GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914179543203840 |
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| author | Nandi, Anupama Neel, Seth Cho, Hyunghoon |
| author_facet | Nandi, Anupama Neel, Seth Cho, Hyunghoon |
| contents | Genome-wide association studies (GWAS) are an essential tool in biomedical research for identifying genetic factors linked to health and disease. However, publicly releasing GWAS summary statistics poses well-recognized privacy risks, including the potential to infer an individual's participation in the study or to reveal sensitive phenotypic information (e.g., disease status). While differential privacy (DP) offers a rigorous mathematical framework for mitigating these risks, existing DP techniques for GWAS either introduce excessive noise or restrict the release to a limited set of results. In this work, we present practical DP mechanisms for releasing the complete set of genome-wide association statistics with privacy guarantees. We demonstrate the accuracy of the privacy-preserving statistics released by our mechanisms on a range of GWAS datasets from the UK Biobank, utilizing both real and simulated phenotypes. We introduce two key techniques to overcome the limitations of prior approaches: (1) an optimization-based randomization mechanism that directly minimizes the expected error in GWAS results to enhance utility, and (2) the use of personalized priors, derived from predictive models privately trained on a subset of the dataset, to enable sample-specific optimization which further reduces the amount of noise introduced by DP. Overall, our work provides practical tools for accurately releasing comprehensive GWAS results with provable protection of study participants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04225 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy Nandi, Anupama Neel, Seth Cho, Hyunghoon Quantitative Methods Genome-wide association studies (GWAS) are an essential tool in biomedical research for identifying genetic factors linked to health and disease. However, publicly releasing GWAS summary statistics poses well-recognized privacy risks, including the potential to infer an individual's participation in the study or to reveal sensitive phenotypic information (e.g., disease status). While differential privacy (DP) offers a rigorous mathematical framework for mitigating these risks, existing DP techniques for GWAS either introduce excessive noise or restrict the release to a limited set of results. In this work, we present practical DP mechanisms for releasing the complete set of genome-wide association statistics with privacy guarantees. We demonstrate the accuracy of the privacy-preserving statistics released by our mechanisms on a range of GWAS datasets from the UK Biobank, utilizing both real and simulated phenotypes. We introduce two key techniques to overcome the limitations of prior approaches: (1) an optimization-based randomization mechanism that directly minimizes the expected error in GWAS results to enhance utility, and (2) the use of personalized priors, derived from predictive models privately trained on a subset of the dataset, to enable sample-specific optimization which further reduces the amount of noise introduced by DP. Overall, our work provides practical tools for accurately releasing comprehensive GWAS results with provable protection of study participants. |
| title | GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2512.04225 |