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Scaling probabilistic models of genetic variation to millions of humans

Prem Gopalan, Wei Hao, David M. Blei, John D. Storey
doi: https://doi.org/10.1101/013227
Prem Gopalan
1Department of Computer Science, Princeton University, Princeton NJ 08544 USA
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Wei Hao
2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton NJ 08544 USA
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David M. Blei
3Departments of Statistics and Computer Science, Columbia University, New York NY 10027 USA
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  • For correspondence: david.blei@columbia.edu jstorey@princeton.edu
John D. Storey
2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton NJ 08544 USA
4Center for Statistics and Machine Learning, Princeton University, Princeton NJ 08544 USA
5Department of Molecular Biology, Princeton University, Princeton NJ 08544 USA
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  • For correspondence: david.blei@columbia.edu jstorey@princeton.edu
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Abstract

One of the major goals of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. To this end, researchers have developed sophisticated statistical methods to capture the complex population structure that underlies observed genotypes in humans, and such methods have been effective for analyzing modestly sized genomic data sets. However, the number of genotyped humans has grown significantly in recent years, and it is accelerating. In aggregate about 1M individuals have been genotyped to date. Analyzing these data will bring us closer to a nearly complete picture of human genetic variation; but existing methods for population genetics analysis do not scale to data of this size. To solve this problem we developed TeraStructure. TeraStructure is a new algorithm to fit Bayesian models of genetic variation in human populations on tera-sample-sized data sets (1012 observed genotypes, e.g., 1M individuals at 1M SNPs). It is a principled approach to Bayesian inference that iterates between subsampling locations of the genome and updating an estimate of the latent population structure of the individuals. On data sets of up to 2K individuals, TeraStructure matches the existing state of the art in terms of both speed and accuracy. On simulated data sets of up to 10K individuals, TeraStructure is twice as fast as existing methods and has higher accuracy in recovering the latent population structure. On genomic data simulated at the tera-sample-size scales, TeraStructure continues to be accurate and is the only method that can complete its analysis.

Software TeraStructure is available for download at https://github.com/premgopalan/terastructure.

Funding This research was supported in part by NIH grant R01 HG006448 and ONR grant N00014-12-1-0764.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 28, 2015.
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Scaling probabilistic models of genetic variation to millions of humans
Prem Gopalan, Wei Hao, David M. Blei, John D. Storey
bioRxiv 013227; doi: https://doi.org/10.1101/013227
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Scaling probabilistic models of genetic variation to millions of humans
Prem Gopalan, Wei Hao, David M. Blei, John D. Storey
bioRxiv 013227; doi: https://doi.org/10.1101/013227

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