Enhancements to the ADMIXTURE algorithm for individual ancestry estimation

By David H. Alexander, Kenneth L. Lange. BMC Bioinformatics, 2011

1,369 citations recorded by the source corpus.

BackgroundThe estimation of individual ancestry from genetic data has become essential to applied population genetics and genetic epidemiology. Software programs for calculating ancestry estimates have become essential tools in the geneticist's analytic arsenal.ResultsHere we describe four enhancements to ADMIXTURE, a high-performance tool for estimating individual ancestries and population allele frequencies from SNP (single nucleotide polymorphism) data. First, ADMIXTURE can be used to estimate the number of underlying populations through cross-validation. Second, individuals of known ancestry can be exploited in supervised learning to yield more precise ancestry estimates. Third, by penalizing small admixture coefficients for each individual, one can encourage model parsimony, often yielding more interpretable results for small datasets or datasets with large numbers of ancestral populations. Finally, by exploiting multiple processors, large datasets can be analyzed even more rapidly.ConclusionsThe enhancements we have described make ADMIXTURE a more accurate, efficient, and versatile tool for ancestry estimation.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/7e9c8caa9229a661d926cfd30d25ea543c776bf6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Olivier Francois
2. Éric Frichot
3. P. Meirmans

Citations in the corpus

• LEA: An R package for landscape and ecological association studies (2015, background): “The entropy criterion can help choosing the number of ancestral populations that best explains the genotypic data (Alexander and Lange, 2011; Frichot et al., 2014).”