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Mapping and characterization of structural variation in 17,795 human genomes

Nature volume 583, pages 83–89 (2020)Cite this article

Subjects

Abstract

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0–11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

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Fig. 1: The public version of the B38 callset derived from 14,623 samples.
Fig. 2: Burden of rare gene-altering SVs.
Fig. 3: Estimation of genome-wide burden of high-impact functional alleles.
Fig. 4: Dosage sensitivity of functional annotations.

Data availability

The sequencing data can be accessed through dbGaP (https://www.ncbi.nlm.nih.gov/gap) under the accession numbers provided in Supplementary Table 7. PacBio long-read data used for SV validation can be accessed through the Sequence Read Archive (SRA), under the accession numbers provided in Supplementary Table 2. The set of high-confidence HGSVC long-read-derived SV calls, validated by our independent PacBio data and used as a truth set, can be found in Supplementary File 3. Supplementary Files 1–4 can be found at https://github.com/hall-lab/sv_paper_042020.

Code availability

Custom code used in the long-read validation can be found here: https://github.com/abelhj/long-read-validation/tree/master.

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Acknowledgements

We thank staff at the NHGRI for supporting this effort. This study was funded by NHGRI CCDG awards to Washington University in St Louis (UM1 HG008853), the Broad Institute of MIT and Harvard (UM1 HG008895), Baylor College of Medicine (UM1 HG008898) and New York Genome Center (UM1 HG008901); an NHGRI GSP Coordinating Center grant to Rutgers (U24 HG008956); and a Burroughs Wellcome Fund Career Award to I.M.H. Additional data production at Washington University in St Louis was funded by a separate NHGRI award (5U54HG003079). We thank S. Sunyaev for comments on the manuscript; T. Teshiba for coordinating samples for FINRISK and EUFAM sequencing; and the staff and participants of the ARIC study for their contributions; and we acknowledge all individuals who were involved in the collection of samples that were analysed for this study. Data production for EUFAM was funded by 4R01HL113315-05; the Metabolic Syndrome in Men (METSIM) study was supported by grants to M. Laakso from the Academy of Finland (no. 321428), the Sigrid Juselius Foundation, the Finnish Foundation for Cardiovascular Research, Kuopio University Hospital and the Centre of Excellence of Cardiovascular and Metabolic Diseases supported by the Academy of Finland; data collection for the CEPH pedigrees was funded by the George S. and Dolores Doré Eccles Foundation and NIH grants GM118335 and GM059290; study recruitment at Washington University in St Louis was funded by the DDRCC (NIDDK P30 DK052574) and the Helmsley Charitable Trust; study recruitment at Cedars-Sinai was supported by the F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, NIH/NIDDK grants P01 DK046763 and U01 DK062413 and the Helmsley Charitable Trust; study recruitment at Intermountain Medical Center was funded by the Dell Loy Hansen Heart Foundation; the Late Onset Alzheimer's Disease Study (LOAD) study was funded by grants to T. Foroud (U24AG021886, U24AG056270, U24AG026395 and R01AG041797); the Atherosclerosis Risk in Communities (ARIC) study was funded by the NHLBI (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I); and the PAGE programme is funded by the NHGRI with co-funding from the NIMHD (U01HG007416, U01HG007417, U01HG007397, U01HG007376 and U01HG007419). Samples from the BioMe Biobank were provided by The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai. The Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by the NHLBI (N01-HC65233, N01-HC65234, N01-HC65235, N01-HC65236 and N01-HC65237), with contributions from the NIMHD, NIDCD, NIDCR, NIDDK, NINDS and NIH ODS. The Multiethnic Cohort (MEC) study is funded through the NCI (R37CA54281, R01 CA63, P01CA33619, U01CA136792 and U01CA98758). For the Stanford Global Reference Panel, individuals from Puno, Peru were recruited by J. Baker and C. Bustamante, with funding from the Burroughs Welcome Fund, and individuals from Rapa Nui (Easter Island) were recruited by K. Sandoval Mendoza and A. Moreno Estrada, with funding from the Charles Rosenkranz Prize for Health Care Research in Developing Countries. The Women’s Health Initiative (WHI) programme is funded by the NHLBI (HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C and HHSN271201100004C). The GALA II study and E. G. Burchard are supported by the Sandler Family Foundation, the American Asthma Foundation, the RWJF Amos Medical Faculty Development Program, the Harry Wm. and Diana V. Hind Distinguished Professor in Pharmaceutical Sciences II, the NHLBI (R01HL117004, R01HL128439, R01HL135156 and X01HL134589), the NIEHS (R01ES015794, R21ES24844), the NIMHD (P60MD006902, R01MD010443, RL5GM118984) and the Tobacco-Related Disease Research Program (24RT-0025). We acknowledge the following GALA II co-investigators for recruitment of individuals, sample processing and quality control: C. Eng, S. Salazar, S. Huntsman, D. Hu, A. C.Y. Mak, L. Caine, S. Thyne, H. J. Farber, P. C. Avila, D. Serebrisky, W. Rodriguez-Cintron, Jose R. Rodriguez-Santana, R. Kumar, L. N. Borrell, E. Brigino-Buenaventura, A. Davis, M. A. LeNoir, K. Meade, S. Sen and F. Lurmann, and we thank the staff and participants who contributed to the GALA II study.

