UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts

English version

Autoři: Alex Diaz-Papkovich ^aff001; Luke Anderson-Trocmé ^aff002; Chief Ben-Eghan ^aff002; Simon Gravel ^aff002
Působiště autorů: Quantitative Life Sciences, McGill University, Montreal, Québec, Canada ^aff001; McGill University and Genome Quebec Innovation Centre, Montreal, Québec, Canada ^aff002; Department of Human Genetics, McGill University, Montreal, Quebec, Canada ^aff003
Vyšlo v časopise: UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts. PLoS Genet 15(11): e32767. doi:10.1371/journal.pgen.1008432
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1008432

Souhrn

Human populations feature both discrete and continuous patterns of variation. Current analysis approaches struggle to jointly identify these patterns because of modelling assumptions, mathematical constraints, or numerical challenges. Here we apply uniform manifold approximation and projection (UMAP), a non-linear dimension reduction tool, to three well-studied genotype datasets and discover overlooked subpopulations within the American Hispanic population, fine-scale relationships between geography, genotypes, and phenotypes in the UK population, and cryptic structure in the Thousand Genomes Project data. This approach is well-suited to the influx of large and diverse data and opens new lines of inquiry in population-scale datasets.

Klíčová slova:

Principal component analysis – Europe – Ethnicities – Data visualization – African people – Chinese people – Hispanic people – Caribbean

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