. Availability and implementation: A python implementation is available at http://groups.csail.mit.edu/cb/DiffPriv/. Contact: bab@csail.mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Artificial Intelligence, Genetics, Statistics and Probability, Privacy & Security, Computational Biology & Bioinformatics

How researchers see this paper

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

In Artificial Intelligence

1. Or Sheffet
2. Aleksandra Korolova
3. Uri Stemmer
4. Jonathan Ullman
5. Zhiwei Steven Wu
6. Kobbi Nissim
7. Amos Beimel
8. Vitaly Feldman

In Genetics

1. Brett K. Beaulieu-Jones
2. Casey S. Greene
3. Chloé-Agathe Azencott
4. Sameer Quazi

In Statistics and Probability

1. Aleksandra Korolova
2. Uri Stemmer
3. Or Sheffet
4. Zhiwei Steven Wu
5. Amos Beimel
6. Brett K. Beaulieu-Jones
7. Kobbi Nissim
8. Casey S. Greene

In Privacy & Security

1. Uri Stemmer
2. Or Sheffet
3. Aleksandra Korolova
4. Kobbi Nissim
5. Zhiwei Steven Wu
6. Amos Beimel
7. Jonathan Ullman
8. Vitaly Feldman

In Computational Biology & Bioinformatics

1. Brett K. Beaulieu-Jones
2. Amos Beimel
3. Casey S. Greene
4. Chloé-Agathe Azencott

Citations in the corpus

• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (2017, methodology): “Within the biomedical domain, Simmons and Berger developed a method using differential privacy to enable privacy preserving genome-wide association studies (24).”
• The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “Solving the problem while ensuring privacy is particularly challenging when the feature dimension is large compared to the number of available samples, which is the case for GWAS [13, 3, 39, 57].”
• Machine learning and genomics: precision medicine versus patient privacy (2018, background): “Nevertheless, recent work [40, 41, 42] has shown how to implement differential privacy for GWAS, making it possible for users to query a genetic database for the differentially private top k most associated SNPs.”
• Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, unknown): “We compare the distance-basedmethod with other methods in Supplemental Experimental Procedures, showing that it performs the best in practice, a result that is consistent with previous work (Simmons and Berger, 2016).”