Author information

Author notes

  1. Richard K. Wilson

    Present address: Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

  2. These authors contributed equally: Haley J. Abel, David E. Larson

Authors and Affiliations

  1. McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA

    Haley J. Abel, David E. Larson, Allison A. Regier, Colby Chiang, Indraniel Das, Krishna L. Kanchi, Elizabeth Appelbaum, Lei Chen, Ryan Christ, Lisa Cook, Matthew Cordes, Laura Courtney, Tracie Deluca, Catrina Fronick, Lucinda Fulton, Robert Fulton, Liron Ganel, Bo Ji, Chul Joo Kang, Adam E. Locke, Amy Ly, Joanne Nelson, Jennifer Ponce, Jason Waligorski, Richard K. Wilson, Erica Young, Susan K. Dutcher, Nathan O. Stitziel & Ira M. Hall

  2. Department of Genetics, Washington University School of Medicine, St Louis, MO, USA

    Haley J. Abel, David E. Larson, Susan K. Dutcher, Nathan O. Stitziel & Ira M. Hall

  3. Department of Medicine, Washington University School of Medicine, St Louis, MO, USA

    Allison A. Regier, Adam E. Locke, Rodney D. Newberry, Erica Young, Nathan O. Stitziel & Ira M. Hall

  4. BioFrontiers Institute, University of Colorado, Boulder, CO, USA

    Ryan M. Layer

  5. Department of Computer Science, University of Colorado, Boulder, CO, USA

    Ryan M. Layer

  6. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Benjamin M. Neale, Eric Banks, Mark J. Daly, Patrick T. Ellinor, Yossi Farjoun, Stacy Gabriel, Namrata Gupta, Daniel Howrigan, Sek Kathiresan, Amit Khera, Robert Maier, Aarno Palotie, Samuli Ripatti, Christine Stevens, Kathleen Tibbetts, Charlotte Tolonen & Eric S. Lander

  7. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Benjamin M. Neale

  8. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Benjamin M. Neale

  9. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    William J. Salerno, Eric Boerwinkle, Huyen Dinh, Harsha Doddapaneni, Richard A. Gibbs, Megan L. Grove, Yi Han, Jianhong Hu, Ziad Khan, Olga Krasheninina, Vipin Menon, Ginger A. Metcalf, Zeineen Momin, Caitlin Nessner, Jireh Santibanez, Kimberly Walker & Donna M. Muzny

  10. New York Genome Center, New York, NY, USA

    Catherine Reeves, Toby Bloom, Robert B. Darnell, Shailu Gargeya, Goren Germer, Lily Khaira, Tuuli Lappalainen, Tom Maniatis, Guiseppe Narzisi, Michael Wigler, Lara Winterkorn & Michael C. Zody

  11. Department of Statistics, Rutgers University, Piscataway, NJ, USA

    Steven Buyske

  12. Department of Genetics, Rutgers University, Piscataway, NJ, USA

    Jinchuan Xing, Yeting Zhang & Tara C. Matise

  13. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Eric S. Lander

  14. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Eric S. Lander

  15. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, School of Public Health, Ann Arbor, MI, USA

    Goncalo R. Abecasis, Michael Boehnke & Hyun Min Kang

  16. Department of Genetics, Stanford University, Stanford, CA, USA

    Julie Baker, Carlos D. Bustamante & Genevieve Wojcik

  17. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA

    Raphael A. Bernier

  18. Human Genetics Center and Department of Epidemiology, University of Texas Health Science Center, Houston, TX, USA

    Eric Boerwinkle, Goo Jun, Paul De Vries & Bing Yu

  19. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Erwin P. Bottinger, Judy H. Cho, Eimear E. Kenny & Ruth J. F. Loos

