Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetic testing in monogenic early-onset atrial fibrillation


A substantial proportion of atrial fibrillation (AF) cases cannot be explained by acquired AF risk factors. Limited guidelines exist that support routine genetic testing. We aim to determine the prevalence of likely pathogenic and pathogenic variants from AF genes with robust evidence in a well phenotyped early-onset AF population. We performed whole exome sequencing on 200 early-onset AF patients. Variants from exome sequencing in affected individuals were filtered in a multi-step process, prior to undergoing clinical classification using current ACMG/AMP guidelines. 200 AF individuals were recruited from St. Paul’s Hospital and London Health Sciences Centre who were ≤ 60 years of age and without any acquired AF risk factors at the time of AF diagnosis. 94 of these AF individuals had very early-onset AF ( ≤ 45). Mean age of AF onset was 43.6 ± 9.4 years, 167 (83.5%) were male and 58 (29.0%) had a confirmed family history. There was a 3.0% diagnostic yield for identifying a likely pathogenic or pathogenic variant across AF genes with robust gene-to-disease association evidence. This study demonstrates the current diagnostic yield for identifying a monogenic cause for AF in a well-phenotyped early-onset AF cohort. Our findings suggest a potential clinical utility for offering different screening and treatment regimens in AF patients with an underlying monogenic defect. However, further work is needed to dissect the additional monogenic and polygenic determinants for patients without a genetic explanation for their AF despite the presence of specific genetic indicators such as young age of onset and/or positive family history.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Categorizing atrial fibrillation genes based on the ClinGen framework for gene to disease association.
Fig. 2: Flow diagram used to identify variants across atrial fibrillation genes with robust evidence.
Fig. 3: Specific criteria used to clinically evaluate variants across atrial fibrillation genes that met our filtering criteria.

Data availability

Data will be made available upon request.


  1. Chugh S, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin E, et al. Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study. Circulation 2014;129:837–47.

    Article  PubMed  Google Scholar 

  2. Darbar D. Genetics of atrial fibrillation: Rare mutations, common polymorphisms, and clinical relevance. Heart Rhythm. 2008;5:483–6.

    Article  PubMed  Google Scholar 

  3. Weng L, Preis S, Hulme O, Larson M, Choi S, Wang B, et al. Genetic predisposition, clinical risk factor burden, and lifetime risk of atrial fibrillation. Circulation 2018;137:1027–38.

    Article  PubMed  Google Scholar 

  4. Lévy S, Maarek M, Coumel P, Guize L, Lekieffre J, Medvedowsky J, et al. Characterization of different subsets of atrial fibrillation in general practice in France: The ALFA study. The college of French cardiologists. Circulation 1999;99:3028–35.

    Article  PubMed  Google Scholar 

  5. Kalstø S, Siland J, Rienstra M, Christophersen I. Atrial fibrillation genetics update: Toward clinical implementation. Front Cardiovasc Med. 2019;6:127.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Olesen M, Nielsen M, Haunsø S, Svendsen J. Atrial fibrillation: The role of common and rare genetic variants. Eur J Hum Genet. 2014;22:297–306.

    Article  CAS  PubMed  Google Scholar 

  7. Nattel S, Dobrev D. Controversies about atrial fibrillation mechanisms. Circ Res. 2017;120:1396–8.

    Article  CAS  PubMed  Google Scholar 

  8. Andrade J, Verma A, Mitchell L, Parkash R, Leblanc K, Atzema C, et al. 2018 focused update of the canadian cardiovascular society guidelines for the management of atrial fibrillation. Can J Cardiol 2018;34:1371–92.

    Article  PubMed  Google Scholar 

  9. Andrade J, Aguilar M, Atzema C, Bell A, Cairns J, Cheung C, et al. The 2020 Canadian cardiovascular society/canadian heart rhythm society comprehensive guidelines for the management of atrial fibrillation. Can J Cardiol. 2020;36:1847–948.

