다기관 유전체 연구에서 유전체 정보 공유의 한계와 그 해결책

Abstract

The advance in high-throughput genomic technology facilitated the generation of genomic data at an ever-decreasing cost. Aggregation of genomic data is indispensable for the progress of biomedical research. Sharing these data sets yields unbiased and novel findings through an increased sample size. However, a growing concern is the ability to protect the genetic privacy of the data originators. Therefore, full discloser of genetic information of samples is often limited, whereas summary-level statistics are shared among institutions. Although some researches proposed cryptographical or statistical approaches to moderate tension between the application of genomic data and privacy, most of them were kept within achieving a single determinate application. We present a framework that reconciles privacy and genomic analysis by sharing additional distance information along with the summary-level genetic data of samples. In this work, we describe our framework which is built upon the concept of multilateration, a localization technique for wireless sensor networks in which spatial coordinates of a node with an unknown position are inferred by distances measured from the node to several reference nodes at known positions. We show that through our framework, certain types of genomic analysis can be achieved, such as identifying sample overlaps and close relatives, decomposing ancestry, and mapping of geographical origin, without disclosing personal genomes.