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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.


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1544-6115
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Comparing Spatial Maps of Human Population-Genetic Variation Using Procrustes Analysis

Chaolong Wang1 / Zachary A Szpiech2 / James H Degnan3 / Mattias Jakobsson4 / Trevor J Pemberton5 / John A Hardy6 / Andrew B Singleton7 / Noah A Rosenberg8

1University of Michigan

2University of Michigan

3University of Canterbury

4Uppsala University

5University of Michigan

6University College London

7National Institute on Aging

8University of Michigan

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1493, January 2010

Publication History

Published Online:
2010-01-27

Recent applications of principal components analysis (PCA) and multidimensional scaling (MDS) in human population genetics have found that "statistical maps" based on the genotypes in population-genetic samples often resemble geographic maps of the underlying sampling locations. To provide formal tests of these qualitative observations, we describe a Procrustes analysis approach for quantitatively assessing the similarity of population-genetic and geographic maps. We confirm in two scenarios, one using single-nucleotide polymorphism (SNP) data from Europe and one using SNP data worldwide, that a measurably high level of concordance exists between statistical maps of population-genetic variation and geographic maps of sampling locations. Two other examples illustrate the versatility of the Procrustes approach in population-genetic applications, verifying the concordance of SNP analyses using PCA and MDS, and showing that statistical maps of worldwide copy-number variants (CNVs) accord with statistical maps of SNP variation, especially when CNV analysis is limited to samples with the highest-quality data. As statistical maps with PCA and MDS have become increasingly common for use in summarizing population relationships, our examples highlight the potential of Procrustes-based quantitative comparisons for interpreting the results in these maps.

Keywords: multidimensional scaling; population genetics; principal components analysis; Procrustes analysis

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