Efficient analysis of large datasets and sex bias with ADMIXTURE

• Published in: BMC bioinformatics Vol. 17; no. 1; p. 218
• Main Authors: Shringarpure, Suyash S., Bustamante, Carlos D., Lange, Kenneth, Alexander, David H.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.05.2016; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Black or African American - genetics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Female; Gene Frequency; Genetics, Population; Genomics; Genomics - methods; HapMap Project; Humans; Life Sciences; Male; Microarrays; Physiological aspects; Sequence analysis (methods); Sex chromosomes; Software; Southwestern United States; United States; Ancestry inference; Supervised learning; Admixture; Reference panels; Pedigrees; Sex-chromosome; Sex bias
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-016-1082-x

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.