Population Structure and Eigenanalysis

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleague...

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Veröffentlicht in:PLoS genetics Jg. 2; H. 12; S. e190
Hauptverfasser: Patterson, Nick, Price, Alkes L., Reich, David
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.12.2006
Public Library of Science (PLoS)
Schlagworte:
Computer Simulation - statistics & numerical data
Eigenfunctions
Eukaryotes
Genetic aspects
Genetic Markers
Genetic Variation
Genetics
Genetics and Genomics
Genetics, Medical - methods
Genetics, Medical - statistics & numerical data
Genetics, Population - methods
Genetics, Population - statistics & numerical data
Humans
Methods
Models, Genetic
Models, Statistical
Phylogenetics
Population genetics
Population research
Principal Component Analysis - methods
Statistics
Theory
ISSN:1553-7390, 1553-7404, 1553-7404
Online-Zugang:Volltext
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Zusammenfassung:Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general "phase change" phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure.
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ISSN:1553-7390
1553-7404
1553-7404
DOI:10.1371/journal.pgen.0020190