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1.  Graphic analysis of population structure on genome-wide rheumatoid arthritis data 
BMC Proceedings  2009;3(Suppl 7):S110.
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 population structures instead of principal components (PCs). For the whole genome-wide association data for the North American Rheumatoid Arthritis Consortium (NARAC) provided by Genetic Workshop Analysis 16, Laplacian eigenfunctions revealed more meaningful structures of the underlying population than PCA. The proposed method has connection to PCA, and it naturally includes PCA as a special case. Our simple method is computationally fast and is suitable for disease studies at the genome-wide scale.
PMCID: PMC2795882  PMID: 20017975
2.  An integrated genome-wide association analysis on rheumatoid arthritis data 
BMC Proceedings  2007;1(Suppl 1):S35.
We propose a nonparametric association analysis combining both family and unrelated case-control genotype data. Under the assumption of Hardy-Weinberg equilibrium, we formed an affected group to compare with a group of unaffecteds.
Comparison with traditional case-control chi-square test and transmission-disequilibrium test shows that this new approach has noticeably improved power. All analysis was based on the simulated rheumatoid arthritis data provided by Genetic Analysis Workshop 15. In the situation of population stratification, we also suggest an approach to update the genotype data using principal components. However, the Genetic Analysis Workshop 15 simulation data does not simulate population stratification. All analysis was done without knowledge of the answers.
PMCID: PMC2367523  PMID: 18466533

Results 1-2 (2)