Protecting Genomic Data Privacy with Probabilistic Modeling
Name
9789813279827_0037.pdf
Description
Published version
Size
1.18 MB
Format
Adobe PDF
Checksum (MD5)
4fd736b5e81ea690c896ceb0127eb5c9
Author(s)
Simmons, Sean
Berger Leighton, Bonnie
Sahinalp, Cenk
Date Issued
2019
Journal
Biocomputing 2019
Publisher
World Scientific
Citation
Gasdaska, Angela et al. "Leveraging summary statistics to make inferences about complex phenotypes in large biobanks." Biocomputing 2019, January 2019, Kohala Coast, Hawaii, USA, World Scientific, 2018 © 2018 The Authors
Version
Final published version
Abstract
As genetic sequencing becomes less expensive and data sets linking genetic data and medical records (e.g., Biobanks) become larger and more common, issues of data privacy and computational challenges become more necessary to address in order to realize the benefits of these datasets. One possibility for alleviating these issues is through the use of already-computed summary statistics (e.g., slopes and standard errors from a regression model of a phenotype on a genotype). If groups share summary statistics from their analyses of biobanks, many of the privacy issues and computational challenges concerning the access of these data could be bypassed. In this paper we explore the possibility of using summary statistics from simple linear models of phenotype on genotype in order to make inferences about more complex phenotypes (those that are derived from two or more simple phenotypes). We provide exact formulas for the slope, intercept, and standard error of the slope for linear regressions when combining phenotypes. Derived equations are validated via simulation and tested on a real data set exploring the genetics of fatty acids. Keywords: privacy; biobank; genetics; genome-wide association study; single nucleotide variant; computational challenges; data security; phenotypes
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1142/9789813279827_0037
