Unsupervised Discovery of Ancestry Informative Markers and Genetic Admixture Proportions in Biobank-Scale Data Sets

Admixture estimation plays a crucial role in ancestry inference and genomewide association studies (GWAS). 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 10⁵to 10⁶samples 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, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank data sets. Our implementation of the method is called OpenADMIXTURE.