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PLIGHT: A tool to assess privacy risk by inferring identifying characteristics from sparse, noisy genotypes

The leakage of identifying information in genetic and omics data has been established in many studies, with single nucleotide polymorphisms (SNPs) shown to carry a strong risk of reidentification for individuals and their genetic relatives. While the ability of thousands or hundreds of thousands of SNPs (especially rare ones) to identify individuals has been demonstrated, here we sought to measure the informativeness of even a sparse set of tens of noisy, common SNPs from an individual, by putting the genotype-based privacy leakage from an individual on quantitative footing. We present a computational tool, PLIGHT ("Privacy Leakage by Inference across Genotypic HMM Trajectories"), that employs a population-genetics-based Hidden Markov Model of recombination and mutation to find piecewise matches of a sparse query set of SNPs to a reference genotype panel. Given the ready availability of auxiliary sources of noisy genotype data -- such as acquiring small samples of environmental DNA or learning about someone's Mendelian diseases and physical characteristics -- inference on sparse data becomes a genuine concern. We explore cases where query individuals are either known to be in databases or not, and consider both simulated "mosaics" of genotypes (i.e. genotypes stitched together from diploid segments sampled from two or more source individuals) and actual genotype data obtained from swabs of coffee cups used by a known individual. Our findings are as follows: (1) Even 10 common SNPs (minor allele frequency > 0.05) often are sufficient to identify individuals in conventional genomic databases. (2) We are able to identify first-order relatives (parents, children and siblings) of query individuals with 20-30 common SNPs. (3) We find some potential for leakage of phenotypic information, based on a simulated attack by combining polygenic risk scores (PRSs) of the piecewise genotypic matches. We also found, for simulated mosaics of two individuals, that 20 common SNPs were often sufficient to find the correct identities of both component individuals. Finally, applying PLIGHT to coffee-cup-derived SNPs, we find that our tool is able identify the individual (when present in the reference database) using as little as 30 SNPs; alternatively, when the individual is not present in the reference database, we reconstruct possible genomes for the individual based on just 30-90 query SNPs by piecewise matching to the reference haplotype database. In this way, we are able to perform a small degree of imputation of unobserved query SNPs. Overall, the tool could be used to determine the value of selectively masking released SNPs, in a way that is agnostic to any explicit assumptions about underlying population membership or allele frequencies.