Assessing transcriptomic reidentification risks using discriminative sequence models.

ABSTRACT: Gene expression data provide molecular insights into the functional impact of genetic variation, for example, through expression quantitative trait loci (eQTLs). With an improving understanding of the association between genotypes and gene expression comes a greater concern that gene expression profiles could be matched to genotype profiles of the same individuals in another data set, known as a linking attack. Prior works show such a risk could analyze only a fraction of eQTLs that is independent owing to restrictive model assumptions, leaving the full extent of this risk incompletely understood. To address this challenge, we introduce the discriminative sequence model (DSM), a novel probabilistic framework for predicting a sequence of genotypes based on gene expression data. By modeling

SUBMITTER: Sadhuka S 

PROVIDER: S-EPMC10538488 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature