GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix t...

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Autores principales:	Oscar Lao, Fan Liu, Andreas Wollstein, Manfred Kayser
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f8d8052c504e41e3be93592990823197
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spelling	oai:doaj.org-article:f8d8052c504e41e3be935929908231972021-11-18T05:53:08ZGAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.1553-734X1553-735810.1371/journal.pcbi.1003480https://doaj.org/article/f8d8052c504e41e3be935929908231972014-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586132/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.Oscar LaoFan LiuAndreas WollsteinManfred KayserPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 2, p e1003480 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Oscar Lao Fan Liu Andreas Wollstein Manfred Kayser GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.
description	Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics.
format	article
author	Oscar Lao Fan Liu Andreas Wollstein Manfred Kayser
author_facet	Oscar Lao Fan Liu Andreas Wollstein Manfred Kayser
author_sort	Oscar Lao
title	GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.
title_short	GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.
title_full	GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.
title_fullStr	GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.
title_full_unstemmed	GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.
title_sort	gaga: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in europeans.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/f8d8052c504e41e3be93592990823197
work_keys_str_mv	AT oscarlao gagaanewalgorithmforgenomicinferenceofgeographicancestryrevealsfinelevelpopulationsubstructureineuropeans AT fanliu gagaanewalgorithmforgenomicinferenceofgeographicancestryrevealsfinelevelpopulationsubstructureineuropeans AT andreaswollstein gagaanewalgorithmforgenomicinferenceofgeographicancestryrevealsfinelevelpopulationsubstructureineuropeans AT manfredkayser gagaanewalgorithmforgenomicinferenceofgeographicancestryrevealsfinelevelpopulationsubstructureineuropeans
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GAGA: a new algorithm for genomic inference of geographic ancestry reveals fine level population substructure in Europeans.

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