Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Abstract

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.

Keywords: genetic data encryption; genomic privacy; genotype imputation.

Figure 1.

Illustration of secure genotype imputation. a) Illustration of the genotype imputation scenario. The incomplete genotypes are measured by genotyping arrays with missing genotypes (represented by stars). Encryption generates random-looking string from the genotypes. At the server, encrypted genotypes are encoded, then they are used to compute the missing variant genotype probabilities. The encrypted probabilities are sent to the researcher, who decrypts the probabilities identifies the genotypes with highest probabilities (italic values). b) Building of the plaintext model for genotype imputation. The server uses a publicly available panel to build genotype estimation models for each variant. The models are stored in plaintext domain. The model in the current study is a linear model where each variant genotype is modeled using genotypes of variants within a k variant vicinity of the target variant. c) The plaintext models implemented under the secure frameworks.

Figure 2.

Accuracy benchmarks. Accuracy for all genotypes (a) and the non-reference genotypes (b) are shown for each method (x-axis). Average accuracy value is shown at the top of each bar for comparison. Precision-recall curves are plotted for all genotypes (c) and the non-reference genotypes (d). Plaintext indicates the non-secure methods.

Figure 3.

The population stratification of the accuracy is shown for EUR all genotypes (a) and non-ref genotypes (b), AMR all (c) and non-ref (d) genotypes, and AFR all (e), and non-ref genotypes (f). Precision-recall curve for rare variants (g). The box plots illustrate the super-population-specific minor allele frequency distribution (y-axis) for the common (top) and un-common variants (bottom) (h). ALL indicates the MAF distribution for all populations. The center and the two ends of the boxplots show the median and 25–75% values of the MAF distributions.

Figure 4.

Memory and time requirements of the secure methods. Each method is divided into 4 steps, (1) key generation, (2) Encryption, (3) Evaluation, (4) Decryption. The bar plots show the time requirements (a) using 20K, 40K, and 80K target variant sets. The aggregated time (b) and the maximum memory usage of the methods are also shown (c).

Figure 5.

Illustration of a secure outsourced imputation service (a). The time (b) and memory requirements (c) are illustrated in the bar plots where colors indicate security context. The y-axis shows the time (in seconds) and main memory (in gigabytes) used by each method to perform the imputation of the 80K variants where the secure outsourced method includes the plaintext model training and secure model evaluation steps.