Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis

Principal component (PC) maps, which plot the values of a given PC estimated on the basis of allele frequency variation at the geographic sampling locations of a set of populations, are often used to investigate the properties of past range expansions. Some studies have argued that in a range expans...

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Main Authors:	DeGiorgio, Michael, Rosenberg, Noah A.
Format:	Online
Language:	English
Published:	Oxford University Press 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548310/

id	pubmed-3548310
recordtype	oai_dc
spelling	pubmed-35483102013-01-18 Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis DeGiorgio, Michael Rosenberg, Noah A. Methods Principal component (PC) maps, which plot the values of a given PC estimated on the basis of allele frequency variation at the geographic sampling locations of a set of populations, are often used to investigate the properties of past range expansions. Some studies have argued that in a range expansion, the axis of greatest variation (i.e., the first PC) is parallel to the axis of expansion. In contrast, others have identified a pattern in which the axis of greatest variation is perpendicular to the axis of expansion. Here, we seek to understand this difference in outcomes by investigating the effect of the geographic sampling scheme on the direction of the axis of greatest variation under a two-dimensional range expansion model. From datasets simulated using each of two different schemes for the geographic sampling of populations under the model, we create PC maps for the first PC. We find that depending on the geographic sampling scheme, the axis of greatest variation can be either parallel or perpendicular to the axis of expansion. We provide an explanation for this result in terms of intra- and interpopulation coalescence times. Oxford University Press 2013-02 2012-10-10 /pmc/articles/PMC3548310/ /pubmed/23051843 http://dx.doi.org/10.1093/molbev/mss233 Text en © The Author 2012. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	DeGiorgio, Michael Rosenberg, Noah A.
spellingShingle	DeGiorgio, Michael Rosenberg, Noah A. Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis
author_facet	DeGiorgio, Michael Rosenberg, Noah A.
author_sort	DeGiorgio, Michael
title	Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis
title_short	Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis
title_full	Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis
title_fullStr	Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis
title_full_unstemmed	Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis
title_sort	geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis
description	Principal component (PC) maps, which plot the values of a given PC estimated on the basis of allele frequency variation at the geographic sampling locations of a set of populations, are often used to investigate the properties of past range expansions. Some studies have argued that in a range expansion, the axis of greatest variation (i.e., the first PC) is parallel to the axis of expansion. In contrast, others have identified a pattern in which the axis of greatest variation is perpendicular to the axis of expansion. Here, we seek to understand this difference in outcomes by investigating the effect of the geographic sampling scheme on the direction of the axis of greatest variation under a two-dimensional range expansion model. From datasets simulated using each of two different schemes for the geographic sampling of populations under the model, we create PC maps for the first PC. We find that depending on the geographic sampling scheme, the axis of greatest variation can be either parallel or perpendicular to the axis of expansion. We provide an explanation for this result in terms of intra- and interpopulation coalescence times.
publisher	Oxford University Press
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548310/
_version_	1611947992184520704

Geographic Sampling Scheme as a Determinant of the Major Axis of Genetic Variation in Principal Components Analysis

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