期刊
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
卷 9, 期 1, 页码 -出版社
WALTER DE GRUYTER GMBH
DOI: 10.2202/1544-6115.1493
关键词
multidimensional scaling; population genetics; principal components analysis; Procrustes analysis
资金
- NIH [R01 GM081441, T32 GM070449]
- Burroughs Wellcome Fund Career Award in the Biomedical Sciences
- Alfred P. Sloan Research Fellowship
- National Institute on Aging, National Institutes of Health, Department of Health and Human Services [Z01-AG000932-02]
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM081441, T32GM070449] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [Z01AG000932] Funding Source: NIH RePORTER
- MRC [G0701075] Funding Source: UKRI
- Medical Research Council [G0701075] Funding Source: researchfish
- Parkinson"
- s UK [G-0907] Funding Source: researchfish
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.
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