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bioRxiv · 10.1101/454355

Fast Hierarchical Bayesian Analysis of Population Structure

Tonkin-Hill, G.·Lees, J. A.·Bentley, S. D.·Frost, S. D. W.·Corander, J.
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Abstract

We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly identifies an approximate fit to a Dirichlet Process Mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10-100 times larger than the existing model-based methods, which we demonstrate by analysing an alignment of over 110,000 sequences of HIV-1 pol genes. We also provide a method for rapidly partitioning an existing hierarchy in order to maximise the DPM model marginal likelihood, allowing us to split phylogenetic trees into clades and subclades using a population genomic model. Extensive tests on simulated data as well as a diverse set of real bacterial and viral datasets show that fastbaps provides comparable or improved solutions to previous model-based methods, while generally being significantly faster. The method is made freely available under an open source MIT licence as an easy to use R package at https://github.com/gtonkinhill/fastbaps.

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Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W., Corander, J.. 2018-10-26. Fast Hierarchical Bayesian Analysis of Population Structure. https://doi.org/10.1101/454355

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