Local Haplotype Classifiers enable Efficient, Flexible, and Secure Genotype Imputation and Downstream Analyses
The decreasing cost of genotyping technologies led to abundant availability and usage of genetic data. Although it offers many potentials for improving health and curing diseases, genetic data is highly intrusive in many aspects of individual privacy. Secure genotype analysis methods have been developed to perform numerous tasks such as genome-wide association studies, meta-analysis, kinship inference, and genotype imputation outsourcing. Here we present a new approach for using lightweight haplotype classifier models to use predicted haplotype information in a flexible privacy-preserving framework to perform genotype imputation and downstream tasks. Compared to the previous secure methods that rely main on linear models, our approach utilizes efficient models that rely on utilizing haplotypic information, which improves accuracy and increases the throughput of imputation by performing multiple imputations per model evaluation.