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SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden..ORCID iD: 0000-0001-6085-6749
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Natl Bioinformat Infrastruct, Sci Life Lab, Stockholm, Sweden..
Natl Genom Infrastruct, Sci Life Lab, Stockholm, Sweden.;Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
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2017 (English)In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 25, no 11, p. 1253-1260Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2017. Vol. 25, no 11, p. 1253-1260
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-337314DOI: 10.1038/ejhg.2017.130ISI: 000412823800012PubMedID: 28832569OAI: oai:DiVA.org:uu-337314DiVA, id: diva2:1171782
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceKnut and Alice Wallenberg Foundation, 2014.0272Swedish Research CouncilSwedish National Infrastructure for Computing (SNIC), sens2016003EU, European Research Council, 282330Available from: 2018-01-08 Created: 2018-01-08 Last updated: 2018-08-27Bibliographically approved
In thesis
1. Genetic Cartography at Massively Parallel Scale
Open this publication in new window or tab >>Genetic Cartography at Massively Parallel Scale
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Massively parallel sequencing (MPS) is revolutionizing genomics. In this work we use, refine, and develop new tools for the discipline.

MPS has led to the discovery of multiple novel subtypes in Acute Lymphoblastic Leukemia (ALL). In Study I we screen for fusion genes in 134 pediatric ALL patients, including patients without an assigned subtype. In approximately 80% of these patients we detect novel or known fusion gene families, most of which display distinct methylation and expression patterns. This shows the potential for improvements in the clinical stratification of ALL. Large sample sizes are important to detect recurrent somatic variation. In Study II we investigate if a non-index overlapping pooling schema can be used to increase sample size and detect somatic variation. We designed a schema for 172 ALL samples and show that it is possible to use this method to call somatic variants.

Around the globe there are many ongoing and completed genome projects. In Study III we sequenced the genome of 1000 Swedes to create a reference data set for the Swedish population. We identified more than 10 million variants that were not present in publicly available databases, highlighting the need for population-specific resources. Data, and the tools developed during this study, have been made publicly available as a resource for genomics in Sweden and abroad.

The increased amount of sequencing data has created a greater need for automation. In Study IV we present Arteria, a computational automation system for sequencing core facilities. This system has been adopted by multiple facilities and has been used to analyze thousands of samples. In Study V we developed CheckQC, a program that provides automated quality control of Illumina sequencing runs. These tools make scaling up MPS less labour intensive, a key to unlocking the full future potential of genomics.

The tools, and data presented here are a valuable contribution to the scientific community. Collectively they showcase the power of MPS and genomics to bring about new knowledge of human health and disease.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 68
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1492
Keywords
Acute Lymphoblastic Leukemia (ALL), RNA-Sequencing, Bioinformatics, Pooling, Whole Genome Sequencing
National Category
Medical Genetics Cancer and Oncology Hematology Computer Systems Bioinformatics (Computational Biology)
Research subject
Medical Genetics; Bioinformatics
Identifiers
urn:nbn:se:uu:diva-358289 (URN)978-91-513-0428-1 (ISBN)
Public defence
2018-10-19, E10:1307-1309 (Trippelrummet), Navet, Biomedicinskt centrum, Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2018-09-20 Created: 2018-08-27 Last updated: 2018-10-02

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Ameur, AdamDahlberg, JohanViklund, JohanKähäri, AndreasLampa, SamuelLiljedahl, UlrikaJonasson, IngerJohansson, ÅsaFeuk, LarsSyvänen, Ann-ChristineNystedt, BjörnGyllensten, Ulf B.
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