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Enhancements to the ADMIXTURE algorithm for individual ancestry estimation

David H Alexander1* and Kenneth Lange123

Author affiliations

1 Department of Biomathematics, UCLA, Los Angeles, California, USA

2 Department of Human Genetics, UCLA Los Angeles, California, USA

3 Department of Statistics, UCLA Los Angeles, California, USA

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Citation and License

BMC Bioinformatics 2011, 12:246  doi:10.1186/1471-2105-12-246

Published: 18 June 2011

Abstract

Background

The 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.

Results

Here 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.

Conclusions

The enhancements we have described make ADMIXTURE a more accurate, efficient, and versatile tool for ancestry estimation.