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StructHDP: automatic inference of number of clusters and population structure from admixed genotype data.

Shringarpure S, Won D, Xing EP - Bioinformatics (2011)

Bottom Line: We use a Gibbs sampler to perform inference on the resulting model and infer the ancestry proportions and the number of clusters that best explain the data.Comparing the results of StructHDP with Structurama shows the utility of combining HDPs with the Structure model.We found that the clusters obtained correspond with major geographical divisions of the world, which is in agreement with previous analyses of the dataset.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. suyash@cs.cmu.edu

ABSTRACT

Motivation: Clustering of genotype data is an important way of understanding similarities and differences between populations. A summary of populations through clustering allows us to make inferences about the evolutionary history of the populations. Many methods have been proposed to perform clustering on multilocus genotype data. However, most of these methods do not directly address the question of how many clusters the data should be divided into and leave that choice to the user.

Methods: We present StructHDP, which is a method for automatically inferring the number of clusters from genotype data in the presence of admixture. Our method is an extension of two existing methods, Structure and Structurama. Using a Hierarchical Dirichlet Process (HDP), we model the presence of admixture of an unknown number of ancestral populations in a given sample of genotype data. We use a Gibbs sampler to perform inference on the resulting model and infer the ancestry proportions and the number of clusters that best explain the data.

Results: To demonstrate our method, we simulated data from an island model using the neutral coalescent. Comparing the results of StructHDP with Structurama shows the utility of combining HDPs with the Structure model. We used StructHDP to analyze a dataset of 155 Taita thrush, Turdus helleri, which has been previously analyzed using Structure and Structurama. StructHDP correctly picks the optimal number of populations to cluster the data. The clustering based on the inferred ancestry proportions also agrees with that inferred using Structure for the optimal number of populations. We also analyzed data from 1048 individuals from the Human Genome Diversity project from 53 world populations. We found that the clusters obtained correspond with major geographical divisions of the world, which is in agreement with previous analyses of the dataset.

Availability: StructHDP is written in C++. The code will be available for download at http://www.sailing.cs.cmu.edu/structhdp.

Contact: suyash@cs.cmu.edu; epxing@cs.cmu.edu.

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Related in: MedlinePlus

Graphical model representation of StructHDP with all priors represented. The shaded circle indicates the observed alleles. The dataset has N individuals, each genotyped at M loci. For ease of representation, we do not show the ploidy of the individual in the graphical model. The diamonds indicate parameters that are supplied by the user.
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Figure 11: Graphical model representation of StructHDP with all priors represented. The shaded circle indicates the observed alleles. The dataset has N individuals, each genotyped at M loci. For ease of representation, we do not show the ploidy of the individual in the graphical model. The diamonds indicate parameters that are supplied by the user.

Mentions: To allow for more flexibility with the parameter settings, we impose priors on α0, γ and the base distributions Hi. We assume that α0 and γ have Gamma priors with parameters (αa,αb) and (γa,γb), respectively, and that Hi has a symmetric Dirichlet distribution with parameter λ. The graphical model with all priors shown can be seen in Figure A11 in the Appendix A.(8)(9)(10)


StructHDP: automatic inference of number of clusters and population structure from admixed genotype data.

Shringarpure S, Won D, Xing EP - Bioinformatics (2011)

Graphical model representation of StructHDP with all priors represented. The shaded circle indicates the observed alleles. The dataset has N individuals, each genotyped at M loci. For ease of representation, we do not show the ploidy of the individual in the graphical model. The diamonds indicate parameters that are supplied by the user.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117349&req=5

Figure 11: Graphical model representation of StructHDP with all priors represented. The shaded circle indicates the observed alleles. The dataset has N individuals, each genotyped at M loci. For ease of representation, we do not show the ploidy of the individual in the graphical model. The diamonds indicate parameters that are supplied by the user.
Mentions: To allow for more flexibility with the parameter settings, we impose priors on α0, γ and the base distributions Hi. We assume that α0 and γ have Gamma priors with parameters (αa,αb) and (γa,γb), respectively, and that Hi has a symmetric Dirichlet distribution with parameter λ. The graphical model with all priors shown can be seen in Figure A11 in the Appendix A.(8)(9)(10)

Bottom Line: We use a Gibbs sampler to perform inference on the resulting model and infer the ancestry proportions and the number of clusters that best explain the data.Comparing the results of StructHDP with Structurama shows the utility of combining HDPs with the Structure model.We found that the clusters obtained correspond with major geographical divisions of the world, which is in agreement with previous analyses of the dataset.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. suyash@cs.cmu.edu

ABSTRACT

Motivation: Clustering of genotype data is an important way of understanding similarities and differences between populations. A summary of populations through clustering allows us to make inferences about the evolutionary history of the populations. Many methods have been proposed to perform clustering on multilocus genotype data. However, most of these methods do not directly address the question of how many clusters the data should be divided into and leave that choice to the user.

Methods: We present StructHDP, which is a method for automatically inferring the number of clusters from genotype data in the presence of admixture. Our method is an extension of two existing methods, Structure and Structurama. Using a Hierarchical Dirichlet Process (HDP), we model the presence of admixture of an unknown number of ancestral populations in a given sample of genotype data. We use a Gibbs sampler to perform inference on the resulting model and infer the ancestry proportions and the number of clusters that best explain the data.

Results: To demonstrate our method, we simulated data from an island model using the neutral coalescent. Comparing the results of StructHDP with Structurama shows the utility of combining HDPs with the Structure model. We used StructHDP to analyze a dataset of 155 Taita thrush, Turdus helleri, which has been previously analyzed using Structure and Structurama. StructHDP correctly picks the optimal number of populations to cluster the data. The clustering based on the inferred ancestry proportions also agrees with that inferred using Structure for the optimal number of populations. We also analyzed data from 1048 individuals from the Human Genome Diversity project from 53 world populations. We found that the clusters obtained correspond with major geographical divisions of the world, which is in agreement with previous analyses of the dataset.

Availability: StructHDP is written in C++. The code will be available for download at http://www.sailing.cs.cmu.edu/structhdp.

Contact: suyash@cs.cmu.edu; epxing@cs.cmu.edu.

Show MeSH
Related in: MedlinePlus