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Scaling probabilistic models of genetic variation to millions of humans

Nature Genetics volume 48, pages 1587–1590 (2016)Cite this article

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Abstract

A major goal of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. The aggregated number of genotyped humans is currently on the order of millions of individuals, and existing methods do not scale to data of this size. To solve this problem, we developed TeraStructure, an algorithm to fit Bayesian models of genetic variation in structured human populations on tera-sample-sized data sets (1012 observed genotypes; for example, 1 million individuals at 1 million SNPs). TeraStructure is a scalable approach to Bayesian inference in which subsamples of markers are used to update an estimate of the latent population structure among individuals. We demonstrate that TeraStructure performs as well as existing methods on current globally sampled data, and we show using simulations that TeraStructure continues to be accurate and is the only method that can scale to tera-sample sizes.

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Figure 1: A schematic diagram of TeraStructure, stochastic variational inference for the PSD model.
Figure 2: TeraStructure recovers the underlying per-individual population proportions on the simulated data sets generated via scenario A.
Figure 3: TeraStructure is the most accurate method for scenario B simulations.

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Acknowledgements

W.H. and J.D.S. were supported in part by US NIH grant R01 HG006448 and ONR grant N00014-12-1-0764. D.M.B. is supported in part by ONR N00014-11-1-0651, DARPA FA8750-14-2-0009 and DARPA N66001-15-C-4032. We thank A. Ochoa for suggesting the design of the scenario B simulation.

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Authors and Affiliations

  1. Department of Computer Science, Princeton University, Princeton, New Jersey, USA

    Prem Gopalan

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA

    Wei Hao & John D Storey

  3. Department of Statistics, Columbia University, New York, New York, USA

    David M Blei

  4. Department of Computer Science, Columbia University, New York, New York, USA

    David M Blei

Authors
  1. Prem Gopalan
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  2. Wei Hao
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  3. David M Blei
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  4. John D Storey
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Contributions

D.M.B. and J.D.S. conceived the study. P.G. implemented the algorithm. W.H. carried out simulation studies. All authors performed data analyses and methods development and wrote the manuscript.

Corresponding authors

Correspondence to David M Blei or John D Storey.

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The authors declare no competing financial interests.

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Supplementary Figures 1–5, Supplementary Tables 1 and 2, and Supplementary Note. (PDF 1205 kb)

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Gopalan, P., Hao, W., Blei, D. et al. Scaling probabilistic models of genetic variation to millions of humans. Nat Genet 48, 1587–1590 (2016). https://doi.org/10.1038/ng.3710

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