  20. Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA

    Steven R. Brant

  21. Department of Bioengineering, University of California, San Francisco, San Francisco, CA, USA

    Esteban G. Burchard

  22. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Judy H. Cho & Eimear E. Kenny

  23. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Judy H. Cho

  24. MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Rajiv Chowdhury

  25. Intermountain Heart Institute, Intermountain Medical Center, Murray, UT, USA

    Michael J. Cutler

  26. Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland

    Mark J. Daly, Aarno Palotie & Samuli Ripatti

  27. Analytical and Translational Genetics Unit, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Mark J. Daly, Daniel Howrigan, Robert Maier, Aarno Palotie & Samuli Ripatti

  28. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Scott M. Damrauer

  29. Laboratory of Molecular Neuro-Oncology, The Rockefeller University, New York, NY, USA

    Robert B. Darnell

  30. Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA

    Robert B. Darnell

  31. Department of Genome Science, University of Washington, Seattle, WA, USA

    Evan E. Eichler & Tychele Turner

  32. Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

    Evan E. Eichler

  33. Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Patrick T. Ellinor, Sek Kathiresan & Amit Khera

  34. National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Mexico

    Andres M. Estrada & Karla S. Mendoza

  35. National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Adam Felsenfeld, Carolyn Hutter, Heidi Sofia, Taylorlyn Stephan & Vivian Ota Wang

  36. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

    Tatiana Foroud

  37. Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA

    Nelson B. Freimer

  38. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

  39. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

  40. Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

  41. Institute for Genomic Medicine, Columbia University Medical Center, New York, NY, USA

    David B. Goldstein

  42. Department of Genetics and Development, Columbia University Medical Center, New York, NY, USA

    David B. Goldstein

  43. Department of Preventative Medicine, University of Southern California, Los Angeles, CA, USA

    Christopher A. Haiman

  44. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Ivan Iossifov & Michael Wigler

  45. Department of Human Genetics, University of Utah, Salt Lake City, UT, USA

    Lynn B. Jorde & Aaron Quinlan

  46. Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA, USA

    John Kane & Clive Pullinger

  47. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Sek Kathiresan & Amit Khera

  48. The Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  49. Center for Statistical Genetics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  50. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Charles Kooperberg & Ulrike Peters

  51. Department of Medicine, Duke University, Durham, NC, USA

    William E. Kraus & Svati H. Shah

  52. Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA

    Subra Kugathasan

  53. Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland

    Markku Laakso

  54. Department of Systems Biology, Columbia University, New York, NY, USA

    Tuuli Lappalainen

  55. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA

    Tom Maniatis

  56. Cancer Center, University of Hawaii, Honolulu, HI, USA

    Loic Le Marchand

  57. Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Gregory M. Marcus

  58. Department of Neurology, Columbia University, New York, NY, USA

    Richard P. Mayeux

  59. F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Dermot P. B. McGovern & Stephan R. Targan

  60. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA

    Kari E. North

  61. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Daniel J. Rader

  62. Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA

    Stephen S. Rich

  63. Department of Medicine, Vanderbilt University, Nashville, TN, USA

    Dan M. Roden & M. Benjamin Shoemaker

  64. National Institute for Health and Welfare, Helsinki, Finland

    Veikko Salomaa

  65. Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland

    Marja-Riitta Taskinen

  66. Heart and Lung Centre, Helsinki University Hospital, Helsinki, Finland

    Marja-Riitta Taskinen

Authors
  1. Haley J. Abel
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  2. David E. Larson
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  18. Ira M. Hall
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Consortia

