4.0 Article

Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations

Journal

THEORETICAL POPULATION BIOLOGY
Volume 89, Issue -, Pages 34-43

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.tpb.2013.08.004

Keywords

Principal components analysis; Eigenanalysis; Population structure; Eigenvalues; Number of subpopulations

Funding

  1. National Institutes of Health under Ruth L. Kirschstein National Research Service Award [5F32HG006411]
  2. NSF [DMS-0904720]

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We present a mathematical model, and the corresponding mathematical analysis, that justifies and quantifies the use of principal component analysis of biallelic genetic marker data for a set of individuals to detect the number of subpopulations represented in the data. We indicate that the power of the technique relies more on the number of individuals genotyped than on the number of markers. (C) 2013 Elsevier Inc. All rights reserved.

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