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Comparing spatial maps of human population-genetic variation using Procrustes analysis
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
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2010 (English)In: Statistical Applications in Genetics and Molecular Biology, ISSN 1544-6115, E-ISSN 1544-6115, Vol. 9, no 1, e13- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2010. Vol. 9, no 1, e13- p.
Keyword [en]
multidimensional scaling, population genetics, principal components analysis, Procrustes analysis
National Category
Biological Sciences
URN: urn:nbn:se:uu:diva-136459DOI: 10.2202/1544-6115.1493ISI: 000274198200007PubMedID: 20196748OAI: oai:DiVA.org:uu-136459DiVA: diva2:376932
Available from: 2010-12-13 Created: 2010-12-13 Last updated: 2016-04-26Bibliographically approved

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