NHGRI Centers for Common Disease Genomics

  • Goncalo R. Abecasis
  • , Elizabeth Appelbaum
  • , Julie Baker
  • , Eric Banks
  • , Raphael A. Bernier
  • , Toby Bloom
  • , Michael Boehnke
  • , Eric Boerwinkle
  • , Erwin P. Bottinger
  • , Steven R. Brant
  • , Esteban G. Burchard
  • , Carlos D. Bustamante
  • , Lei Chen
  • , Judy H. Cho
  • , Rajiv Chowdhury
  • , Ryan Christ
  • , Lisa Cook
  • , Matthew Cordes
  • , Laura Courtney
  • , Michael J. Cutler
  • , Mark J. Daly
  • , Scott M. Damrauer
  • , Robert B. Darnell
  • , Tracie Deluca
  • , Huyen Dinh
  • , Harsha Doddapaneni
  • , Evan E. Eichler
  • , Patrick T. Ellinor
  • , Andres M. Estrada
  • , Yossi Farjoun
  • , Adam Felsenfeld
  • , Tatiana Foroud
  • , Nelson B. Freimer
  • , Catrina Fronick
  • , Lucinda Fulton
  • , Robert Fulton
  • , Stacy Gabriel
  • , Liron Ganel
  • , Shailu Gargeya
  • , Goren Germer
  • , Daniel H. Geschwind
  • , Richard A. Gibbs
  • , David B. Goldstein
  • , Megan L. Grove
  • , Namrata Gupta
  • , Christopher A. Haiman
  • , Yi Han
  • , Daniel Howrigan
  • , Jianhong Hu
  • , Carolyn Hutter
  • , Ivan Iossifov
  • , Bo Ji
  • , Lynn B. Jorde
  • , Goo Jun
  • , John Kane
  • , Chul Joo Kang
  • , Hyun Min Kang
  • , Sek Kathiresan
  • , Eimear E. Kenny
  • , Lily Khaira
  • , Ziad Khan
  • , Amit Khera
  • , Charles Kooperberg
  • , Olga Krasheninina
  • , William E. Kraus
  • , Subra Kugathasan
  • , Markku Laakso
  • , Tuuli Lappalainen
  • , Adam E. Locke
  • , Ruth J. F. Loos
  • , Amy Ly
  • , Robert Maier
  • , Tom Maniatis
  • , Loic Le Marchand
  • , Gregory M. Marcus
  • , Richard P. Mayeux
  • , Dermot P. B. McGovern
  • , Karla S. Mendoza
  • , Vipin Menon
  • , Ginger A. Metcalf
  • , Zeineen Momin
  • , Guiseppe Narzisi
  • , Joanne Nelson
  • , Caitlin Nessner
  • , Rodney D. Newberry
  • , Kari E. North
  • , Aarno Palotie
  • , Ulrike Peters
  • , Jennifer Ponce
  • , Clive Pullinger
  • , Aaron Quinlan
  • , Daniel J. Rader
  • , Stephen S. Rich
  • , Samuli Ripatti
  • , Dan M. Roden
  • , Veikko Salomaa
  • , Jireh Santibanez
  • , Svati H. Shah
  • , M. Benjamin Shoemaker
  • , Heidi Sofia
  • , Taylorlyn Stephan
  • , Christine Stevens
  • , Stephan R. Targan
  • , Marja-Riitta Taskinen
  • , Kathleen Tibbetts
  • , Charlotte Tolonen
  • , Tychele Turner
  • , Paul De Vries
  • , Jason Waligorski
  • , Kimberly Walker
  • , Vivian Ota Wang
  • , Michael Wigler
  • , Richard K. Wilson
  • , Lara Winterkorn
  • , Genevieve Wojcik
  • , Jinchuan Xing
  • , Erica Young
  • , Bing Yu
  •  & Yeting Zhang

Contributions

I.M.H. conceived and directed the study. D.E.L. and H.J.A. developed the final version of the SV calling pipeline, constructed the SV callsets and performed the data analyses. C.C. and R.M.L. helped design the SV calling pipeline. A.A.R. contributed to long-read validation. I.D. was instrumental in the migration of workflows to the Google Cloud Platform. K.L.K. assisted with data management. E.S.L., B.M.N. and N.O.S. provided input on population genetic analyses. W.J.S., D.M.M., E.S.L., B.M.N., M.C.Z., C.R., T.C.M., S.B., S.K.D., I.M.H. and N.O.S. directed data production, processing and management at their respective sites, and edited the manuscript. Members of the NHGRI CCDG consortium provided samples, produced sequencing data and coordinated and administered data-sharing efforts. H.J.A., D.E.L. and I.M.H. wrote the manuscript.

Corresponding author

Correspondence to Ira M. Hall.

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Competing interests

The authors declare no competing interests.

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

Extended data figures and tables

Extended Data Fig. 1 SV mapping pipeline.

SVs are detected within each sample using LUMPY. Breakpoint probability distributions are used to merge and refine the position of detected SVs within a cohort, followed by parallelized re-genotyping and copy-number annotation. Samples are merged into a single cohort-level VCF file, variant types reclassified and genotypes refined with svtools using the combined breakpoint genotype and read-depth information. Finally, sample-level quality control (QC) and variant confidence scoring is conducted to produce the final callset.