  10. Strande N, Riggs E, Buchanan A, Ceyhan-Birsoy O, Distefano M, Dwight S, et al. Evaluating the clinical validity of gene-disease associations: An evidence-based framework developed by the clinical genome resource. Am J Hum Genet. 2017;100:895–906.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ingles J, Goldstein J, Thaxton C, Caleshu C, Corty EW, Crowley SB, et al. Evaluating the clinical validity of hypertrophic cardiomyopathy genes. Circulation: Genomic and Precision. Medicine 2019;12:e002460.

    CAS  Google Scholar 

  12. Jordan E, Peterson L, Ai T, Asatryan B, Bronicki L, Brown E, et al. Evidence-based assessment of genes in dilated cardiomyopathy. Circulation 2021;144:7–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: A Fast and Accurate Lllumina paired-end ReAd MergeR. Bioinformatics 2014;30:614–20.

    Article  CAS  PubMed  Google Scholar 

  14. Bolger A, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Lllumina sequence data. Bioinformatics 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li H, Durbin R. Fast and accurate long-read alignment with burrows–wheeler transform. Bioinformatics 2010;26:589–95.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kosugi S, Natsume S, Yoshida K, Maclean D, Cano L, Kamoun S, et al. Coval: Improving alignment quality and variant calling accuracy for next-generation sequencing data. PLoS ONE. 2013;8:e75402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cornish A, Guda C. A comparison of variant calling pipelines using genome in a bottle as a reference. Biomed Res Int. 2015;2015:456479.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American college of medical genetics and genomics and the association for molecular pathology. Genet Med. 2015;17:405–23.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen Y, Xu S, Bendahhou S, Wang X, Wang Y, Xu W, et al. KCNQ1 Gain-of-function mutation in familial atrial fibrillation. Science 2003;299:251–4.

    Article  CAS  PubMed  Google Scholar 

  20. Steffensen AB, Refsgaard L, Andersen MN, Vallet C, Mujezinovic A, Haunsø S, et al. IKs gain- and loss-of-function in early-onset lone atrial fibrillation. J Cardiovasc Electrophysiol. 2015;26:715–23.

    Article  PubMed  Google Scholar 

  21. Ellinor PT, Nam EG, Shea MA, Milan DJ, Ruskin JN, CA M. Cardiac sodium channel mutation in atrial fibrillation. Heart Rhythm. 2008;5:99–105.

    Article  PubMed  Google Scholar 

  22. Olson TM, Alekseev AE, Liu XK, Park S, Zingman LV, Bienengraeber M, et al. Kv1.5 Channelopathy Due to KCNA5 loss-of-function mutation causes human atrial fibrillation. Hum Mol Genet. 2006;15:2185–91.

    Article  CAS  PubMed  Google Scholar 

  23. Sébillon P, Bouchier C, Bidot LD, Bonne G, Ahamed K, Charron P, et al. Expanding the phenotype of LMNA mutations in dilated cardiomyopathy and functional consequences of these mutations. J Med Genet. 2003;40:560–7.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yang YQ, Xu YJ, Li RG, Qu XK, Fang WY, Liu X. Prevalence and Spectrum of PITX2c mutations associated with familial atrial fibrillation. Int J Cardiol. 2013;168:2873–6.

    Article  PubMed  Google Scholar 

  25. Postma AV, van de Meerakker JB, Mathijssen IB, Barnett P, Christoffels VM, Ilgun A, et al. A Gain-of-Function TBX5 mutation is associated with atypical Holt–Oram syndrome and paroxysmal atrial fibrillation. Circ Res. 2008;102:1433–42.

    Article  CAS  PubMed  Google Scholar 

  26. Gollob MH, Jones DL, Krahn AD, Danis L, Gong XQ, Shao Q, et al. Somatic Mutations in the Connexin 40 Gene (GJA5) in Atrial Fibrillation. N. Engl J Med. 2006;354:2677–88.

