Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases

F. Yu, M. Rybar, C. Uhler, S. Fienberg, in:, J. Domingo Ferrer (Ed.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 170–184.


Conference Paper | Published | English
Author
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Editor
Series Title
LNCS
Abstract
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems with convex penalty functions and selecting the penalty parameters by cross-validation. In particular, we focus on penalized logistic regression with elastic-net regularization, a method widely used to in GWAS analyses to identify disease-causing genes. We show how a differentially private procedure for penalized logistic regression with elastic-net regularization can be applied to the analysis of GWAS data and evaluate our method’s performance.
Publishing Year
Date Published
2014-01-01
Proceedings Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Acknowledgement
This research was partially supported by BCS- 0941518 to the Department of Statistics at Carnegie Mellon University.
Volume
8744
Page
170 - 184
Conference
PSD: Privacy in Statistical Databases
Conference Location
Ibiza, Spain
Conference Date
2014-09-17 – 2014-09-19
IST-REx-ID

Cite this

Yu F, Rybar M, Uhler C, Fienberg S. Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. In: Domingo Ferrer J, ed. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8744. Springer; 2014:170-184. doi:10.1007/978-3-319-11257-2_14
Yu, F., Rybar, M., Uhler, C., & Fienberg, S. (2014). Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. In J. Domingo Ferrer (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8744, pp. 170–184). Ibiza, Spain: Springer. https://doi.org/10.1007/978-3-319-11257-2_14
Yu, Fei, Michal Rybar, Caroline Uhler, and Stephen Fienberg. “Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by Josep Domingo Ferrer, 8744:170–84. Springer, 2014. https://doi.org/10.1007/978-3-319-11257-2_14.
F. Yu, M. Rybar, C. Uhler, and S. Fienberg, “Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Ibiza, Spain, 2014, vol. 8744, pp. 170–184.
Yu F, Rybar M, Uhler C, Fienberg S. 2014. Differentially-private logistic regression for detecting multiple-SNP association in GWAS databases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PSD: Privacy in Statistical Databases, LNCS, vol. 8744. 170–184.
Yu, Fei, et al. “Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by Josep Domingo Ferrer, vol. 8744, Springer, 2014, pp. 170–84, doi:10.1007/978-3-319-11257-2_14.

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