Fine population structure analysis method for genomes of many

Abstract Fine population structure can be examined through the clustering of individuals into subpopulations. The clustering of individuals in large sequence datasets into subpopulations makes the calculation of subpopulation specific allele frequency possible, which may shed light on selection of c...

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Autores principales:	Xuedong Pan, Yi Wang, Emily H. M. Wong, Amalio Telenti, J. Craig Venter, Li Jin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c200df0cc40b4ab69b6fd5028613b674
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spelling	oai:doaj.org-article:c200df0cc40b4ab69b6fd5028613b6742021-12-02T15:06:04ZFine population structure analysis method for genomes of many10.1038/s41598-017-12319-12045-2322https://doaj.org/article/c200df0cc40b4ab69b6fd5028613b6742017-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-12319-1https://doaj.org/toc/2045-2322Abstract Fine population structure can be examined through the clustering of individuals into subpopulations. The clustering of individuals in large sequence datasets into subpopulations makes the calculation of subpopulation specific allele frequency possible, which may shed light on selection of candidate variants for rare diseases. However, as the magnitude of the data increases, computational burden becomes a challenge in fine population structure analysis. To address this issue, we propose fine population structure analysis (FIPSA), which is an individual-based non-parametric method for dissecting fine population structure. FIPSA maximizes the likelihood ratio of the contingency table of the allele counts multiplied by the group. We demonstrated that its speed and accuracy were superior to existing non-parametric methods when the simulated sample size was up to 5,000 individuals. When applied to real data, the method showed high resolution on the Human Genome Diversity Project (HGDP) East Asian dataset. FIPSA was independently validated on 11,257 human genomes. The group assignment given by FIPSA was 99.1% similar to those assigned based on supervised learning. Thus, FIPSA provides high resolution and is compatible with a real dataset of more than ten thousand individuals.Xuedong PanYi WangEmily H. M. WongAmalio TelentiJ. Craig VenterLi JinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuedong Pan Yi Wang Emily H. M. Wong Amalio Telenti J. Craig Venter Li Jin Fine population structure analysis method for genomes of many
description	Abstract Fine population structure can be examined through the clustering of individuals into subpopulations. The clustering of individuals in large sequence datasets into subpopulations makes the calculation of subpopulation specific allele frequency possible, which may shed light on selection of candidate variants for rare diseases. However, as the magnitude of the data increases, computational burden becomes a challenge in fine population structure analysis. To address this issue, we propose fine population structure analysis (FIPSA), which is an individual-based non-parametric method for dissecting fine population structure. FIPSA maximizes the likelihood ratio of the contingency table of the allele counts multiplied by the group. We demonstrated that its speed and accuracy were superior to existing non-parametric methods when the simulated sample size was up to 5,000 individuals. When applied to real data, the method showed high resolution on the Human Genome Diversity Project (HGDP) East Asian dataset. FIPSA was independently validated on 11,257 human genomes. The group assignment given by FIPSA was 99.1% similar to those assigned based on supervised learning. Thus, FIPSA provides high resolution and is compatible with a real dataset of more than ten thousand individuals.
format	article
author	Xuedong Pan Yi Wang Emily H. M. Wong Amalio Telenti J. Craig Venter Li Jin
author_facet	Xuedong Pan Yi Wang Emily H. M. Wong Amalio Telenti J. Craig Venter Li Jin
author_sort	Xuedong Pan
title	Fine population structure analysis method for genomes of many
title_short	Fine population structure analysis method for genomes of many
title_full	Fine population structure analysis method for genomes of many
title_fullStr	Fine population structure analysis method for genomes of many
title_full_unstemmed	Fine population structure analysis method for genomes of many
title_sort	fine population structure analysis method for genomes of many
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c200df0cc40b4ab69b6fd5028613b674
work_keys_str_mv	AT xuedongpan finepopulationstructureanalysismethodforgenomesofmany AT yiwang finepopulationstructureanalysismethodforgenomesofmany AT emilyhmwong finepopulationstructureanalysismethodforgenomesofmany AT amaliotelenti finepopulationstructureanalysismethodforgenomesofmany AT jcraigventer finepopulationstructureanalysismethodforgenomesofmany AT lijin finepopulationstructureanalysismethodforgenomesofmany
_version_	1718388608153419776

Fine population structure analysis method for genomes of many

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