ArticleProceedings of the National Academy of Sciences of the United States of America2015
Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Model-independent searches of new physics in DARWIN with deep learning.The European physical journal. C, Particles and fields · 2026Article
- Coancestry superposed on admixed populations yields measures of relatedness at individual-level resolution.PLoS computational biology · 2025Article
- Standardized Biogeographic Grouping System for Annotating Populations in Pharmacogenetic Research.Clinical pharmacology and therapeutics · 2019Article
- Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.Scientific reports · 2016Article
- Efficient analysis of large datasets and sex bias with ADMIXTURE.BMC bioinformatics · 2016Article
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Authors and funding
3 authors.
Funding
Abstract
Admixture models are a ubiquitous approach to capture latent population structure in genetic samples. Despite the widespread application of admixture models, little thought has been devoted to the quality of the model fit or the accuracy of the estimates of parameters of interest for a particular study. Here we develop methods for validating admixture models based on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of fit of a statistical model to a specific dataset. We develop PPCs for five population-level statistics of interest: within-population genetic variation, background linkage disequilibrium, number of ancestral populations, between-population genetic variation, and the downstream use of admixture parameters to correct for population structure in association studies. Using PPCs, we evaluate the quality of the admixture model fit to four qualitatively different population genetic datasets: the population reference sample (POPRES) European individuals, the HapMap phase 3 individuals, continental Indians, and African American individuals. We found that the same model fitted to different genomic studies resulted in highly study-specific results when evaluated using PPCs, illustrating the utility of PPCs for model-based analyses in large genomic studies.
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