Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases.

ABSTRACT: Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider s

SUBMITTER: Emani PS 

PROVIDER: S-EPMC10760520 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature