Private Random Variate Sampling for Secure and Federated Polygenic Risk Scores

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Yen, Derek Jia-Wen

Advisors dc:contributor.advisor

• Berger, Bonnie
• Cho, Hyunghoon

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153906

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153906