SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population.
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Ameur A, Dahlberg J, Olason P, Vezzi F, Karlsson R, Martin M, Viklund J, Kähäri AK, Lundin P, Che H, Thutkawkorapin J, Eisfeldt J, Lampa S, Dahlberg M, Hagberg J, Jareborg N, Liljedahl U, Jonasson I, Johansson Å, Feuk L, Lundeberg J, Syvänen AC, Lundin S, Nilsson D, Nystedt B, Magnusson PK, Gyllensten U
Eur. J. Hum. Genet. 25 (11) 1253-1260 [2017-11-00; online 2017-08-23]
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.
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Bioinformatics Support and Infrastructure [Collaborative]
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Bioinformatics Support, Infrastructure and Training [Collaborative]
NGI Stockholm (Genomics Applications) [Service]
NGI Stockholm (Genomics Production) [Service]
NGI Uppsala (SNP&SEQ Technology Platform) [Service]
NGI Uppsala (Uppsala Genome Center) [Collaborative]
National Genomics Infrastructure [Service]
PubMed 28832569
DOI 10.1038/ejhg.2017.130
Crossref 10.1038/ejhg.2017.130
pii: ejhg2017130
pmc: PMC5765326