Efficient analysis of large datasets and sex bias with ADMIXTURE

被引：37

作者：

Shringarpure, Suyash S. ^{[1
]}

Bustamante, Carlos D. ^{[1
]}

Lange, Kenneth ^{[2
]}

Alexander, David H. ^{[3
]}

机构：

[1] Stanford Univ, Dept Genet, Stanford, CA 94305 USA

[2] Univ Calif Los Angeles, Dept Biomath, Los Angeles, CA USA

[3] Pacific Biosci, Menlo Pk, CA USA

来源：

BMC BIOINFORMATICS | 2016年 / 17卷

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

Supervised learning; Reference panels; Pedigrees; Sex-chromosome; Sex bias; Ancestry inference; Admixture; POPULATION-STRUCTURE; AFRICAN-AMERICANS; ANCESTRY; INFERENCE; PATTERNS;

D O I：

10.1186/s12859-016-1082-x

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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 5x 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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