Extended Data Fig. 2 The B37 callset.

a, Variant counts (y axis) for each sample (x axis) in the callset, ordered by cohort. Large (>1 kb) variants are shown in dark shades and smaller variants in light shades. b, Variant counts per sample, ordered by self-reported ancestry according to the colour scheme on the right. Abbreviations as in Fig. 1a. Note that African-ancestry samples show more variant calls, as expected. c, Table showing the number of variant calls by variant type and frequency class, and Mendelian error rate by variant type. d, Histogram of allele count for each variant class, showing alleles with counts ≤ 100. e, Linkage disequilibrium of each variant class as represented by maximum R2 value to nearby SNVs, for n = 1,581 samples. Note that these distributions mirror those from our previous SV callset for GTEx4, which was characterized extensively in the context of expression quantitative trait loci.

Extended Data Fig. 3 The B38 callset.

a, Variant counts (y axis) for each sample (x axis) in the callset, ordered by cohort. Large (>1 kb) variants are shown in dark shades and smaller variants in light shades. b, Variant counts per sample, ordered by self-reported ancestry according to the colour scheme on the right. Abbreviations as in Fig. 1a. Note that African-ancestry samples show more variant calls, as expected. Note also that there is some residual variability in variant counts owing to differences in data from each sequencing centre, but that this is mainly limited to small tandem duplications (see a), primarily at STRs. c, SV length distribution by variant class. d, Distribution of the number of singleton SVs detected in samples from different ancestry groups. Only groups with ≥1,000 samples in the B38 callset are shown, and each group was subsampled down to 1,000 individuals before recalculation of the allele frequency. e, Histogram showing the resolution of SV breakpoint calls, as defined by the length of the 95% confidence interval of the breakpoint-containing region defined by LUMPY, after cross-sample merging and refinement using svtools. Data are from n = 360,614 breakpoints, 2 per variant. f, Distribution of the number of SVs detected per sample in WGS data from each sequencing centre (x axis) for African and non-African (non-AFR) samples, showing all variants (left), and those larger (middle) and smaller (right) than 1 kb in size. Per-centre counts are as follows: centre A, 1,527 AFR, 2,080 non-AFR; centre B, 408 AFR, 2,745 non-AFR; centre C, 2,953 AFR, 2226 non-AFR; centre D, 150 AFR, 2,534 non-AFR. g, Plots of Mendelian error (ME) rate (y axis) by MSQ for each variant class. Dot size is determined by point density (right) and the threshold used to determine high and low confidence SVs are shown by the vertical lines. All box plots in indicate the median (centre line) and the first and third quartiles (box limits); whiskers extend 1.5 × IQR.

Extended Data Fig. 4 PCA for the B37 callset.

PCA was performed using a linkage disequilibrium-pruned subset of high-confidence DEL and MEI variants, with MAF > 1%. Self-reported ancestry is shown using the colour scheme on the right, with abbreviations as in Fig. 1a.

Extended Data Fig. 5 Validation of SV calls by PacBio long reads in nine control samples.

n = 9,905 variants. a, Validation rates in variant carriers (y axis) versus validation rates in non-carriers (that is, false validations; x axis) for each method of determining variant overlap, for a range of supporting-read-count thresholds. Ultra-rare variants (n = 133) are shown separately on the right. For each variant overlap method, each data point represents a distinct read-count threshold (≥1, 2, 3, 5, 10, 15 or 20 PacBio reads) that was used to determine validation of SV calls by long-read alignments. Two methods were used for determining overlap between SV coordinates and long-read alignments while accounting for positional uncertainty: (1) BEDTools pairtopair, requiring overlap between the pair of breakpoint intervals predicted by short-read SV mapping and the pair of breakpoint intervals predicted by long-read alignment, allowing 100 bp or 200 bp of ‘slop’; and (2) BEDTools intersect, requiring 90% or 95% reciprocal overlap between the coordinates spanned by the SV predicted by short-read SV mapping and the SV predicted by long-read alignment. Here, we plot the first criteria by themselves, and in pairwise combination with the latter (see key on the right of the figure). Note that Supplementary Table 3 is based on the ‘100 bp slop or 90% reciprocal overlap’ method, requiring at least two PacBio reads. b, Validation rates by frequency class for variant carriers and non-carriers with increasing PacBio supporting-read thresholds, shown using the same overlap method as in Supplementary Table 3. Variant counts per frequency class are as follows: ultra-rare, n = 133; rare, n = 734; low frequency, n = 1,361; common, n = 7,677.

