SHIELD: Secure Haplotype Imputation Employing Local Differential Privacy

TL;DR

SHIELD is a privacy-preserving method for haplotype imputation that combines genetic modeling with local differential privacy to accurately infer genotypes without compromising donor privacy.

Contribution

It introduces a novel approach integrating the Li-Stephens model with local differential privacy for secure haplotype imputation.

Findings

Achieves accurate genotype estimation while preserving privacy.

Utilizes the forward-backward algorithm within a privacy framework.

Demonstrates effectiveness on genetic data with privacy guarantees.

Abstract

We introduce Secure Haplotype Imputation Employing Local Differential privacy (SHIELD), a program for accurately estimating the genotype of target samples at markers that are not directly assayed by array-based genotyping platforms while preserving the privacy of donors to public reference panels. At the core of SHIELD is the Li-Stephens model of genetic recombination, according to which genomic information is comprised of mosaics of ancestral haplotype fragments that coalesce via a Markov random field. We use the standard forward-backward algorithm for inferring the ancestral haplotypes of target genomes, and hence the most likely genotype at unobserved sites, using a reference panel of template haplotypes whose privacy is guaranteed by the randomized response technique from differential privacy.