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Personalized medicine and atrial fibrillation: will it ever happen?


Atrial fibrillation (AF) is a common arrhythmia of substantial public health importance. Recent evidence demonstrates a heritable component underlying AF, and genetic discoveries have identified common variants associated with the arrhythmia. Ultimately one hopes that the consideration of genetic variation in clinical practice may enhance care and improve health outcomes. In this review we explore areas of potential clinical utility in AF management including those relating to pharmacogenetics and risk prediction.

Peer Review reports


Atrial fibrillation (AF) is a common cardiac arrhythmia marked by loss of coordinated atrial electrical and mechanical function. Among the potential consequences of AF are symptoms and decreased functional status [1], as well as increased risks of thromboembolic stroke, heart failure, and mortality [2]. Thus far, efforts to prevent AF have generally been limited, and therefore management is directed at preventing or alleviating adverse consequences of the arrhythmia.

In recent years, heritability underlying AF has been recognized [37]. AF risk is increased by about 40% if a first-degree relative has AF, and two-fold if that relative develops AF by age 65 [7]. Numerous genetic variants associated with AF have been reported through traditional mapping techniques such as linkage and candidate gene analyses [8]. For example, novel gain-of-function mutations in KCNE1 were recently described in this journal using a candidate gene approach in patients with early onset AF [9]. KCNE1 encodes the beta subunit of the potassium channel comprising the IKs current, which governs atrial repolarization. Such gain-of-function mutations, and their expected shortening of the action potential duration, are consistent with electrical reentry as a predominant pathophysiological mechanism underlying AF [10]. Mutations in sodium, potassium, and calcium channel subunits, as well as in gap junction and non-ion channel proteins have been reported and are reviewed elsewhere (Table 1) [8, 11].

Table 1 Atrial fibrillation (AF) susceptibility genes and loci

Large-scale genome-wide association studies have identified nine common AF susceptibility loci (Table 1) [1216]. In aggregate, these discoveries implicate cardiac ion channels, transcription factors central to cardiopulmonary development, and signaling molecules as common pathways involved in the development of AF. Prior to these genetic discoveries, the relations between many of these pathways and AF pathogenesis were unrecognized.

Genetic associations with AF have spawned an exciting new era of biological investigation. Integrating recent discoveries with previous insights about atrial remodeling and AF pathophysiology has led to resurgence of interest in exploiting biological pathways with pharmaceuticals for AF management [17, 18]. Given the strong heritable component underlying AF, these discoveries also have elicited hope that determining genetic variation in patients will help individualize clinical management and improve health outcomes. The concept of applying knowledge of an individual's genotype to clinical management is commonly referred to as personalized or precision medicine.

In many ways, as with most areas of clinical medicine, the management of patients with AF is already personalized [19, 20]. Prescription of thromboembolism prophylaxis regimens is tailored for each patient. Bedside stroke and bleeding risk prediction rules [2124] are available to help clinicians decide whether to prescribe aspirin or systemic anticoagulants. Cardioversions, antiarrhythmic therapy, and ablation for AF are variably prescribed depending on patient symptoms. When choosing which systemic anticoagulants or antiarrhythmic medications to prescribe, clinicians consider comorbidities, drug efficacy, adverse effects, contraindications, pharmacologic interactions, and cost. Ablation procedures for patients with persistent AF are more extensive than for those with paroxysmal AF [25].

Despite the personalized nature of AF management, care of patients with AF is challenging owing to the complexity of the arrhythmia. Patient management is limited by unpredictable recurrence and progression of AF, differing susceptibilities to stroke and heart failure, and variable treatment responses. These challenges are opportunities for diagnostic and therapeutic innovation, and represent areas where genetic medicine might facilitate clinical management. Two promising areas for the near-term application of genetic information in AF include pharmacological management and risk prediction (Table 2).

Table 2 Potential areas for clinical application of genetic discoveries in atrial fibrillation (AF) management

Potential areas for clinical application of genetic information


Many medications used for AF treatment have narrow therapeutic margins. Antiarrhythmic efficacy is only about 50% at 12 months [26], and prohibitive adverse effects such as proarrhythmia or other reactions may occur [27, 28]. Thromboembolism prophylaxis with warfarin is limited by increased risks of disabling stroke among subtherapeutic patients, and bleeding among those with supratherapeutic doses [29].

Can the effectiveness and safety of pharmacologic agents used in AF be improved by utilizing genetic information?

Pharmacogenetics refers to how genetic variation accounts for differences in drug responses among individuals. Measurable differences in pharmacodynamics (that is, the interaction of drugs with their target molecules) and pharmacokinetics (that is, properties relating to drug uptake, distribution, duration of effect, metabolism, excretion, and so on) are likely to be associated with genetic variation. In a recent proof-of-concept paper, whole genome sequencing in a single individual identified over 60 previously reported pharmacogenetic variants affecting drug response [30].

A high-profile example of a pharmacogenetic effect relevant to AF management relates to warfarin. Warfarin dose requirements have been linked to variation in CYP2C9 [31] and VKORC1 [32], genes encoding products involved in warfarin pharmacokinetics and pharmacodynamics, respectively. Dosing algorithms that include genetic variation in these genes explain a striking one-third to one-half of the variability in warfarin dose requirements [33]. Accounting for variants in these genes may be particularly beneficial (1) during initiation of warfarin therapy when complications relating to excessive or underdosing are particularly prevalent, and (2) for identifying individuals with unexpectedly high or low therapeutic dose requirements [34] who may therefore be at elevated risk for thromboembolism or bleeding, respectively. The US Food and Drug Administration has advised physicians that genetic testing may improve initial dosing estimates among patients taking warfarin [35] and dosing prediction algorithms are available.

Nevertheless, routine warfarin testing has not been widely adopted for several reasons. Novel anticoagulants [36] and procedures [37] are emerging as potential alternatives to warfarin, rigorous surveillance may improve time spent in the therapeutic range thereby improving safety and efficacy [38], and logistical considerations such as costs and genetic literacy pose challenges to the adoption of genetic testing. Currently, the safety and efficacy of genotype-guided warfarin dosing is being compared to routine dosing in the ongoing Clarification of Optimal Warfarin Dosing through Genetics (COAG) trial ( identifier NCT00839657).

Regardless of the outcome of this trial, the striking associations between genetic variants and warfarin doses have revealed the clinical potential for pharmacogenetics. Emerging reports demonstrate other associations between genetic variants and drug response. Genetic variation has been reported to associate with response to β-adrenergic antagonism in patients with AF [39], and genetic variation associates with drug-induced torsade de pointes [40, 41]. Given the heterogeneity in the molecular mechanisms underlying AF [42], as well as variability in medication efficacy and adverse effects, there may be a practical role for pharmacogenetics in the management of patients with this arrhythmia. It is worth noting that genetic variation has also been reported to associate with AF recurrence after catheter ablation [43]; however, the role of genetic testing for a procedure in which success is dependent on many technical factors remains undefined.

The next steps for assessing the clinical utility of pharmacogenetics will involve performing systematic phenotyping, genotyping, and association testing of established and novel pharmacological agents used for AF management. Candidate genes that ought to be prioritized for screening include those encoding products involved in drug uptake or metabolism, and those encoding drug targets.

Risk prediction

Risk prediction encompasses estimation of AF probability and prediction of AF-related complications such as stroke, heart failure, and death. The goal of prediction algorithms is to identify individuals prior to the onset of AF or AF-related morbidity, and thereby help target prevention efforts. Estimation of risk may also assist with clinical trial development by enabling enrollment of individuals in a particular risk stratum.

Clinical prediction algorithms for new-onset AF have been developed in community-based cohorts of European and African American ancestry [4446]. These algorithms have good but not excellent discriminative properties. In a given sample of individuals without AF, they correctly assigned a higher predicted risk to those who ultimately developed AF only about 60% to 70% of the time [4446]. Simply flipping a coin would correctly assign a higher risk to those developing AF 50% of the time.

Can genetic discoveries improve risk prediction?

We and other investigators from the Framingham Heart Study tested whether accounting for the occurrence of AF in a first-degree relative (familial AF) improved AF risk prediction beyond clinical risk factors [7]. AF prediction improved slightly after accounting for familial AF. In a separate study from Sweden, adding genotypes for two common polymorphisms at the chromosome 4q25 and 16q22 AF susceptibility loci to clinical risk factors did not significantly improve AF risk prediction [47].

For most complex conditions, genetic risk scores have not achieved substantial improvements in discrimination over scores comprised of clinical risk factors. Yet enhancements in the understanding of genetic risk modeling reveal insights that may improve the utility of genetic risk scores in the future. For example, variants included in genetic risk scores to date have small effect sizes (for example, relative risks of 1.1 to 1.5). Single risk factors with such modest relative risks are unlikely to have meaningful impacts on discrimination alone [48]. Large-effect variants, which have greater potential to improve discrimination, are rare and not captured by genotyping panels used in genome-wide association studies.

Fine mapping and sequencing offer promise for the discovery of such large-effect variants [49]. For example in a fine mapping analysis we previously identified independent genetic variants at the chromosome 4q25 locus associated with AF [50]. A multimarker risk score comprised of genotypes tagging these three independent signals demonstrated that AF risk increased with an increasing number of AF risk alleles. The multimarker score identified about 12% of individuals with a twofold or greater relative risk for AF, and 1% with an estimated sixfold elevated risk of AF compared to those with the most common genotypes. This is one of the largest relative risks to date for a qualitative trait in the era of genome-wide association studies.

Another potential reason that genetic risk scores have not substantially improved prediction models is that few variants were tested. Accounting for additional genetic variants associated with conditions at far less stringent significance thresholds can substantially increase the proportion of variance explained by genetic factors [5153]. In simulations, genetic profiling using large numbers of genetic variants significantly improved risk discrimination [54].

Novel metrics have been developed to assess clinical utility of prediction models, with increased recognition that measures of discrimination alone can be insensitive [55]. The role of novel prediction metrics with respect to genetic profiling has not been extensively explored.

Furthermore, although a widespread heritable component underlies AF, the arrhythmia may result from a variety of different pathological processes. Whether genetic profiling will successfully predict incident AF may depend on the extent to which the 'type' of AF studied is influenced by genetic factors. The importance of refining AF classification has been highlighted elsewhere [56, 57], and may overcome the potential challenges that phenotypic heterogeneity presents to risk prediction efforts.

The absence of data demonstrating clinical utility of routinely using genotype information to guide clinical management in AF should not deter hope at this stage, but rather should be viewed as an opportunity for systematic testing of the clinical role of genetic information. Nevertheless, there is a risk that hype can obscure the monumental insights gained from the current era of genetic discovery. As an example, companies such as deCODE and 23andMe are performing direct to consumer genetic testing, and purport to calculate one's genetic risk of AF based on one or a few polymorphisms associated with modestly increased relative risks of AF. These calculations are not based on well-defined genetic effect estimates, and do not consider other genetic risk factors, interactions between genes, the predictive utility of genetic risk markers, or competing risks. Moreover, in the absence of data regarding clinical risk factors and environmental exposures, how are these results to be interpreted? Problems with direct to consumer genetic testing are reinforced by recent literature highlighting the facts that patients often have misperceptions about the results genetic testing [58], and practitioners frequently lack confidence in explaining direct to consumer genetic testing results [59].


Owing to the absence of currently available outcome data, there is insufficient evidence to recommend routine testing for genetic variation in the management of patients with AF, as was highlighted by a recent Heart Rhythm Society and European Heart Rhythm Association consensus statement on genetic testing [60]. However, increased attention to pharmacogenetics and prediction modeling using genetic information offers hope for improved clinical care. The absence of data demonstrating direct utility of incorporating genotype information into clinical practice provides an opportunity to systematically assess whether applying genetic data to clinical practice will enhance outcomes.

As with any new technology or development, systematic assessment of its utility in clinical medicine is warranted. Such assessment includes identifying suboptimal realms of clinical care, determining whether such areas present opportunities for application of genetic knowledge, testing whether genetic discoveries are superior to standard care for improving health outcomes, and evaluating whether the expected benefits of implementing genetic testing justify the resource utilization that would be necessary for its adoption.

Recent AF genetic discoveries herald a sea change in the approach to AF research. Investigation of the pathogenic mechanisms underlying this common and morbid arrhythmia is now informed by unbiased techniques, allowing investigators to branch out from preconceived biological mechanisms and reliance on established animal models of AF. It will take many years to realize the full impact of recent genetic discoveries. In the meantime, questions surrounding the utility of applying genetic discoveries to patient management will only be clarified by rigorous testing of genetic information in clinical practice. Ultimately, it is the opportunity for innovation, discovery, and improvement in health outcomes that makes this era of genetic discovery so exciting.

Authors' information

Steven Lubitz, MD, MPH is a cardiac electrophysiologist at the Massachusetts General Hospital and Instructor in Medicine at Harvard Medical School. Patrick Ellinor, MD, PhD is a cardiac electrophysiologist at Massachusetts General Hospital and Associate Professor at Harvard Medical School.


  1. 1.

    Rienstra M, Lubitz SA, Mahida S, Magnani JW, Fontes JD, Sinner MF, Van Gelder IC, Ellinor PT, Benjamin EJ: Symptoms and functional status of patients with atrial fibrillation: state of the art and future research opportunities. Circulation. 2012, 125: 2933-2943. 10.1161/CIRCULATIONAHA.111.069450.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Soliman EZ, et al: Heart disease and stroke statistics--2012 update: a report from the American Heart Association. Circulation. 2012, 125: e2-e220.

    PubMed  Google Scholar 

  3. 3.

    Fox CS, Parise H, D'Agostino RB Sr, Lloyd-Jones DM, Vasan RS, Wang TJ, Levy D, Wolf PA, Benjamin EJ: Parental atrial fibrillation as a risk factor for atrial fibrillation in offspring. JAMA. 2004, 291: 2851-2855. 10.1001/jama.291.23.2851.

    CAS  PubMed  Google Scholar 

  4. 4.

    Ellinor PT, Yoerger DM, Ruskin JN, MacRae CA: Familial aggregation in lone atrial fibrillation. Hum Genet. 2005, 118: 179-184. 10.1007/s00439-005-0034-8.

    PubMed  Google Scholar 

  5. 5.

    Arnar DO, Thorvaldsson S, Manolio TA, Thorgeirsson G, Kristjansson K, Hakonarson H, Stefansson K: Familial aggregation of atrial fibrillation in Iceland. Eur Heart J. 2006, 27: 708-712. 10.1093/eurheartj/ehi727.

    PubMed  Google Scholar 

  6. 6.

    Christophersen IE, Ravn LS, Budtz-Joergensen E, Skytthe A, Haunsoe S, Svendsen JH, Christensen K: Familial aggregation of atrial fibrillation: a study in Danish twins. Circ Arrhythm Electrophysiol. 2009, 2: 378-383. 10.1161/CIRCEP.108.786665.

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Lubitz SA, Yin X, Fontes JD, Magnani JW, Rienstra M, Pai M, Villalon ML, Vasan RS, Pencina MJ, Levy D, Larson MG, Ellinor PT, Benjamin EJ: Association between familial atrial fibrillation and risk of new-onset atrial fibrillation. JAMA. 2010, 304: 2263-2269. 10.1001/jama.2010.1690.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Mahida S, Lubitz SA, Rienstra M, Milan DJ, Ellinor PT: Monogenic atrial fibrillation as pathophysiological paradigms. Cardiovasc Res. 2011, 89: 692-700. 10.1093/cvr/cvq381.

    CAS  PubMed  Google Scholar 

  9. 9.

    Olesen MS, Bentzen BH, Nielsen JB, Steffensen AB, David JP, Jabbari J, Jensen HK, Haunso S, Svendsen JH, Schmitt N: Mutations in the potassium channel subunit KCNE1 are associated with early-onset familial atrial fibrillation. BMC Med Genet. 2012, 13: 24.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Nattel S: New ideas about atrial fibrillation 50 years on. Nature. 2002, 415: 219-226. 10.1038/415219a.

    CAS  PubMed  Google Scholar 

  11. 11.

    Shan J, Xie W, Betzenhauser M, Reiken S, Chen BX, Wronska A, Marks AR: Calcium leak through ryanodine receptors leads to atrial fibrillation in 3 mouse models of catecholaminergic polymorphic ventricular tachycardia. Circ Res. 2012, 111: 708-717. 10.1161/CIRCRESAHA.112.273342.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sigurdsson A, Jonasdottir A, Baker A, Thorleifsson G, Kristjansson K, Palsson A, Blondal T, Sulem P, Backman VM, Hardarson GA, Palsdottir E, Helgason A, Sigurjonsdottir R, Sverrisson JT, Kostulas K, Ng MC, Baum L, So WY, Wong KS, Chan JC, Furie KL, Greenberg SM, Sale M, Kelly P, MacRae CA, et al: Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007, 448: 353-357. 10.1038/nature06007.

    CAS  PubMed  Google Scholar 

  13. 13.

    Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, Schnabel RB, Bis JC, Boerwinkle E, Sinner MF, Dehghan A, Lubitz SA, D'Agostino RB Sr, Lumley T, Ehret GB, Heeringa J, Aspelund T, Newton-Cheh C, Larson MG, Marciante KD, Soliman EZ, Rivadeneira F, Wang TJ, Eiriksdottir G, Levy D, Psaty BM, Li M, Chamberlain AM, Hofman A, Vasan RS, et al: Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009, 41: 879-881. 10.1038/ng.416.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Gudbjartsson DF, Holm H, Gretarsdottir S, Thorleifsson G, Walters GB, Thorgeirsson G, Gulcher J, Mathiesen EB, Njolstad I, Nyrnes A, Wilsgaard T, Hald EM, Hveem K, Stoltenberg C, Kucera G, Stubblefield T, Carter S, Roden D, Ng MC, Baum L, So WY, Wong KS, Chan JC, Gieger C, Wichmann HE, Gschwendtner A, Dichgans M, Kuhlenbaumer G, Berger K, Ringelstein EB, et al: A sequence variant in ZFHX3 on 16q22 associates with atrial fibrillation and ischemic stroke. Nat Genet. 2009, 41: 876-878. 10.1038/ng.417.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ellinor PT, Lunetta KL, Glazer NL, Pfeufer A, Alonso A, Chung MK, Sinner MF, de Bakker PI, Mueller M, Lubitz SA, Fox E, Darbar D, Smith NL, Smith JD, Schnabel R, Soliman EZ, Rice K, Van Wagoner DR, Beckmann BM, van Noord C, Wang K, Ehret GB, Rotter JI, Hazen S, Steinbeck G, Makino S, Nelis M, Milan DJ, Perz S, Esko T, et al: Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet. 2010, 42: 240-244. 10.1038/ng.537.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, Arking DE, Muller-Nurasyid M, Krijthe BP, Lubitz SA, Bis JC, Chung MK, Dorr M, Ozaki K, Roberts JD, Smith JG, Pfeufer A, Sinner MF, Lohman K, Ding J, Smith NL, Smith JD, Rienstra M, Rice KM, Van Wagoner DR, Magnani JW, Wakili R, Clauss S, Rotter JI, Steinbeck G, et al: Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012, 44.

    Google Scholar 

  17. 17.

    Wakili R, Voigt N, Kaab S, Dobrev D, Nattel S: Recent advances in the molecular pathophysiology of atrial fibrillation. J Clin Invest. 2011, 121: 2955-2968. 10.1172/JCI46315.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Dobrev D, Carlsson L, Nattel S: Novel molecular targets for atrial fibrillation therapy. Nat Rev Drug Discov. 2012, 11: 275-291. 10.1038/nrd3682.

    CAS  PubMed  Google Scholar 

  19. 19.

    Fuster V, Ryden LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA, Halperin JL, Kay GN, Le Huezey JY, Lowe JE, Olsson SB, Prystowsky EN, Tamargo JL, Wann LS, Smith SC, Priori SG, Estes NA, Ezekowitz MD, Jackman WM, January CT, Page RL, Slotwiner DJ, Stevenson WG, Tracy CM, Jacobs AK, Anderson JL, Albert N, Buller CE, Creager MA, Ettinger SM, et al: 2011 ACCF/AHA/HRS focused updates incorporated into the ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2011, 123: e269-367. 10.1161/CIR.0b013e318214876d.

    PubMed  Google Scholar 

  20. 20.

    Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, Ernst S, Van Gelder IC, Al-Attar N, Hindricks G, Prendergast B, Heidbuchel H, Alfieri O, Angelini A, Atar D, Colonna P, De Caterina R, De Sutter J, Goette A, Gorenek B, Heldal M, Hohloser SH, Kolh P, Le Heuzey JY, Ponikowski P, Rutten FH: Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J. 2010, 31: 2369-2429.

    PubMed  Google Scholar 

  21. 21.

    Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford MJ: Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA. 2001, 285: 2864-2870. 10.1001/jama.285.22.2864.

    CAS  PubMed  Google Scholar 

  22. 22.

    Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ: Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation. Chest. 2010, 137: 263-272. 10.1378/chest.09-1584.

    PubMed  Google Scholar 

  23. 23.

    Fang MC, Go AS, Chang Y, Borowsky LH, Pomernacki NK, Udaltsova N, Singer DE: A new risk scheme to predict warfarin-associated hemorrhage: The ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study. J Am Coll Cardiol. 2011, 58: 395-401. 10.1016/j.jacc.2011.03.031.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJ, Lip GY: A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest. 2010, 138: 1093-1100. 10.1378/chest.10-0134.

    PubMed  Google Scholar 

  25. 25.

    Calkins H, Kuck KH, Cappato R, Brugada J, Camm AJ, Chen SA, Crijns HJ, Damiano RJ, Davies DW, DiMarco J, Edgerton J, Ellenbogen K, Ezekowitz MD, Haines DE, Haissaguerre M, Hindricks G, Iesaka Y, Jackman W, Jalife J, Jais P, Kalman J, Keane D, Kim YH, Kirchhof P, Klein G, Kottkamp H, Kumagai K, Lindsay BD, Mansour M, Marchlinski FE, et al: 2012 HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design. J Interv Card Electrophysiol. 2012, 33: 171-257. 10.1007/s10840-012-9672-7.

    PubMed  Google Scholar 

  26. 26.

    Calkins H, Reynolds MR, Spector P, Sondhi M, Xu Y, Martin A, Williams CJ, Sledge I: Treatment of atrial fibrillation with antiarrhythmic drugs or radiofrequency ablation: two systematic literature reviews and meta-analyses. Circ Arrhythm Electrophysiol. 2009, 2: 349-361. 10.1161/CIRCEP.108.824789.

    CAS  PubMed  Google Scholar 

  27. 27.

    Freemantle N, Lafuente-Lafuente C, Mitchell S, Eckert L, Reynolds M: Mixed treatment comparison of dronedarone, amiodarone, sotalol, flecainide, and propafenone, for the management of atrial fibrillation. Europace. 2011, 13: 329-345. 10.1093/europace/euq450.

    PubMed  Google Scholar 

  28. 28.

    Lafuente-Lafuente C, Mouly S, Longas-Tejero MA, Bergmann JF: Antiarrhythmics for maintaining sinus rhythm after cardioversion of atrial fibrillation. Cochrane Database Syst Rev. 2007, 4: CD005049.

    PubMed  Google Scholar 

  29. 29.

    van Walraven C, Jennings A, Oake N, Fergusson D, Forster AJ: Effect of study setting on anticoagulation control: a systematic review and metaregression. Chest. 2006, 129: 1155-1166. 10.1378/chest.129.5.1155.

    PubMed  Google Scholar 

  30. 30.

    Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, Dudley JT, Ormond KE, Pavlovic A, Morgan AA, Pushkarev D, Neff NF, Hudgins L, Gong L, Hodges LM, Berlin DS, Thorn CF, Sangkuhl K, Hebert JM, Woon M, Sagreiya H, Whaley R, Knowles JW, Chou MF, Thakuria JV, Rosenbaum AM, Zaranek AW, Church GM, Greely HT, Quake SR, et al: Clinical assessment incorporating a personal genome. Lancet. 2010, 375: 1525-1535. 10.1016/S0140-6736(10)60452-7.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Higashi MK, Veenstra DL, Kondo LM, Wittkowsky AK, Srinouanprachanh SL, Farin FM, Rettie AE: Association between CYP2C9 genetic variants and anticoagulation-related outcomes during warfarin therapy. JAMA. 2002, 287: 1690-1698. 10.1001/jama.287.13.1690.

    CAS  PubMed  Google Scholar 

  32. 32.

    Rieder MJ, Reiner AP, Gage BF, Nickerson DA, Eby CS, McLeod HL, Blough DK, Thummel KE, Veenstra DL, Rettie AE: Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. N Engl J Med. 2005, 352: 2285-2293. 10.1056/NEJMoa044503.

    CAS  PubMed  Google Scholar 

  33. 33.

    Lubitz SA, Scott SA, Rothlauf EB, Agarwal A, Peter I, Doheny D, Van Der Zee S, Jaremko M, Yoo C, Desnick RJ, Halperin JL: Comparative performance of gene-based warfarin dosing algorithms in a multiethnic population. J Thromb Haemost. 2010, 8: 1018-1026.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Klein TE, Altman RB, Eriksson N, Gage BF, Kimmel SE, Lee MT, Limdi NA, Page D, Roden DM, Wagner MJ, Caldwell MD, Johnson JA: Estimation of the warfarin dose with clinical and pharmacogenetic data. N Engl J Med. 2009, 360: 753-764.

    CAS  PubMed  Google Scholar 

  35. 35.

    US Food and Drug Administration: FDA approves updated warfarin (Coumadin) prescribing information. Press release of the Food and Drug Administration, August 16, 2007. 2007, Washington, DC: FDA

    Google Scholar 

  36. 36.

    Katsnelson M, Sacco RL, Moscucci M: Progress for stroke prevention with atrial fibrillation: emergence of alternative oral anticoagulants. Circulation. 2012, 125: 1577-1583. 10.1161/CIR.0b013e31825498e8.

    PubMed  Google Scholar 

  37. 37.

    Holmes DR, Reddy VY, Turi ZG, Doshi SK, Sievert H, Buchbinder M, Mullin CM, Sick P: Percutaneous closure of the left atrial appendage versus warfarin therapy for prevention of stroke in patients with atrial fibrillation: a randomised non-inferiority trial. Lancet. 2009, 374: 534-542. 10.1016/S0140-6736(09)61343-X.

    CAS  PubMed  Google Scholar 

  38. 38.

    Wieloch M, Sjalander A, Frykman V, Rosenqvist M, Eriksson N, Svensson PJ: Anticoagulation control in Sweden: reports of time in therapeutic range, major bleeding, and thrombo-embolic complications from the national quality registry AuriculA. Eur Heart J. 2011, 32: 2282-2289. 10.1093/eurheartj/ehr134.

    CAS  PubMed  Google Scholar 

  39. 39.

    Parvez B, Chopra N, Rowan S, Vaglio JC, Muhammad R, Roden DM, Darbar D: A common beta1-adrenergic receptor polymorphism predicts favorable response to rate-control therapy in atrial fibrillation. J Am Coll Cardiol. 2012, 59: 49-56. 10.1016/j.jacc.2011.08.061.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Sauer AJ, Newton-Cheh C: Clinical and genetic determinants of torsade de pointes risk. Circulation. 2012, 125: 1684-1694. 10.1161/CIRCULATIONAHA.111.080887.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Jamshidi Y, Nolte IM, Dalageorgou C, Zheng D, Johnson T, Bastiaenen R, Ruddy S, Talbott D, Norris KJ, Snieder H, George AL, Marshall V, Shakir S, Kannankeril PJ, Munroe PB, Camm AJ, Jeffery S, Roden DM, Behr ER: Common variation in the NOS1AP gene is associated with drug-induced qt prolongation and ventricular arrhythmia. J Am Coll Cardiol. 2012, 60: 841-850. 10.1016/j.jacc.2012.03.031.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Roberts JD, Gollob MH: Impact of genetic discoveries on the classification of lone atrial fibrillation. J Am Coll Cardiol. 2010, 55: 705-712. 10.1016/j.jacc.2009.12.005.

    CAS  PubMed  Google Scholar 

  43. 43.

    Husser D, Adams V, Piorkowski C, Hindricks G, Bollmann A: Chromosome 4q25 variants and atrial fibrillation recurrence after catheter ablation. J Am Coll Cardiol. 2010, 55: 747-753. 10.1016/j.jacc.2009.11.041.

    CAS  PubMed  Google Scholar 

  44. 44.

    Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D'Agostino RB Sr, Newton-Cheh C, Yamamoto JF, Magnani JW, Tadros TM, Kannel WB, Wang TJ, Ellinor PT, Wolf PA, Vasan RS, Benjamin EJ: Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009, 373: 739-745. 10.1016/S0140-6736(09)60443-8.

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Schnabel RB, Aspelund T, Li G, Sullivan LM, Suchy-Dicey A, Harris TB, Pencina MJ, D'Agostino RB Sr, Levy D, Kannel WB, Wang TJ, Kronmal RA, Wolf PA, Burke GL, Launer LJ, Vasan RS, Psaty BM, Benjamin EJ, Gudnason V, Heckbert SR: Validation of an atrial fibrillation risk algorithm in whites and African Americans. Arch Intern Med. 2010, 170: 1909-1917. 10.1001/archinternmed.2010.434.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Chamberlain AM, Agarwal SK, Folsom AR, Soliman EZ, Chambless LE, Crow R, Ambrose M, Alonso A: A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk in Communities [ARIC] study). Am J Cardiol. 2011, 107: 85-91. 10.1016/j.amjcard.2010.08.049.

    PubMed  PubMed Central  Google Scholar 

  47. 47.

