Privacy preserving identification of population stratification for collaborative genomic research.

ABSTRACT: The rapid improvements in genomic sequencing technology have led to the proliferation of locally collected genomic datasets. Given the sensitivity of genomic data, it is crucial to conduct collaborative studies while preserving the privacy of the individuals. However, before starting any collaborative research effort, the quality of the data needs to be assessed. One of the essential steps of the quality control process is population stratification: identifying the presence of genetic difference in individuals due to subpopulations. One of the common methods used to group genomes of individuals based on ancestry is principal component analysis (PCA). In this article, we propose a privacy-preserving framework which utilizes PCA to assign individuals to populations across multiple collaborat

SUBMITTER: Dervishi L 

PROVIDER: S-EPMC10311306 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature