Letter | Published:

Biobank-driven genomic discovery yields new insight into atrial fibrillation biology

Nature Genetics (2018) | Download Citation


To identify genetic variation underlying atrial fibrillation, the most common cardiac arrhythmia, we performed a genome-wide association study of >1,000,000 people, including 60,620 atrial fibrillation cases and 970,216 controls. We identified 142 independent risk variants at 111 loci and prioritized 151 functional candidate genes likely to be involved in atrial fibrillation. Many of the identified risk variants fall near genes where more deleterious mutations have been reported to cause serious heart defects in humans (GATA4, MYH6, NKX2-5, PITX2, TBX5)1, or near genes important for striated muscle function and integrity (for example, CFL2, MYH7, PKP2, RBM20, SGCG, SSPN). Pathway and functional enrichment analyses also suggested that many of the putative atrial fibrillation genes act via cardiac structural remodeling, potentially in the form of an ‘atrial cardiomyopathy’2, either during fetal heart development or as a response to stress in the adult heart.

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Download references


The Nord-Trøndelag Health Study (the HUNT Study) is a collaboration between the HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology (NTNU)), Nord-Trøndelag County Council, the Central Norway Health Authority, and the Norwegian Institute of Public Health. The K.G. Jebesen Center for Genetic Epidemiology is financed by Stiftelsen Kristian Gerhard Jebsen, the Faculty of Medicine and Health Sciences Norwegian University of Science and Technology (NTNU), and the Central Norway Regional Health Authority. This research has been conducted using the UK Biobank Resource under application number 24460. J.B.N. was supported by grants from the Danish Heart Foundation (16-R107-A6779) and the Lundbeck Foundation (R220-2016-1434). T.J.H. was supported by an American Heart Association Scientist Development Grant (0735464Z). J.A.S. was supported by National Institutes of Health grant R01-HL124232. C.J.W. was supported by National Institutes of Health grants R35-HL135824, R01-HL127564, R01-HL117626-02-S1, and R01-HL130705. To the best of our knowledge, this manuscript complies with all relevant ethical regulations.

Author information

Author notes

  1. These authors contributed equally: Jonas B. Nielsen, Rosa B. Thorolfsdottir, Lars G. Fritsche, Wei Zhou, Morten W. Skov, Sarah E. Graham, Todd J. Herron, Shane McCarthy, Ellen M. Schmidt, Gardar Sveinbjornsson.


  1. Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, USA

    • Jonas B. Nielsen
    • , Lars G. Fritsche
    • , Wei Zhou
    • , Sarah E. Graham
    • , Ida Surakka
    • , Maoxuan Lin
    • , Brooke N. Wolford
    • , Whitney Hornsby
    • , José Jalife
    •  & Cristen J. Willer
  2. Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA

    • Jonas B. Nielsen
    • , Sarah E. Graham
    • , Ida Surakka
    • , Maoxuan Lin
    • , Jacob Kitzman
    •  & Cristen J. Willer
  3. deCODE genetics/Amgen, Inc., Reykjavik, Iceland

    • Rosa B. Thorolfsdottir
    • , Gardar Sveinbjornsson
    • , Patrick Sulem
    • , David O. Arnar
    • , Unnur Thorsteinsdottir
    • , Hilma Holm
    • , Daniel F. Gudbjartsson
    •  & Kari Stefansson
  4. Faculty of Medicine, University of Iceland, Reykjavik, Iceland

    • Rosa B. Thorolfsdottir
    • , David O. Arnar
    • , Unnur Thorsteinsdottir
    •  & Kari Stefansson
  5. HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, Norway

    • Lars G. Fritsche
    • , Maiken E. Gabrielsen
    • , Anne Heidi Skogholt
    • , Oddgeir L. Holmen
    •  & Kristian Hveem
  6. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, Trondheim, Norway

    • Lars G. Fritsche
    • , Maiken E. Gabrielsen
    • , Anne Heidi Skogholt
    • , Oddgeir L. Holmen
    • , Gonçalo R. Abecasis
    •  & Kristian Hveem
  7. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    • Lars G. Fritsche
    • , Wei Zhou
    • , Ryan D. Crawford
    • , Brooke N. Wolford
    •  & Cristen J. Willer
  8. Laboratory of Molecular Cardiology, Department of Cardiology, The Heart Centre, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark

    • Morten W. Skov
    •  & Anders G. Holst
  9. Department of Internal Medicine, Center for Arrhythmia Research, University of Michigan, Ann Arbor, MI, USA

    • Todd J. Herron
    •  & José Jalife
  10. Regeneron Genetics Center, Tarrytown, NY, USA

    • Shane McCarthy
    • , Jonathan H. Chung
    • , Joshua D. Backman
    • , Aris Baras
    • , Colm O’Dushlaine
    • , Frederick E. Dewey
    •  & Tanya M. Teslovich
  11. Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    • Ellen M. Schmidt
    • , Rounak Dey
    • , Xiaoquan Wen
    • , Michael Boehnke
    • , Bhramar Mukherjee
    • , Seunggeun Lee
    • , Hyun M. Kang
    •  & Gonçalo R. Abecasis
  12. Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA

    • Michael R. Mathis
    • , Sachin Kheterpal
    •  & Chad M. Brummett
  13. Medical Device Development and Regulation Research Center, The University of Tokyo, Tokyo, Japan

    • Masatoshi Yamazaki
  14. Department of Cardiology, St. Olav’s University Hospital, Trondheim, Norway

    • Oddgeir L. Holmen
  15. Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway

    • Håvard Dalen
    •  & Kristian Hveem
  16. Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway

    • Håvard Dalen
  17. Department of Cardiology, St. Olav’s University Hospital, Trondheim University Hospital, Trondheim, Norway

    • Håvard Dalen
  18. Department of Medicine, Landspitali – National University Hospital, Reykjavik, Iceland

    • David O. Arnar
  19. Department of Pediatrics and Communicable Diseases, University of Michigan, Ann Arbor, MI, USA

    • Jordan A. Shavit
  20. Fundación Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

    • José Jalife
  21. Geisinger Health System, Danville, PA, USA

    • David J. Carey
  22. School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland

    • Daniel F. Gudbjartsson


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J.B.N., R.B.T., L.G.F., W.Z., M.W.S., S.E.G., S.M., E.M.S., G.S., I.S., M.L., B.N.W., R.D., P.S., U.T., and X.W. performed the computational analyses. M.R.M., M.E.G., A.H.S., O.L.H., H.D., J.H.C., J.D.B., D.O.A., U.T., A.B., C.O., A.G.H., W.H., S.K., C.M.B., and T.M.T. conducted data acquisition. T.J.H., M.Y., R.D.C., J.K., J.A.S., and J.J. performed wet lab experiments. O.L.H., F.E.D., M.B., S.L., H.M.K., H.H., D.J.C., D.F.G., K.S., B.M., G.R.A., K.H., and C.J.W. designed and supervised the study. All authors contributed to manuscript preparation and read, commented on, and approved the manuscript.

Competing interests

R.B.T., G.S., D.O.A., P.S., U.T., D.F.G., H.H., and K.S. are employed by deCODE genetics/Amgen, Inc., Reykjavik, Iceland. A.G.H. is employed by Novo Nordisk A/S, Bagsværd, Denmark. S.M., J.H.C., J.D.B., A.B., C.O., F.E.D., G.R.A., and T.M.T. are employed by Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA. D.J.C. is employed by Geisinger Health System, Danville, Pennsylvania, USA.

Corresponding authors

Correspondence to Gonçalo R. Abecasis or Kristian Hveem or Cristen J. Willer.

Integrated supplementary information

  1. Supplementary Figure 1 Quantile–quantile plots for genome-wide single-variant association analyses for the six contributing study cohorts.

