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Probabilistic models of genetic variation in structured populations applied to global human studies.

ABSTRACT: Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes without requiring a higher-level admixture interpretation.We formulate two general probabilistic models, and we propose computationally efficient algorithms to estimate them. First, we show how principal component analysis can be utilized to estimate a general model that includes the well-known Pritchard-Stephens-Donnelly admixture model as a special case. Noting some drawbacks of this approach, we introduce a new 'logistic factor analysis' framework that seeks to directly model the logit transformation of probabilities underlying observed genotypes in terms of latent variables that capture population structure. We demonstrate these advances on data from the Human Genome Diversity Panel and 1000 Genomes Project, where we are able to identify SNPs that are highly differentiated with respect to structure while making minimal modeling assumptions.A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.htmljstorey@princeton.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Hao W

PROVIDER: S-EPMC4795615 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Probabilistic models of genetic variation in structured populations applied to global human studies.

Hao Wei W Song Minsun M Storey John D JD

Bioinformatics (Oxford, England) 20151106 5

<h4>Motivation</h4>Modern population genetics studies typically involve genome-wide genotyping of individuals from a diverse network of ancestries. An important problem is how to formulate and estimate probabilistic models of observed genotypes that account for complex population structure. The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual. Here, we instead focus on modeling variation of the genotypes w ...[more]

PMID: 26545820

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Project description:MotivationGenetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait.ResultsOur algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches.Availability and implementationOur software is available at https://github.com/anand-bhaskar/gap .Contactsabhaskar@stanford.edu or ajavanma@usc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Probabilistic models of genetic variation in structured populations applied to global human studies.

Publications

Probabilistic models of genetic variation in structured populations applied to global human studies.

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