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Ko S, Chu BB, Peterson D, Okenwa C, Papp JC, Alexander DH, Sobel EM, Zhou H, Lange KL. Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets. Am J Hum Genet 2023; 110:314-325. [PMID: 36610401 PMCID: PMC9943729 DOI: 10.1016/j.ajhg.2022.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/12/2022] [Indexed: 01/09/2023] Open
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, 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.
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Affiliation(s)
- Seyoon Ko
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Benjamin B. Chu
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Daniel Peterson
- Department of Mathematics, Brigham Young University, Provo, UT 84602, USA
| | - Chidera Okenwa
- Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jeanette C. Papp
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | | | - Eric M. Sobel
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA,Corresponding author
| | - Hua Zhou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kenneth L. Lange
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Statistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
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