    Article  CAS  PubMed  Google Scholar 

  27. Ahlberg GRL, Lundegaard PR, Andreasen L, Ranthe MF, Linscheid N, Nielsen JB, et al. Rare truncating variants in the sarcomeric protein titin associate with familial and early-onset atrial fibrillation. Nat Commun. 2018;9:4316.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Choi SH, Weng LC, Roselli C, Lin H, Haggerty CM, Shoemaker MB, et al. Association between titin loss-of-function variants and early-onset atrial fibrillation. JAMA 2018;320:2354–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Goodyer W, Dunn K, Caleshu C, Jackson M, Wylie J, Moscarello T, et al. Broad genetic testing in a clinical setting uncovers a high prevalence of titin loss-of-function variants in very early onset atrial fibrillation. Circ Genom Precis Med. 2019;12:e002713.

    Article  PubMed  Google Scholar 

  30. Lazarte J, Laksman Z, Wang J, Robinson J, Dron J, Leach E, et al. Enrichment of loss-of-function and copy number variants in ventricular. Cardiomyopathy Genes in “Lone” Atrial Fibrillation. Europace. 2021;23:844–50.

  31. Palmio J, Leonard-Louis S, Sacconi S, Savarese M, Penttila S, Semmler AL, et al. Expanding the importance of HMERF Titinopathy: New mutations and clinical aspects. J Neurol. 2019;266:680–90.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yoneda Z, Anderson K, Quintana J, O’Neill M, Sims R, Glazer A, et al. Early-onset atrial fibrillation and the prevalence of rare variants in cardiomyopathy and arrhythmia genes. JAMA Cardiol. 2021;6:e213370.

    Article  Google Scholar 

  33. Kelly MA, Caleshu C, Morales A, Buchan J, Wolf Z, Harrison SM, et al. Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: Recommendations by ClinGen’s inherited cardiomyopathy expert panel. Genet Med. 2018;20:351–9.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chalazan B, Mol D, Darbar F, Ornelas-Loredo A, Al-Azzam B, Chen Y, et al. Association of rare genetic variants and early-onset atrial fibrillation in ethnic minority individuals. JAMA Cardiol. 2021;6:811–9.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, Calkins H, et al. HRS/EHRA Expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies. Europace 2011;13:1077–109.

    Article  PubMed  Google Scholar 

  36. Priori SG, Wilde AA, Horie M, Cho Y, Behr ER, Berul C, et al. HRS/EHRA/APHRS Expert consensus statement on the diagnosis and management of patients with inherited primary arrhythmia syndromes. Heart Rhythm. 2013;10:1932–63.

    Article  PubMed  Google Scholar 

  37. Shoemaker MB, Shah RL, Roden DM, Perez MV. How will genetics inform the clinical care of atrial fibrillation? Circ Res. 2020;127:111–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


This work was supported by the Canadian Cardiovascular Society, University of British Columbia Cardiology Academic Practice Plan; Charles Kerr Scholarship in Cardiovascular Genetics.

Author information

Authors and Affiliations



Conceptualization: BC, AL, ZL; Data curation: BC, AL; Formal analysis: BC, AM; Investigation: BC; Resources: AL, ZL; Software: AL; Visualization: BC; Writing-original draft: BC; Writing-review & editing: BC, EL, AL, KR, MB, JW, LH, TR, JL, RH, AL, ZL.

Corresponding author

Correspondence to Zachary Laksman.

Ethics declarations

Ethical approval

We attest that the research included in this report was conducted in a manner consistent with the principles of research ethics, such as those described in the Declaration of Helsinki and/or the Belmont Report. In particular, this research was conducted with the voluntary, informed consent of any research participants, free of coercion or coercive circumstances, and received Research Ethics Board (REB) approval from the University of British Columbia that is consistent with the principles of research ethics and the legal requirements of the lead authors’ jurisdiction(s). Written informed consent was obtained from all participants under a protocol approved by the University of British Columbia Research Ethics Board (H16-02531).

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chalazan, B., Freeth, E., Mohajeri, A. et al. Genetic testing in monogenic early-onset atrial fibrillation. Eur J Hum Genet 31, 769–775 (2023).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links