ArticleBioinformatics (Oxford, England)2016
Probabilistic models of genetic variation in structured populations applied to global human studies.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- Reflections on the Human Genome Diversity Project: a conversation with Marcus W. Feldman, Henry T. Greely, and Mary-Claire King.Genetics · 2026Article
- Coancestry superposed on admixed populations yields measures of relatedness at individual-level resolution.PLoS computational biology · 2025Article
- Multi-ancestry genome-wide association analyses: a comparison of meta- and mega-analyses in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study.BMC genomics · 2025Article
- Identifying latent genetic interactions in genome-wide association studies using multiple traits.Genome medicine · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries.Genetics · 2021Article
- Estimating FST and kinship for arbitrary population structures.PLoS genetics · 2021Article
- Ancestry-agnostic estimation of DNA sample contamination from sequence reads.Genome research · 2020Article
- Network-based hierarchical population structure analysis for large genomic data sets.Genome research · 2019Article
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- A Likelihood-Free Estimator of Population Structure Bridging Admixture Models and Principal Components Analysis.Genetics · 2019Article
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- Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.Bioinformatics (Oxford, England) · 2017Article
- Population Structure Analysis of Bull Genomes of European and Western Ancestry.Scientific reports · 2017Article
- Recent advances in the study of fine-scale population structure in humans.Current opinion in genetics & development · 2016Review
- Signature of positive selection of PTK6 gene in East Asian populations: a cross talk for Helicobacter pylori invasion and gastric cancer endemicity.Molecular genetics and genomics : MGG · 2015Article
- Testing for genetic associations in arbitrarily structured populations.Nature genetics · 2015Article
- Genome scans for detecting footprints of local adaptation using a Bayesian factor model.Molecular biology and evolution · 2014Article
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3 authors.
Funding
Abstract
motivationModern 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.
resultsWe 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. AVAILABILITY AND IMPLEMENTATION: A Bioconductor R package called lfa is available at http://www.bioconductor.org/packages/release/bioc/html/lfa.html CONTACT: jstorey@princeton.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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