Evidence map›Paper›PMID 28832569›Full record

ArticleEuropean journal of human genetics : EJHG2017

SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population.

Adam Ameur, Johan Dahlberg, Pall Olason, Francesco Vezzi, Robert Karlsson, Marcel Martin, Johan Viklund, Andreas Kusalananda Kähäri, Pär Lundin, Huiwen Che and 17 more

Abstract readTwin Study
In one paragraph

Article in European journal of human genetics : EJHG, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
120citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

120 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Observational
  6. Frequency of ZFHX3-Mediated Spinocerebellar Ataxia 4 in a US Undiagnosed Ataxia Cohort.Movement disorders : official journal of the Movement Disorder Society · 2026
    Article
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  8. Review
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  10. Article
  11. Fifty Shades of Risk: Population Studies and the Genetic Architecture of Kidney Diseases.Journal of the American Society of Nephrology : JASN · 2026
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Egypt Genome: Towards an African new genomic era.Journal of advanced research · 2025
    Review
  17. Review
  18. Article
  19. Article
  20. Article

60 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Adam AmeurScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Johan DahlbergNational Genomics Infrastructure, Science for Life Laboratory, Sweden.
Pall OlasonScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Francesco VezziNational Genomics Infrastructure, Science for Life Laboratory, Sweden.
Robert KarlssonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-8949-2587
Marcel MartinNational Bioinformatics Infrastructure, Science for Life Laboratory, Sweden.
Johan ViklundScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Andreas Kusalananda KähäriScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Pär LundinScience for Life Laboratory, Department of Biochemistry and Biophysics (DBB), Stockholm University, Stockholm, Sweden.
Huiwen CheScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Jessada ThutkawkorapinDepartment of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Jesper EisfeldtDepartment of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Samuel LampaNational Bioinformatics Infrastructure, Science for Life Laboratory, Sweden.
Mats DahlbergNational Bioinformatics Infrastructure, Science for Life Laboratory, Sweden.
Jonas HagbergNational Bioinformatics Infrastructure, Science for Life Laboratory, Sweden.ORCID 0000-0003-2370-6025
Niclas JareborgNational Bioinformatics Infrastructure, Science for Life Laboratory, Sweden.ORCID 0000-0002-4520-044X
Ulrika LiljedahlNational Genomics Infrastructure, Science for Life Laboratory, Sweden.
Inger JonassonScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Åsa JohanssonScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Lars FeukScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.ORCID 0000-0003-2355-2919
Joakim LundebergNational Genomics Infrastructure, Science for Life Laboratory, Sweden.
Ann-Christine SyvänenNational Genomics Infrastructure, Science for Life Laboratory, Sweden.
Sverker LundinScience for Life Laboratory, School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden.
Daniel NilssonDepartment of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Björn NystedtScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.
Patrik Ke MagnussonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Ulf GyllenstenScience for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Genome, HumanPolymorphism, Single NucleotideRegistriesDatasets as TopicGenome-Wide Association StudyHumansSwedenTwins

Identifiers

PMID28832569
PMCPMC5765326

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.