Protecting Participant Privacy in Genotype-Phenotype Association Meta-analysis
Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies (GWAS). However, various recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. This thesis introduces a novel cryptographic strategy to securely perform meta-analysis for genotype-phenotype association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where data privacy or confidentiality is of concern. We validate our method using three multi-site meta-analyses from two large consortia. This work shows that genetic associations can be analyzed efficiently and accurately across study sites, without leaking information on individual participants and site-level study summaries.