Abstract
Population structure in genetic data depends on complex demographic processes including geographic isolation, genetic drift, migration, and admixture. Together with technical artifacts, population structure is a prominent confounder of genomic studies. Identifying such patterns is therefore central to the genomic enterprise. Whereas many methods can identify specific types of population structure, few are able to provide simple representations of genomic diversity across a range of scales.
Here we investigate an approach to dimension reduction and visualization of genomic data that combines principal components analysis (PCA) with uniform manifold approximation and projection (UMAP) to succinctly illustrate population structure in large cohorts and capture their relationships on local and global scales. We demonstrate using genotype data from the 1000 Genome Project, the Health and Retirement Study, and the UK Biobank that projections using PCA-UMAP effectively cluster individuals who are genetically closely related while placing them in a global continuum of genetic variation. These projections reveal non-trivial population groupings, reflect ethnicity and geography on fine-scale levels, and uncover patterns in the distributions of a variety of phenotypes, establishing PCA-UMAP as a general-purpose approach toexploratory analysis in genomics.
