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Theor Popul Biol. 2016 Feb;107:65-76. doi: 10.1016/j.tpb.2015.09.004. Epub 2015 Oct 23.

Eigenanalysis of SNP data with an identity by descent interpretation.

Author information

1
Department of Biostatistics, University of Washington, Box 359461, Seattle, WA 98195-9461, USA. Electronic address: zhengx@uw.edu.
2
Department of Biostatistics, University of Washington, Box 359461, Seattle, WA 98195-9461, USA. Electronic address: bsweir@uw.edu.

Abstract

Principal component analysis (PCA) is widely used in genome-wide association studies (GWAS), and the principal component axes often represent perpendicular gradients in geographic space. The explanation of PCA results is of major interest for geneticists to understand fundamental demographic parameters. Here, we provide an interpretation of PCA based on relatedness measures, which are described by the probability that sets of genes are identical-by-descent (IBD). An approximately linear transformation between ancestral proportions (AP) of individuals with multiple ancestries and their projections onto the principal components is found. In addition, a new method of eigenanalysis "EIGMIX" is proposed to estimate individual ancestries. EIGMIX is a method of moments with computational efficiency suitable for millions of SNP data, and it is not subject to the assumption of linkage equilibrium. With the assumptions of multiple ancestries and their surrogate ancestral samples, EIGMIX is able to infer ancestral proportions (APs) of individuals. The methods were applied to the SNP data from the HapMap Phase 3 project and the Human Genome Diversity Panel. The APs of individuals inferred by EIGMIX are consistent with the findings of the program ADMIXTURE. In conclusion, EIGMIX can be used to detect population structure and estimate genome-wide ancestral proportions with a relatively high accuracy.

KEYWORDS:

Admixture; Coancestry; IBD; PCA; Relatedness; SNP

PMID:
26482676
PMCID:
PMC4716003
DOI:
10.1016/j.tpb.2015.09.004
[Indexed for MEDLINE]
Free PMC Article

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