Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.

ABSTRACT: Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 10⁵ to 10⁶ samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper,

SUBMITTER: Ko S 

PROVIDER: S-EPMC9943729 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature