| Title: | Neural ADMIXTURE for rapid genomic clustering |
| Journal: | Nature Computational Science |
| Published: | 6 Jul 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37600116/ |
| DOI: | https://doi.org/10.1038/s43588-023-00482-7 |
| URL: | https://www.nature.com/articles/s43588-023-00482-7.pdf |
Publication 11618
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Abstract
Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA variant frequencies. However, with rapidly increasing biobank sizes, these methods have become computationally intractable. Here we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as the current standard algorithm, ADMIXTURE, while reducing the compute time by orders of magnitude surpassing even the fastest alternatives. One month of continuous compute using ADMIXTURE can be reduced to just hours with Neural ADMIXTURE. A multi-head approach allows Neural ADMIXTURE to offer even further acceleration by computing multiple cluster numbers in a single run. Furthermore, the models can be stored, allowing cluster assignment to be performed on new data in linear time without needing to share the training samples.</p>
5 Authors
- Albert Dominguez Mantes
- Daniel Mas Montserrat
- Carlos D. Bustamante
- Xavier Giró-i-Nieto
- Alexander G. Ioannidis
1 Application
| Application ID | Title |
|---|---|
| 89006 | Comparison of the genetic architecture between Latino and UKBB populations as strategy to understand its role on human health traits and diseases. |
Enabling scientific discoveries that improve human health