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This article is part of the supplement: Genetic Analysis Workshop 16

Open Access Proceedings

Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling

Dai Wang1*, Yu Sun1, Paul Stang2, Jesse A Berlin2, Marsha A Wilcox2 and Qingqin Li1

Author Affiliations

1 Department of Pharmacogenomics, Johnson & Johnson Pharmaceutical Research and Development, LLC, Raritan, New Jersey 08869 USA

2 Department of Epidemiology, Johnson & Johnson Pharmaceutical Research and Development, LLC, Titusville, New Jersey 08560 USA

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BMC Proceedings 2009, 3(Suppl 7):S109  doi:

Published: 15 December 2009

Abstract

Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in this dataset, principal-component analysis performed slightly better than the multidimensional-scaling method in correcting for PS in genome-wide association analysis of this dataset.