ProxyTyper: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

Abstract

One of the major challenges in genomic data sharing is protecting the privacy of participants. Numerous studies demonstrated that genetic data and related summary statistics can be used for identifying individuals. These studies led to a strong chilling effect on researchers that hindered collaborative data sharing. Consequently, population-level genetic databases are often siloed in central repositories with complex and burdensome data usage agreements. While cryptographic methods that are provably secure have been developed, these methods require high-level expertise in security and depend on large computational resources.To fill the methodological gap in this domain, we present ProxyTyper, a set of data protection mechanisms to generate “proxy-panels” from sensitive haplotype-level genetic datasets. ProxyTyper uses haplotype sampling, allele hashing, and anonymization to protect the genetic variant coordinates, genetic maps, and chromosome-wide haplotypes. These mechanisms can provide strong deterrence against honest-but-curious entities and well-known re-identification and linking attacks. The proxy panels can be used as input to existing tools without any modifications to the underlying algorithms. We focus on building proxy-panels for genotype imputation to protect typed and untyped variants. We demonstrate that proxy-based imputation provides protection against well-known attacks with a minor decrease of imputation accuracy for variants in wide range of allele frequencies.