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Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits

Nature Genetics volume 53, pages 779–786 (2021)Cite this article

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Abstract

Long-read sequencing (LRS) promises to improve the characterization of structural variants (SVs). We generated LRS data from 3,622 Icelanders and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions). We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association of a rare deletion in PCSK9 with lower low-density lipoprotein (LDL) cholesterol levels, compared to the population average. We also discovered an association of a multiallelic SV in ACAN with height; we found 11 alleles that differed in the number of a 57-bp-motif repeat and observed a linear relationship between the number of repeats carried and height. These results show that SVs can be accurately characterized at the population scale using LRS data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

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Fig. 1: SV analysis workflow.
Fig. 2: Merged SV set characteristics.
Fig. 3: Large deletion in PCSK9 associated with lower LDL cholesterol levels.
Fig. 4: Multiallelic SVs in repeat regions within exons of ACAN, NACA and PRDM9, difficult for SV detection using SRS.

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Data availability

Access to these data is controlled; the sequence data cannot be made publicly available because Icelandic law and the regulations of the Icelandic Data Protection Authority prohibit the release of individual-level and personally identifying data. Data access can be granted only at the facilities of deCODE genetics in Iceland, subject to Icelandic law regarding data usage. Anyone wishing to gain access to the data should contact K.S. (kstefans@decode.is). Icelandic law allows for unimpeded sharing of summary-level data. Data access consists of Supplementary Data 1–5 as described below, alongside the VCF and index files for the high-confidence SV alleles at https://github.com/DecodeGenetics/LRS_SV_sets.

Code availability

Codes are available as follows: SViper, modified, used in this study (https://github.com/DecodeGenetics/SViper/tree/cornercases); SViper, original repository (https://github.com/smehringer/SViper); Scrappie, modified, used in this study (https://github.com/DecodeGenetics/scrappie/tree/v1.3.0.events); Scrappie, original repository (https://github.com/nanoporetech/scrappie); SquiggleSVFilter (https://github.com/DecodeGenetics/nanopolish/tree/squigglesv); sample execution of SquiggleSVFilter with input and expected output data (https://github.com/DecodeGenetics/SquiggleSV_samplerun); sv-merger, to form SV cliques using the Cluster Affinity Search Technique algorithm (https://github.com/DecodeGenetics/sv-merger); LRcaller (https://github.com/DecodeGenetics/LRcaller).

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Acknowledgements

We thank J. Simpson, our colleagues from deCODE genetics/Amgen Inc. and employees of ONT for their help and advice. We also thank all research participants who provided a biological sample to deCODE genetics.

Author information

Authors and Affiliations

  1. deCODE genetics/Amgen, Inc., Reykjavik, Iceland

    Doruk Beyter, Helga Ingimundardottir, Asmundur Oddsson, Hannes P. Eggertsson, Eythor Bjornsson, Hakon Jonsson, Bjarni A. Atlason, Snaedis Kristmundsdottir, Marteinn T. Hardarson, Sigurjon A. Gudjonsson, Droplaug N. Magnusdottir, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Ragnar P. Kristjansson, Sverrir T. Sverrisson, Guillaume Holley, Gunnar Palsson, Olafur A. Stefansson, Gisli Masson, Agnar Helgason, Unnur Thorsteinsdottir, Hilma Holm, Daniel F. Gudbjartsson, Patrick Sulem, Olafur T. Magnusson, Bjarni V. Halldorsson & Kari Stefansson

  2. School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland

    Hannes P. Eggertsson & Daniel F. Gudbjartsson

  3. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    Eythor Bjornsson, Unnur Thorsteinsdottir & Kari Stefansson

  4. Department of Internal Medicine, Landspitali—the National University Hospital of Iceland, Reykjavik, Iceland

    Eythor Bjornsson

  5. School of Technology, Reykjavik University, Reykjavik, Iceland

    Snaedis Kristmundsdottir & Bjarni V. Halldorsson

  6. Algorithmic Bioinformatics (ABI), Freie Universitat Berlin, Berlin, Germany

    Svenja Mehringer

  7. The Laboratory in Mjodd, Reykjavik, Iceland

    Gudmundur Eyjolfsson

  8. Department of Clinical Biochemistry, Landspitali—the National University Hospital of Iceland, Reykjavik, Iceland

    Isleifur Olafsson

  9. Department of Clinical Biochemistry, Akureyri Hospital, Akureyri, Iceland

    Olof Sigurdardottir

  10. Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland

    Bjarni Torfason

  11. Department of Cardiothoracic Surgery, Landspitali—the National University Hospital, Reykjavik, Iceland

    Bjarni Torfason

  12. Department of Anthropology, University of Iceland, Reykjavik, Iceland

    Agnar Helgason

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  1. Doruk Beyter
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Contributions

