Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations

By Sean K. Simmons, Cenk Sahinalp, Bonnie Berger. 2016

60 citations recorded by the source corpus.

Summary The proliferation of large genomic databases offers the potential to perform increasingly larger-scale genome-wide association studies (GWAS). Due to privacy concerns, however, access to these data is limited, greatly reducing their usefulness for research. Here, we introduce a computational framework for performing GWAS that adapts principles of differential privacy—a cryptographic theory that facilitates secure analysis of sensitive data—to, for the first time, both protect private phenotype information (e.g., disease status) and correct for population stratification. This framework enables us to produce privacy-preserving GWAS results based on EIGENSTRAT and linear mixed model (LMM)-based statistics, both of which correct for population stratification. We test our differentially private statistics, PrivSTRAT and PrivLMM, on simulated and real GWAS datasets and find they are able to protect privacy while returning meaningful results. Our framework can be used to securely query private genomic datasets to discover which specific genomic alterations may be associated with a disease, thus increasing the availability of these valuable datasets.

Subfields: Artificial Intelligence, Computer Science Applications, Computer Vision and Pattern Recognition

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. Ziteng Sun
2. Jayadev Acharya
3. Huanyu Zhang
4. Gautam Kamath
5. Constantinos Daskalakis
6. Bryan Cai
7. Ayfer Özgür
8. Peter Kairouz

In Computer Science Applications

1. Ziteng Sun
2. Jayadev Acharya
3. Gautam Kamath
4. Huanyu Zhang
5. Clément Louis Canonne
6. Ayfer Özgür

In Computer Vision and Pattern Recognition

1. Gautam Kamath
2. Jayadev Acharya
3. Constantinos Daskalakis

Citations in the corpus

• Priv'IT: Private and Sample Efficient Identity Testing (2017, background): “Motivated in part by this realization, there has been increased recent interest in developing data sharing techniques which are private (Johnson & Shmatikov, 2013; Uhler et al., 2013; Yu et al., 2014; Simmons et al., 2016).”
• INSPECTRE: Privately Estimating the Unseen (2018, methodology): “This realization has motivated a surge of interest in developing data sharing techniques with an explicit focus on maintaining privacy of the data (Johnson & Shmatikov, 2013; Uhler et al., 2013; Yu et al., 2014; Simmons et al., 2016).”