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Efficient analysis of large datasets and sex bias with ADMIXTURE
Suyash S Shringarpure1, Carlos D Bustamante2, Kenneth Lange3
1Department of Genetics, Stanford University, Stanford, California, USA. suyashs@stanford.edu.
Background:
A number of large genomic datasets are being generated for studies of human ancestry and diseases. The ADMIXTURE program is commonly used to infer individual ancestry from genomic data.
Results:
We describe two improvements to the ADMIXTURE software. The first enables ADMIXTURE to infer ancestry for a new set of individuals using cluster allele frequencies from a reference set of individuals. Using data from the 1000 Genomes Project, we show that this allows ADMIXTURE to infer ancestry for 10,920 individuals in a few hours (a 5 × speedup). This mode also allows ADMIXTURE to correctly estimate individual ancestry and allele frequencies from a set of related individuals. The second modification allows ADMIXTURE to correctly handle X-chromosome (and other haploid) data from both males and females. We demonstrate increased power to detect sex-biased admixture in African-American individuals from the 1000 Genomes project using this extension.
Conclusions:
These modifications make ADMIXTURE more efficient and versatile, allowing users to extract more information from large genomic datasets.
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