    Markers are stratified by minor allele frequency below versus above 0.01. A genomic control factor of 1.38 was applied to the deCODE association results. Dots indicate observed P values (–log10 (P value)) compared with those expected by chance under the null hypothesis (no association). The black line indicates the identity (no association) with corresponding 95% confidence intervals.

  2. Supplementary Figure 2 Enrichment of atrial fibrillation–associated risk variants in regulatory elements across 127 Roadmap Epigenomics tissue groups.

    A total of 785 combinations of regulatory features and tissues were examined. P values and fold enrichment were estimated using GREGOR. The most statistically significant findings comprised an overlap with H3K27 in right atrium and left ventricle along with H3K4me1 and DNase sites in fetal heart.

  3. Supplementary Figure 3 Heat map showing the effects of atrial fibrillation (AF) variants on electrocardiogram (ECG) traits in sinus rhythm ECGs, excluding AF cases.

    Sinus rhythm ECG measurements were available for 62,974 Icelandic individuals without diagnosis of AF. Each column shows the estimated effect of the AF risk allele on various ECG traits. The effect of each variant, annotated with the locus gene names, is scaled with the log AF odds ratio. Novel variants are marked with an asterisk. Red represents a positive effect of the AF risk allele on the ECG variable, and blue represents negative effect. The effect is shown only for significant associations after adjusting for multiple testing with a false discovery rate procedure for each variant. Non-significant associations are white in the heat map. Sixty of 111 variants with at least one association are shown. P values and effect estimates were obtained using BOLT-LMM. For readability, selected highly correlated lead-specific time duration ECG variables (P interval, r2 > 0.51; PR segment, r2 > 0.46; QRS duration, r2 > 0.47; and T duration, r2 > 0.16) have been omitted from the plot. A complete set of association results is provided in Supplementary Table 12. PRint, PR interval; PRseg, PR segment; QRSdur, QRS interval duration; Pamp, P-wave amplitude; Parea, P-wave area; Pdur, P-wave duration; Ramp, R-wave amplitude; Tamp, T-wave amplitude.

  4. Supplementary Figure 4 Relationship between left atrium pressure and duration of atrial fibrillation (AF) following burst pacing of rabbit hearts.

    This is an extended version of Fig. 4b showing all individual data points. Heart failure (HF) hearts (n = 4) developed long-lasting AF (>60 s) when intra-atrial pressure was increased to 10 cm H2O. Control hearts (n = 4) did not develop long-lasting AF until intra-atrial pressure was increased to 30 cm H2O. Each individual measurement (represented by a dot) is superimposed on box plots showing the median (horizontal black lines), interquartile range (upper and lower box boarders), and interquartile range × 1.5 (vertical black lines) of AF duration.

  5. Supplementary Figure 5 Western blotting for MYH7 expression (β-MyHC protein) indicates MYH7 expression exclusively in the remodeled heart failure left atrium.

    Uncropped version of Fig. 4c.

  6. Supplementary Figure 6 Immunostaining and confocal microscopy reveal heterogeneous MYH7 expression in the heart failure left atrium.

    Green represents MYH7 expression (β-myosin), and red represents actin filaments.

  7. Supplementary Figure 7 Association between atrial fibrillation polygenic risk score (n = 142 markers) and 1,494 ICD-based traits in UK Biobank participants of white British ancestry.

    Association tests were performed using a logistic regression adjusted for sex and birth year. The horizontal dotted red line represents a P-value threshold of significance based on Bonferroni correction (P < 0.05/1,494 = 3.3 × 10–5). Some labels have been omitted on the left plot (see Supplementary Table 15 for details on association results).

  8. Supplementary Figure 8 Polygenic risk score distributions for atrial fibrillation–associated variants stratified by age of onset of disease.

    Results are based on the HUNT Study only. White dots represent the median, black boxes represent interquartile ranges, black whiskers are the interquartile range times 1.5, and the colored areas show the probability density of the data. The horizontal red dotted line represents the median score for controls.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8 and Supplementary Note

  2. Reporting Summary

  3. Supplementary Tables

    Supplementary Tables 1–16

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