More practical differentially private publication of key statistics in GWAS

Motivation: Analyses of datasets that contain personal genomic information are very important for revealing associations between diseases and genomes. Genome-wide association studies, which are large-scale genetic statistical analyses, often involve tests with contingency tables. However, if the statistics obtained by these tests are made public as they are, sensitive information of individuals could be leaked. Existing studies have proposed privacy-preserving methods for statistics in the χ² test with a 3 × 2 contingency table, but they do not cover all the tests used in association studies. In addition, existing methods for releasing differentially private P-values are not practical. Results: In this work, we propose methods for releasing statistics in the χ² test, the Fisher's exact test and the Cochran-Armitage's trend test while preserving both personal privacy and utility. Our methods for releasing P-values are the first to achieve practicality under the concept of differential privacy by considering their base 10 logarithms. We make theoretical guarantees by showing the sensitivity of the above statistics. From our experimental results, we evaluate the utility of the proposed methods and show appropriate thresholds with high accuracy for using the private statistics in actual tests.

A python implementation of our experiments is available at https://github.com/ay0408/DP-statistics-GWAS.

Supplementary data are available at Bioinformatics Advances online.