Enhanced localization of genetic samples through linkage-disequilibrium correction

Characterizing the spatial patterns of genetic diversity in human populations has a wide range of applications, from detecting genetic mutations associated with disease to inferring human history. Current approaches, including the widely used principal-component analysis, are not suited for the anal...

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Detalles Bibliográficos
Autores: Baran, Y., Quintela García, Inés, Carracedo Álvarez, Ángel, Pasaniuc, B., Halperin, E.
Tipo de recurso: artículo
Fecha de publicación:2013
País:España
Institución:Servizo Galego de Saúde (SERGAS)
Repositorio:RUNA. Repositorio da Consellería de Sanidade e Sergas
OAI Identifier:oai:runa.sergas.gal:20.500.11940/2432
Acceso en línea:http://hdl.handle.net/20.500.11940/2432
Access Level:acceso abierto
Palabra clave:Algorithms
Genetic Markers
Genetics, Population
Genome, Human
Humans
Linkage Disequilibrium
Models, Genetic
Phylogeography
Polymorphism, Single Nucleotide
Principal Component Analysis
Software
Spain
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oai_identifier_str oai:runa.sergas.gal:20.500.11940/2432
network_acronym_str ES
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repository_id_str
spelling Enhanced localization of genetic samples through linkage-disequilibrium correctionBaran, Y.Quintela García, InésCarracedo Álvarez, ÁngelPasaniuc, B.Halperin, E.AlgorithmsGenetic MarkersGenetics, PopulationGenome, HumanHumansLinkage DisequilibriumModels, GeneticPhylogeographyPolymorphism, Single NucleotidePrincipal Component AnalysisSoftwareSpainCharacterizing the spatial patterns of genetic diversity in human populations has a wide range of applications, from detecting genetic mutations associated with disease to inferring human history. Current approaches, including the widely used principal-component analysis, are not suited for the analysis of linked markers, and local and long-range linkage disequilibrium (LD) can dramatically reduce the accuracy of spatial localization when unaccounted for. To overcome this, we have introduced an approach that performs spatial localization of individuals on the basis of their genetic data and explicitly models LD among markers by using a multivariate normal distribution. By leveraging external reference panels, we derive closed-form solutions to the optimization procedure to achieve a computationally efficient method that can handle large data sets. We validate the method on empirical data from a large sample of European individuals from the POPRES data set, as well as on a large sample of individuals of Spanish ancestry. First, we show that by modeling LD, we achieve accuracy superior to that of existing methods. Importantly, whereas other methods show decreased performance when dense marker panels are used in the inference, our approach improves in accuracy as more markers become available. Second, we show that accurate localization of genetic data can be achieved with only a part of the genome, and this could potentially enable the spatial localization of admixed samples that have a fraction of their genome originating from a given continent. Finally, we demonstrate that our approach is resistant to distortions resulting from long-range LD regions; such distortions can dramatically bias the results when unaccounted for.2013info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.11940/2432reponame:RUNA. Repositorio da Consellería de Sanidade e Sergasinstname:Servizo Galego de Saúde (SERGAS)Inglésinfo:eu-repo/semantics/openAccessoai:runa.sergas.gal:20.500.11940/24322026-06-12T08:40:47Z
dc.title.none.fl_str_mv Enhanced localization of genetic samples through linkage-disequilibrium correction
title Enhanced localization of genetic samples through linkage-disequilibrium correction
spellingShingle Enhanced localization of genetic samples through linkage-disequilibrium correction
Baran, Y.
Algorithms
Genetic Markers
Genetics, Population
Genome, Human
Humans
Linkage Disequilibrium
Models, Genetic
Phylogeography
Polymorphism, Single Nucleotide
Principal Component Analysis
Software
Spain
title_short Enhanced localization of genetic samples through linkage-disequilibrium correction
title_full Enhanced localization of genetic samples through linkage-disequilibrium correction
title_fullStr Enhanced localization of genetic samples through linkage-disequilibrium correction
title_full_unstemmed Enhanced localization of genetic samples through linkage-disequilibrium correction
title_sort Enhanced localization of genetic samples through linkage-disequilibrium correction
dc.creator.none.fl_str_mv Baran, Y.
Quintela García, Inés
Carracedo Álvarez, Ángel
Pasaniuc, B.
Halperin, E.
author Baran, Y.
author_facet Baran, Y.
Quintela García, Inés
Carracedo Álvarez, Ángel
Pasaniuc, B.
Halperin, E.
author_role author
author2 Quintela García, Inés
Carracedo Álvarez, Ángel
Pasaniuc, B.
Halperin, E.
author2_role author
author
author
author
dc.subject.none.fl_str_mv Algorithms
Genetic Markers
Genetics, Population
Genome, Human
Humans
Linkage Disequilibrium
Models, Genetic
Phylogeography
Polymorphism, Single Nucleotide
Principal Component Analysis
Software
Spain
topic Algorithms
Genetic Markers
Genetics, Population
Genome, Human
Humans
Linkage Disequilibrium
Models, Genetic
Phylogeography
Polymorphism, Single Nucleotide
Principal Component Analysis
Software
Spain
description Characterizing the spatial patterns of genetic diversity in human populations has a wide range of applications, from detecting genetic mutations associated with disease to inferring human history. Current approaches, including the widely used principal-component analysis, are not suited for the analysis of linked markers, and local and long-range linkage disequilibrium (LD) can dramatically reduce the accuracy of spatial localization when unaccounted for. To overcome this, we have introduced an approach that performs spatial localization of individuals on the basis of their genetic data and explicitly models LD among markers by using a multivariate normal distribution. By leveraging external reference panels, we derive closed-form solutions to the optimization procedure to achieve a computationally efficient method that can handle large data sets. We validate the method on empirical data from a large sample of European individuals from the POPRES data set, as well as on a large sample of individuals of Spanish ancestry. First, we show that by modeling LD, we achieve accuracy superior to that of existing methods. Importantly, whereas other methods show decreased performance when dense marker panels are used in the inference, our approach improves in accuracy as more markers become available. Second, we show that accurate localization of genetic data can be achieved with only a part of the genome, and this could potentially enable the spatial localization of admixed samples that have a fraction of their genome originating from a given continent. Finally, we demonstrate that our approach is resistant to distortions resulting from long-range LD regions; such distortions can dramatically bias the results when unaccounted for.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11940/2432
url http://hdl.handle.net/20.500.11940/2432
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RUNA. Repositorio da Consellería de Sanidade e Sergas
instname:Servizo Galego de Saúde (SERGAS)
instname_str Servizo Galego de Saúde (SERGAS)
reponame_str RUNA. Repositorio da Consellería de Sanidade e Sergas
collection RUNA. Repositorio da Consellería de Sanidade e Sergas
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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