Scaling probabilistic models of genetic variation to millions of humans

Gopalan, Prem; Hao, Wei Min; Blei, David M.; Storey, John D.

doi:10.1101/013227

Cited by 12 publications

(31 citation statements)

References 29 publications

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“…The Human Genome Diversity Panel (HGDP) includes genomic samples from 52 human populations from across the world Cavalli-Sforza 2005). This set of populations is particularly well-suited as an example, because it has served as a test set for a variety of population structure methods (Corander et al 2004;Corander and Marttinen 2006;Francois et al 2006;Patterson et al 2006;Nievergelt et al 2007;Corander et al 2008;Hubisz et al 2009;Shringarpure and Xing 2009;Jombart et al 2010;Pickrell and Pritchard 2012;San Lucas et al 2012;Loh et al 2013;Frichot et al 2014;Raj et al 2014;Gopalan et al 2016;Granot et al 2016;Hao et al 2016;Hunley et al 2016;Zheng and Weir 2016). We generated a PST from 938 HGDP individuals, typed at 647,976 SNPs.…”

Section: Population Structure Tree In Humansmentioning

confidence: 99%

Network-based hierarchical population structure analysis for large genomic datasets

Greenbaum

Rubin

Templeton

et al. 2019

Preprint

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Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic datasets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these datasets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We demonstrate the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrates how network-based approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic datasets.

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Section: Population Structure Tree In Humansmentioning

confidence: 99%

Network-based hierarchical population structure analysis for large genomic datasets

Greenbaum

Rubin

Templeton

et al. 2019

Preprint

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“…Bayesian methods (GOPALAN et al, 2016;PRITCHARD et al, 2000;RAJ et al, 2014) fit the PSD model specifically, while existing maximum likelihood methods (ALEXANDER et al, 2009;TANG et al, 2005) and ALStructure require only the admixture model assumptions.…”

Section: The Admixture Modelmentioning

confidence: 99%

“…(i) the allele frequencies of ancestral populations (ii) the admixture proportions of each modern individual Many popular global ancestry estimation methods have been developed within a probabilistic framework. In these methods, which we will refer to as likelihood-based approaches, the strategy is to fit a probabilistic model to the observed genome-wide genotype data by either maximizing the likelihood function (ALEXANDER et al, 2009;TANG et al, 2005) or the posterior probability (GOPALAN et al, 2016;PRITCHARD et al, 2000;RAJ et al, 2014). The probabilistic model fit in each of these cases is the admixture model, described in detail in Section 2.1, in which the global ancestry quantities (i) and (ii) are explicit parameters to be estimated.…”

Section: Introductionmentioning

confidence: 99%

A likelihood-free estimator of population structure bridging admixture models and principal components analysis

Cabreros

Storey

2017

Preprint

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We introduce a simple and computationally efficient method for fitting the admixture model of genetic population structure, called ALStructure. The strategy of ALStructure is to first estimate the low-dimensional linear subspace of the population admixture components and then search for a model within this subspace that is consistent with the admixture model's natural probabilistic constraints. Central to this strategy is the observation that all models belonging to this constrained space of solutions are risk-minimizing and have equal likelihood, rendering any additional optimization unnecessary. The low-dimensional linear subspace is estimated through a recently introduced principal components analysis method that is appropriate for genotype data, thereby providing a solution that has both principal components and probabilistic admixture interpretations. Our approach differs fundamentally from other existing methods for estimating admixture, which aim to fit the admixture model directly by searching for parameters that maximize the likelihood function or the posterior probability. We observe that ALStructure typically outperforms existing methods both in accuracy and computational speed under a wide array of simulated and real human genotype datasets. Throughout this work we emphasize that the admixture model is a special case of a much broader class of models for which algorithms similar to ALStructure may be successfully employed.

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“…When genome-wide SNP data first became available, difficulties in applying the now classic Bayesian method for admixture inference (STRUCTURE [45]) occasioned the development of fast maximum-likelihood based approaches [47,48]. The past two years have seen the return of Bayesian approaches with two new fast approaches that use variational approximations [49,50]. Impressively, teraSTRUCTURE [50] handles samples containing millions of individuals.…”

Section: Refining and Expanding Models That Handle Human Admixturementioning

confidence: 99%

“…The past two years have seen the return of Bayesian approaches with two new fast approaches that use variational approximations [49,50]. Impressively, teraSTRUCTURE [50] handles samples containing millions of individuals. Local ancestry approaches have recently been improved by the development of fast algorithms that leverage rare variants within ancestries [51] and wavelet techniques [52].…”

Section: Refining and Expanding Models That Handle Human Admixturementioning

confidence: 99%

Recent advances in the study of fine-scale population structure in humans

Novembre

Peter

2016

Current Opinion in Genetics & Development

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Empowered by modern genotyping and large samples, population structure can be accurately described and quantified even when it only explains a fraction of a percent of total genetic variance. This is especially relevant and interesting for humans, where fine-scale population structure can both confound disease-mapping studies and reveal the history of migration and divergence that shaped our species’ diversity. Here we review notable recent advances in the detection, use, and understanding of population structure. Our work addresses multiple areas where substantial progress is being made: improved statistics and models for better capturing differentiation, admixture, and the spatial distribution of variation; computational speed-ups that allow methods to scale to modern data; and advances in haplotypic modeling that have wide ranging consequences for the analysis of population structure. We conclude by outlining four important open challenges: The limitations of discrete population models, uncertainty in individual origins, the incorporation of both fine-scale structure and ancient DNA in parametric models, and the development of efficient computational tools, particularly for haplotype-based methods.

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Scaling probabilistic models of genetic variation to millions of humans

Cited by 12 publications

References 29 publications

Network-based hierarchical population structure analysis for large genomic datasets

Network-based hierarchical population structure analysis for large genomic datasets

A likelihood-free estimator of population structure bridging admixture models and principal components analysis

Recent advances in the study of fine-scale population structure in humans

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