Graphic analysis of population structure on genome-wide rheumatoid arthritis data.

ABSTRACT: Principal-component analysis (PCA) has been used for decades to summarize the human genetic variation across geographic regions and to infer population migration history. Reduction of spurious associations due to population structure is crucial for the success of disease association studies. Recently, PCA has also become a popular method for detecting population structure and correction of population stratification in disease association studies. Inspired by manifold learning, we propose a novel method based on spectral graph theory. Regarding each study subject as a node with suitably defined weights for its edges to close neighbors, one can form a weighted graph. We suggest using the spectrum of the associated graph Laplacian operator, namely, Laplacian eigenfunctions, to infer populatio

SUBMITTER: Zhang J 

PROVIDER: S-EPMC2795882 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature