Eigenanalysis on SNP Data with an Interpretation of Identity by Descent

• Activity Number: 123
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Xiuwen Zheng*+ and Bruce Spencer Weir
• Companies: University of Washington and University of Washington
• Keywords: PCA ; Relatedness ; Coancestry ; IBD ; SNP ; Admixture

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. However, PCA is a model-free approach and its behavior is still of great 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 revealed.

A method of eigenanalysis EIGMIX is proposed to deliver ancestry information with an adjustment compared to PCA. The AP can be estimated by making assumptions of surrogate ancestral samples. The methods were applied to the SNP data from the HapMap Phase 3 project and HGDP project. The AP of individuals inferred by PCA and EIGMIX are consistent with the findings of ADMIXTURE, and EIGMIX is observed to be more robust than PCA.