4.7 Article

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

Journal

AMERICAN JOURNAL OF HUMAN GENETICS
Volume 110, Issue 2, Pages 313-325

Publisher

CELL PRESS
DOI: 10.1016/j.ajhg.2022.12.008

Keywords

-

Ask authors/readers for more resources

This paper introduces an unsupervised and scalable method for selecting ancestry-informative SNP markers and estimating admixture proportions. The method, implemented in the open-source software OpenADMIXTURE, shows scalability to modern biobank datasets in simulated and real data examples.
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, 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 datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available