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Author Notes:

Xihong Lin, Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, Email: xlin@hsph.harvard.edu

Subjects:

Research Funding:

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:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Genetics & Heredity
  • Mathematical & Computational Biology
  • ancestry-informative markers
  • genome-wide association studies
  • population stratification
  • principal component analysis
  • variable selection
  • POPULATION STRATIFICATION
  • GENETIC ASSOCIATION
  • SEMIPARAMETRIC TEST
  • ADMIXTURE
  • AMERICANS
  • SELECTION
  • VARIANTS
  • LASSO
  • PANEL
  • MAP

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

Tools:

Journal Title:

Genetic Epidemiology

Volume:

Volume 36, Number 4

Publisher:

, Pages 293-302

Type of Work:

Article | Post-print: After Peer Review

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.

Copyright information:

© 2012 Wiley Periodicals, Inc.

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