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Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.
Ko, Seyoon; Chu, Benjamin B; Peterson, Daniel; Okenwa, Chidera; Papp, Jeanette C; Alexander, David H; Sobel, Eric M; Zhou, Hua; Lange, Kenneth L.
  • Ko S; 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.
  • Chu BB; 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.
  • Peterson D; Department of Mathematics, Brigham Young University, Provo, UT 84602, USA.
  • Okenwa C; Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720, USA.
  • Papp JC; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Alexander DH; X Development LLC, Mountain View, CA 94043, USA.
  • Sobel EM; 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. Electronic address: esobel@ucla.edu.
  • Zhou H; 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.
  • Lange KL; 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.
Am J Hum Genet ; 110(2): 314-325, 2023 02 02.
Article en En | MEDLINE | ID: mdl-36610401
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bancos de Muestras Biológicas / Estudio de Asociación del Genoma Completo Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Bancos de Muestras Biológicas / Estudio de Asociación del Genoma Completo Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article