Sparse Principal Component Analysis for Identifying Ancestry-Informative Markers in Genome Wide Association Studies

• Type of Material: Article
• Authors: Seokho Lee, Hankuk University of Foreign Studies; Michael Epstein, Emory University; Richard Duncan, Emory University; Xihong Lin, Harvard University
• Language: English
• Date: 2012-05-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21621
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 36
• Issue: 4
• Start Page: 293
• End Page: 302
• Grant/Funding Information: This work was supported by National Institutes of Health grants R37CA076404 and P01CA134294 for X.L. and S.L. and HG003618 for M.P.E and R.D..; And it was also supported for S.L. by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0011608).
• Keywords: MAP; POPULATION STRATIFICATION; Life Sciences & Biomedicine; Genetics & Heredity; population stratification; ADMIXTURE; Science & Technology; LASSO; SEMIPARAMETRIC TEST; genome-wide association studies; SELECTION; PANEL; principal component analysis; ancestry-informative markers; GENETIC ASSOCIATION; VARIANTS; variable selection; Mathematical & Computational Biology; AMERICANS
• Research Categories: Statistics; Biology, Genetics; Biology, Biostatistics

Abstract

Genome-Wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single-nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of Ancestry-Informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow-up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from Genome-Wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a Genome-Wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of Genome-Wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a Genome-Wide study of inflammatory bowel disease. We have implemented our approach in open-source R software for public use.