fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets

By Anil Raj, Matthew Stephens, J. K. Pritchard. 2014

1,583 citations recorded by the source corpus.

Tools for estimating population structure from genetic data are now used in a wide variety of applications in population genetics. However, inferring population structure in large modern data sets imposes severe computational challenges. Here, we develop efficient algorithms for approximate inference of the model underlying the STRUCTURE program using a variational Bayesian framework. Variational methods pose the problem of computing relevant posterior distributions as an optimization problem, allowing us to build on recent advances in optimization theory to develop fast inference tools. In addition, we propose useful heuristic scores to identify the number of populations represented in a data set and a new hierarchical prior to detect weak population structure in the data. We test the variational algorithms on simulated data and illustrate using genotype data from the CEPH–Human Genome Diversity Panel. The variational algorithms are almost two orders of magnitude faster than STRUCTURE and achieve accuracies comparable to those of ADMIXTURE. Furthermore, our results show that the heuristic scores for choosing model complexity provide a reasonable range of values for the number of populations represented in the data, with minimal bias toward detecting structure when it is very weak. Our algorithm, fastSTRUCTURE, is freely available online at http://pritchardlab.stanford.edu/structure.html.

Subfields: Genetics

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, background): “…and Stephens 2012), regulatory network analysis (Sanguinetti, Lawrence, and Rattray 2006), motif detection (Xing et al. 2004), phylogenetic hidden Markov models (Jojic et al. 2004), population genetics (Raj, Stephens, and Pritchard 2014), and gene expression analysis (Stegle et al. 2010).”
Stochastic Gradient Markov Chain Monte Carlo (2019, positive): “he demand to store and analyse large-scale data sets and to ﬁt increasingly sophisticated and complex models to these high-dimensional data. For example, scientiﬁc ﬁelds, such as population genetics (Raj et al., 2014), brain imaging (Andersen et al., 2018) and natural language processing (Yogatama e”
pophelper: an R package and web app to analyse and visualize population structure (2017, methodology): “Some of the soft-wares discussed in this article includes ADMIXTURE (Alexander et al. 2009), FASTSTRUCTURE (Raj et al. 2014), STRUCTURE (Pritchard et al. 2000) and TESS (Franc (cid:1) ois & Durand 2010).”
StructureSelector: A web‐based software to select and visualize the optimal number of clusters using multiple methods (2018, methodology): “With the advance of the next-generation sequencing, rather than STRUCTURE , both ADMIX-TURE and FASTSTRUCTURE have been widely adopted in population genomic studies.”
Genomic and Chemical Diversity in Cannabis (2015, methodology): “Bayesian clustering method of fastSTRUCTURE to the same 195 samples (Raj et al., 2014).”
Genotyping-By-Sequencing for Plant Genetic Diversity Analysis: A Lab Guide for SNP Genotyping (2014, methodology): “Thus, it is desirable to develop more computation-friendly tools, such as fastSTRUCTURE [36], to analyze large GBS data.”
dartr: An r package to facilitate analysis of SNP data generated from reduced representation genome sequencing (2018, methodology): “The package provides an export function to convert genlight objects to be used for the following software programs: STRUCTURE (Pritchard, Stephens, & Donnelly, 2000), FASTSTRUCTURE (Raj, Stephens, & Pritchard, 2014), NEWHYBRIDS (Anderson & Thompson, 2002).”