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Neural ADMIXTURE: rapid population clustering with autoencoders

Albert Dominguez Mantes, Daniel Mas Montserrat, Carlos D. Bustamante, Xavier Giró-i-Nieto, Alexander G. Ioannidis
doi: https://doi.org/10.1101/2021.06.27.450081
Albert Dominguez Mantes
1Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
2Signal Theory and Communications Department, Universitat Polit`ecnica de Catalunya, Barcelona, Catalonia, Spain
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  • For correspondence: adomi@stanford.edu
Daniel Mas Montserrat
1Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
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Carlos D. Bustamante
1Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
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Xavier Giró-i-Nieto
2Signal Theory and Communications Department, Universitat Polit`ecnica de Catalunya, Barcelona, Catalonia, Spain
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Alexander G. Ioannidis
1Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
3Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, United States
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Abstract

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. In addition, this network can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. These models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* adomi{at}stanford.edu and ioannidis{at}stanford.edu

  • https://github.com/AI-sandbox/neural-admixture

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 28, 2021.
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Neural ADMIXTURE: rapid population clustering with autoencoders
Albert Dominguez Mantes, Daniel Mas Montserrat, Carlos D. Bustamante, Xavier Giró-i-Nieto, Alexander G. Ioannidis
bioRxiv 2021.06.27.450081; doi: https://doi.org/10.1101/2021.06.27.450081
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Neural ADMIXTURE: rapid population clustering with autoencoders
Albert Dominguez Mantes, Daniel Mas Montserrat, Carlos D. Bustamante, Xavier Giró-i-Nieto, Alexander G. Ioannidis
bioRxiv 2021.06.27.450081; doi: https://doi.org/10.1101/2021.06.27.450081

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