Fast model-based estimation of ancestry in unrelated individuals.

By David H. Alexander, John Novembre, Kenneth L. Lange. Genome Research, 2009

8,750 citations recorded by the source corpus.

Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/20f6dda28b380eabb9c7bbb94063437dc735c63b

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Citations in the corpus

Inference of Population Structure using Dense Haplotype Data (2012, methodology): “Although powerful, these approaches have drawbacks – determination of K is difficult despite some technical advances [18,19], and typically Kv10 is required for satisfactory convergence, due to issues of computational cost and the presence of distinct local optima, affecting even the fastest methods”
Clumpak: a program for identifying clustering modes and packaging population structure inferences across K (2015, methodology): “…et al. 2005; Franc (cid:1) ois et al. 2006; Pella & Masuda 2006; Chen et al. 2007; Gao et al. 2007; Huelsen-beck & Andolfatto 2007; Guillot 2008; Alexander et al. 2009; Durand et al. 2009; Hubisz et al. 2009; Shringarpure used of which is S TRUCTURE (Pritchard et al. 2000; Falush et al. 2003,…”
1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana (2016, methodology): “Moreover, while relicts show strong IBD on all geographic scales, non-relicts have a similarly clear pattern only over short distances (Figure 3D), as expected if they had spread rapidly to occupy their (A) Coalescence rates over time for pairs of individuals from different ADMIXTURE groups, inferre”
pophelper: an R package and web app to analyse and visualize population structure (2017, methodology): “Some of the soft-wares discussed in this article includes ADMIXTURE (Alexander et al. 2009), FASTSTRUCTURE (Raj et al. 2014), STRUCTURE (Pritchard et al. 2000) and TESS (Franc (cid:1) ois & Durand 2010).”
Fast hierarchical Bayesian analysis of population structure (2018, methodology): “Solutions to this problem include STRUCTURE (4,7), BAPS (8–10), Ad-mixture (11), fast Structure (12), sNMF (13) and the method of Anderson and Thompson (14).”
Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, methodology): “2 (Alexander et al. 2009) was used on the pruned data sets for cluster analysis and the cross validation (CV) error for identifying the best K value.”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “The population structure can be previously investigated using specific tools like ADMIXTURE (Alexander et al. 2009), STRUCTURE (Pritchard et al. 2000), Localdiff (Duforet-Frebourg and Blum 2014), or a principal component analysis (PCA, Patterson et al. 2006).”