Large scale proteomic studies create novel privacy considerations

Abstract

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90–95% of proteomes to their correct genome and for 95–99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Assessing transcriptomic reidentification risks using discriminative sequence models

“…Our framework unifies QTL privacy analyses, because it allows joint prediction of genotypes from any source. It would be interesting to explore incorporating other sources of phenotypic data that could leak genotype information into the DSM, including protein abundance (pQTLs) (Hill et al 2023), methylation (mQTLs) (Gaunt et al 2016;Backes et al 2017), and allele-specific expression (aseQTLs) (Gürsoy et al 2021). Another promising direction would be extending the DSM formulation to the coexpression of genes conditional upon eQTLs, which has been used by other models (Gamazon et al 2015;Gusev et al 2016).…”

“…The growing availability of large-scale genomic data repositories has led to increasing concerns for the privacy of the individuals from whom the data were collected ( Erlich and Narayanan 2014 ; Naveed et al 2015 ; Bonomi et al 2020 ). Although many nations and organizations have introduced policies and regulations (e.g., HIPAA and GDPR) to safeguard the collection, use, and sharing of personally-identifying information, existing policies often fall short of providing clear guidance regarding many widely used types of biomedical data, including genetic sequences and functional genomic data, for which the underlying privacy risks are often unclear and only beginning to be understood ( Clayton et al 2019 ; Gürsoy et al 2020 , 2021 , 2022b ; Wan et al 2022 ; Hill et al 2023 ). This lack of guidance, particularly for functional genomic data, presents a key challenge for ensuring the protection of study participants, leaving the possibility for future privacy breaches that may diminish public trust in the scientific community.…”