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SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population
Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort t...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765326/ https://www.ncbi.nlm.nih.gov/pubmed/28832569 http://dx.doi.org/10.1038/ejhg.2017.130 |
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author | Ameur, Adam Dahlberg, Johan Olason, Pall Vezzi, Francesco Karlsson, Robert Martin, Marcel Viklund, Johan Kähäri, Andreas Kusalananda Lundin, Pär Che, Huiwen Thutkawkorapin, Jessada Eisfeldt, Jesper Lampa, Samuel Dahlberg, Mats Hagberg, Jonas Jareborg, Niclas Liljedahl, Ulrika Jonasson, Inger Johansson, Åsa Feuk, Lars Lundeberg, Joakim Syvänen, Ann-Christine Lundin, Sverker Nilsson, Daniel Nystedt, Björn Magnusson, Patrik KE Gyllensten, Ulf |
author_facet | Ameur, Adam Dahlberg, Johan Olason, Pall Vezzi, Francesco Karlsson, Robert Martin, Marcel Viklund, Johan Kähäri, Andreas Kusalananda Lundin, Pär Che, Huiwen Thutkawkorapin, Jessada Eisfeldt, Jesper Lampa, Samuel Dahlberg, Mats Hagberg, Jonas Jareborg, Niclas Liljedahl, Ulrika Jonasson, Inger Johansson, Åsa Feuk, Lars Lundeberg, Joakim Syvänen, Ann-Christine Lundin, Sverker Nilsson, Daniel Nystedt, Björn Magnusson, Patrik KE Gyllensten, Ulf |
author_sort | Ameur, Adam |
collection | PubMed |
description | Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set. |
format | Online Article Text |
id | pubmed-5765326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-57653262018-01-12 SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population Ameur, Adam Dahlberg, Johan Olason, Pall Vezzi, Francesco Karlsson, Robert Martin, Marcel Viklund, Johan Kähäri, Andreas Kusalananda Lundin, Pär Che, Huiwen Thutkawkorapin, Jessada Eisfeldt, Jesper Lampa, Samuel Dahlberg, Mats Hagberg, Jonas Jareborg, Niclas Liljedahl, Ulrika Jonasson, Inger Johansson, Åsa Feuk, Lars Lundeberg, Joakim Syvänen, Ann-Christine Lundin, Sverker Nilsson, Daniel Nystedt, Björn Magnusson, Patrik KE Gyllensten, Ulf Eur J Hum Genet Article Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set. Nature Publishing Group 2017-11 2017-08-23 /pmc/articles/PMC5765326/ /pubmed/28832569 http://dx.doi.org/10.1038/ejhg.2017.130 Text en Copyright © 2017 The Author(s) http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Article Ameur, Adam Dahlberg, Johan Olason, Pall Vezzi, Francesco Karlsson, Robert Martin, Marcel Viklund, Johan Kähäri, Andreas Kusalananda Lundin, Pär Che, Huiwen Thutkawkorapin, Jessada Eisfeldt, Jesper Lampa, Samuel Dahlberg, Mats Hagberg, Jonas Jareborg, Niclas Liljedahl, Ulrika Jonasson, Inger Johansson, Åsa Feuk, Lars Lundeberg, Joakim Syvänen, Ann-Christine Lundin, Sverker Nilsson, Daniel Nystedt, Björn Magnusson, Patrik KE Gyllensten, Ulf SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population |
title | SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population |
title_full | SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population |
title_fullStr | SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population |
title_full_unstemmed | SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population |
title_short | SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population |
title_sort | swegen: a whole-genome data resource of genetic variability in a cross-section of the swedish population |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765326/ https://www.ncbi.nlm.nih.gov/pubmed/28832569 http://dx.doi.org/10.1038/ejhg.2017.130 |
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