Extended Data Fig. 6 Mendelian inheritance analysis in a set of three-generation CEPH pedigrees comprising 409 parent–offspring trios.

a, Example structure of a single CEPH pedigree indicating nomenclature of the parental (P0), first (F1) and second (F2) generations. b, Transmission rate of SVs from different allele-frequency classes including SVs that are unique to a single family (ultra-rare), rare (<1%), low frequency (‘low’; 1–5%) and common (>5%). c, Table showing the number and rate of Mendelian errors by allele-frequency class. d, Table showing the number and rate of Mendelian errors for SVs that are unique to a single family, for each SV type.

Extended Data Fig. 7 Comparison of SV calls and genotypes to the 1KG phase 3 callset.

a, Number of known and novel SVs in the B37 (left) and B38 (right) callsets, shown by frequency class. b, Table showing the genotypes (GT) reported in our B38 callset5 (rows) versus the 1KG callset (columns) at SVs identified by both studies among the five samples included in both callsets. c, Table showing genotype concordance by SV type including the fraction of concordant calls and Cohen’s κ coefficient. d, Distribution of correlation (R2) between genotype information determined by breakpoint-spanning reads and estimates of copy number (CN) determined by read-depth analysis for the SVs shown in b, c when genotype information between the B38 and the 1KG callset is concordant (left) or discordant (middle, right). At sites with discordant genotypes, correlation with copy-number information is typically higher for genotypes from the B38 callset (middle) than the 1KG callset (right).

Extended Data Fig. 8 Ultra-rare SVs in the B38 callset.

n = 14,623. a, Histogram showing the number of ultra-rare SVs per individual (ultra-rare is defined as singleton variants private to a single individual or nuclear family). b, Histogram showing the number of genes affected by ultra-rare SVs larger than 1 Mb in size.

Extended Data Fig. 9 Correlations between dosage sensitivity scores for CNV in the combined callset.

n = 17,795. a, Results for deletion variants. The ExAC score is the published ExAC DEL intolerance score45; the CCDG score is similarly calculated from our data, using CCDG deletions; pLI is the published loss-of-function intolerance score from ExAC27; ‘HI.Z’ is the negative of the inverse-normal transformed haploinsufficiency score from DECIPHER46; ‘ave.ccdg.exac’ is the arithmetic mean of the CCDG and ExAC DEL intolerance scores; and ‘ave.ccdg.hi’ is the arithmetic mean of the CCDG and HI-Z scores. The correlations shown are Spearman rank correlations (rho); P values are calculated by two-sided Spearman rank correlation test; and N represents the number of genes included in the test. b, Results for duplication variants, using the same naming conventions as in a.

Extended Data Table 1 Ancestry, ethnicity and continental origin of the samples analysed in this study
Full size table

Supplementary information

Supplementary Information

This file contains: (1) Supplementary Note: SV Callset Quality Assessment; (2) Supplementary References (3) Supplementary Table legends (Supplementary Table files provided in separate excel format); (4) Supplementary Files; and (5) Consortium Members: NHGRI Centers for Common Disease Genomics.

Reporting Summary

Supplementary Table 1

Description of which callset and sample subsets were used for each of the major analyses in the study. See main Supplementary Information PDF for full legend.

Supplementary Table 2

Description of PacBio long-read datasets used for SV validation analyses. See main Supplementary Information PDF for full legend.

Supplementary Table 3

SV validation rate analysis using split-read mapping with deep coverage (>60x) PacBio long-read WGS data. See main Supplementary Information PDF for full legend.

Supplementary Table 4

SV detection sensitivity analysis based on long-read SV calls from the Human Genome Structural Variation Consortium (HGSVC). See main Supplementary Information PDF for full legend.

Supplementary Table 5

Number of variants represented in Fig 2d. See main Supplementary Information PDF for full legend.

Supplementary Table 6

Number of variants for each category in Fig. 3a and 3b. See main Supplementary Information PDF for full legend.

Supplementary Table 7

Data Availability. See main Supplementary Information PDF for full legend.

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Abel, H.J., Larson, D.E., Regier, A.A. et al. Mapping and characterization of structural variation in 17,795 human genomes. Nature 583, 83–89 (2020). https://doi.org/10.1038/s41586-020-2371-0

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