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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Published in:	PLoS genetics Vol. 2; no. 12; p. e190
Main Authors:	Patterson, Nick, Price, Alkes L., Reich, David
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 01.12.2006 Public Library of Science (PLoS)
Subjects:	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 Access:	Get full text
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Abstract	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.
AbstractList	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. When analyzing genetic data, one often wishes to determine if the samples are from a population that has structure. Can the samples be regarded as randomly chosen from a homogeneous population, or does the data imply that the population is not genetically homogeneous? Patterson, Price, and Reich show that an old method (principal components) together with modern statistics (Tracy–Widom theory) can be combined to yield a fast and effective answer to this question. The technique is simple and practical on the largest datasets, and can be applied both to genetic markers that are biallelic and to markers that are highly polymorphic such as microsatellites. The theory also allows the authors to estimate the data size needed to detect structure if their samples are in fact from two populations that have a given, but small level of differentiation. 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. 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.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. 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. 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 F sub(ST)) 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.
Audience	Academic
Author	Price, Alkes L. Patterson, Nick Reich, David
AuthorAffiliation	1 Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America 2 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America University of Alabama at Birmingham, United States of America
AuthorAffiliation_xml	– name: 2 Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America – name: 1 Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America – name: University of Alabama at Birmingham, United States of America
Author_xml	– sequence: 1 givenname: Nick surname: Patterson fullname: Patterson, Nick – sequence: 2 givenname: Alkes L. surname: Price fullname: Price, Alkes L. – sequence: 3 givenname: David surname: Reich fullname: Reich, David
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/17194218$$D View this record in MEDLINE/PubMed
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SubjectTerms	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
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Title	Population Structure and Eigenanalysis
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