2026-09-19T17:17:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1539062024-03-22T03:33:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Berger, Bonnie
Cho, Hyunghoon
Yen, Derek Jia-Wen
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-03-21T19:15:09Z
2024-03-21T19:15:09Z
2024-02
2024-03-04T16:38:08.614Z
https://hdl.handle.net/1721.1/153906
Polygenic risk scores (PRS) are used to quantify the additive effect of single nucleotide polymorphisms (SNPs) on an individual’s genetic risk for developing a particular trait or condition. Collaborations between data centers are important for improving the statistical power and validity of PRS through larger, more genetically diverse datasets. However, owing to the privacy concerns inherent in genomic data, regulations restrict institutions’ capacity to share data. Using cryptography, we present a secure and federated implementation of a Monte Carlo algorithm for PRS, enabling collaborations that respect data regulations. To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest.
M.Eng.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Private Random Variate Sampling for Secure and Federated Polygenic Risk Scores
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Polygenic risk scores (PRS) are used to quantify the additive effect of single nucleotide polymorphisms (SNPs) on an individual’s genetic risk for developing a particular trait or condition. Collaborations between data centers are important for improving the statistical power and validity of PRS through larger, more genetically diverse datasets. However, owing to the privacy concerns inherent in genomic data, regulations restrict institutions’ capacity to share data. Using cryptography, we present a secure and federated implementation of a Monte Carlo algorithm for PRS, enabling collaborations that respect data regulations. To implement a Monte Carlo algorithm in a privacy-preserving context, our work exhibits techniques for sampling random variates with cryptographically private parameters, which may be of independent interest.</Abstract>
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