D.B. implemented software, with additional software implemented by H.I., H.P.E., S.K., S.M., G.H. and B.V.H. D.B. and B.V.H. wrote the paper with input from H.I., A.O., H.P.E., E.B., H.J., B.A.A., S.K., M.T.H., S.A.G., R.P.K., G.H., G.P., O.A.S., A.H., U.T., H.H., D.F.G., P.S., O.T.M. and K.S. H.I. implemented the analysis pipelines, with input from D.B., S.K., S.A.G., S.T.S., G.M. and B.V.H. D.N.M. and O.T.M. performed ONT sequencing. Aslaug Jonasdottir and Adalbjorg Jonasdottir performed PCR validation experiments. G.E., I.O. and O.S. acquired LDL measurements. H.H. and B.T. acquired heart tissues. B.V.H. and K.S. conceived and supervised the study. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Bjarni V. Halldorsson or Kari Stefansson.

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Competing interests

D.B., H.I., A.O., H.P.E., E.B., H.J., B.A.A., S.K., M.T.H., S.A.G., D.N.M., Aslaug Jonasdottir, Adalbjorg Jonasdottir, R.P.K., S.T.S., G.H., G.P., O.A.S., G.M., A.H., U.T., H.H., D.F.G., P.S., O.T.M., B.V.H. and K.S. are employees of deCODE genetics/Amgen. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Mark Chaisson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Oxford Nanopore Technologies (ONT) long-read sequencing statistics.

a, N50 length per flowcell (N = 4,757 flowcells) prior to GRCh38 alignment. b,c,d, Aligned coverage, alignment percentage, and error rates stratified by type, per individual (N = 3,622 individuals). Statistics are computed over sequenced reads longer than 3000 bp. In panel d, box limits indicate upper and lower quartiles, centre line indicates median, and whiskers indicate ±1.5 times the interquartile range.

Extended Data Fig. 2 SquiggleSVFilter overview.

Given a candidate structural variant (SV), and an SV supporting read, SquiggleSVFilter first identifies the subread of the ONT basecalled read overlapping the SV, using the reference alignment BAM file. Next it finds the squiggle slice of the identified subsequence using the event table. For both the left and right flanks around the variant, it determines the reference and alternative sequences given the candidate variant, and computes their raw data-vs-sequence log likelihood scores with the squiggle slice. A sufficiently high log likelihood score difference for the alternate allele marks the read as an SV supporting read.

Extended Data Fig. 3 Allele frequency distribution of SVs at low frequency.

SVs are binned at 0.01% for alleles with 0.1% to 5% frequency.

Extended Data Fig. 4 Length and modulo distributions of structural variants (SVs) that are contained within exons.

a, Length distribution of SVs with lengths between 50 and 100. Stars denote lengths divisible by 3. (N = 224 markers). b, Modulo distribution of SV lengths across length intervals. (N = 549).

Supplementary information

Supplementary Information

Supplementary Methods and Figs. 1–4

Reporting Summary

Supplementary Tables

Supplementary Tables 1–6

Supplementary Data 1

Sequencing-related information of 4,757 flow cells from 3,622 individuals.

Supplementary Data 2

Summary-level data of high-confidence SVs.

Supplementary Data 3

Primer sequences and results from PCR validation.

Supplementary Data 4

5,238 SVs in strong LD with GWAS catalog variants and related data.

Supplementary Data 5

List of genes with at least one homozygous carrier of a rare high-impact SV allele in our study.

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Beyter, D., Ingimundardottir, H., Oddsson, A. et al. Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits. Nat Genet 53, 779–786 (2021). https://doi.org/10.1038/s41588-021-00865-4

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  • DOI: https://doi.org/10.1038/s41588-021-00865-4

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