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

Wang Chaolong, Szpiech Zachary A, Degnan James H, Jakobsson Mattias, Pemberton Trevor J, Hardy John A, Singleton Andrew B and Rosenberg Noah A
Additional contact information
Wang Chaolong: University of Michigan
Szpiech Zachary A: University of Michigan
Degnan James H: University of Canterbury
Jakobsson Mattias: Uppsala University
Pemberton Trevor J: University of Michigan
Hardy John A: University College London
Singleton Andrew B: National Institute on Aging
Rosenberg Noah A: University of Michigan

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 22

Abstract: 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 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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DOI: 10.2202/1544-6115.1493

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