    Smith JG, Newton-Cheh C, Almgren P, Melander O, Platonov PG: Genetic polymorphisms for estimating risk of atrial fibrillation in the general population: a prospective study. Arch Intern Med. 2012, 172: 742-744. 10.1001/archinternmed.2012.786.

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P: Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004, 159: 882-890. 10.1093/aje/kwh101.

    PubMed  Google Scholar 

  49. 49.

    Brunham LR, Hayden MR: Medicine. Whole-genome sequencing: the new standard of care?. Science. 2012, 336: 1112-1113. 10.1126/science.1220967.

    CAS  PubMed  Google Scholar 

  50. 50.

    Lubitz SA, Sinner MF, Lunetta KL, Makino S, Pfeufer A, Rahman R, Veltman CE, Barnard J, Bis JC, Danik SP, Sonni A, Shea MA, Del Monte F, Perz S, Muller M, Peters A, Greenberg SM, Furie KL, van Noord C, Boerwinkle E, Stricker BH, Witteman J, Smith JD, Chung MK, Heckbert SR, Benjamin EJ, Rosand J, Arking DE, Alonso A, Kaab S, et al: Independent susceptibility markers for atrial fibrillation on chromosome 4q25. Circulation. 2010, 122: 976-984. 10.1161/CIRCULATIONAHA.109.886440.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Evans DM, Visscher PM, Wray NR: Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum Mol Genet. 2009, 18: 3525-3531. 10.1093/hmg/ddp295.

    CAS  PubMed  Google Scholar 

  52. 52.

    Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009, 460: 748-752.

    CAS  PubMed  Google Scholar 

  53. 53.

    Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, Ferreira T, Wood AR, Weyant RJ, Segre AV, Speliotes EK, Wheeler E, Soranzo N, Park JH, Yang J, Gudbjartsson D, Heard-Costa NL, Randall JC, Qi L, Vernon Smith A, Magi R, Pastinen T, Liang L, Heid IM, Luan J, Thorleifsson G, et al: Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010, 467: 832-838. 10.1038/nature09410.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Janssens AC, Aulchenko YS, Elefante S, Borsboom GJ, Steyerberg EW, van Duijn CM: Predictive testing for complex diseases using multiple genes: fact or fiction?. Genet Med. 2006, 8: 395-400. 10.1097/01.gim.0000229689.18263.f4.

    PubMed  Google Scholar 

  55. 55.

    Pencina MJ, D'Agostino RB Sr, Steyerberg EW: Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011, 30: 11-21. 10.1002/sim.4085.

    PubMed  Google Scholar 

  56. 56.

    Kirchhof P, Lip GY, Van Gelder IC, Bax J, Hylek E, Kaab S, Schotten U, Wegscheider K, Boriani G, Brandes A, Ezekowitz M, Diener H, Haegeli L, Heidbuchel H, Lane D, Mont L, Willems S, Dorian P, Aunes-Jansson M, Blomstrom-Lundqvist C, Borentain M, Breitenstein S, Brueckmann M, Cater N, Clemens A, Dobrev D, Dubner S, Edvardsson NG, Friberg L, Goette A, et al: Comprehensive risk reduction in patients with atrial fibrillation: emerging diagnostic and therapeutic options--a report from the 3rd Atrial Fibrillation Competence NETwork/European Heart Rhythm Association consensus conference. Europace. 2012, 14: 8-27. 10.1093/europace/eur241.

    PubMed  Google Scholar 

  57. 57.

    Lubitz SA, Benjamin EJ, Ruskin JN, Fuster V, Ellinor PT: Challenges in the classification of atrial fibrillation. Nat Rev Cardiol. 2010, 7: 451-460. 10.1038/nrcardio.2010.86.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Goldsmith L, Jackson L, O'Connor A, Skirton H: Direct-to-consumer genomic testing: systematic review of the literature on user perspectives. Eur J Hum Genet. 2012, 20: 811-816. 10.1038/ejhg.2012.18.

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Brett GR, Metcalfe SA, Amor DJ, Halliday JL: An exploration of genetic health professionals' experience with direct-to-consumer genetic testing in their clinical practice. Eur J Hum Genet. 2012, 20: 825-830. 10.1038/ejhg.2012.13.

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Ackerman MJ, Priori SG, Willems S, Berul C, Brugada R, Calkins H, Camm AJ, Ellinor PT, Gollob M, Hamilton R, Hershberger RE, Judge DP, Le Marec H, McKenna WJ, Schulze-Bahr E, Semsarian C, Towbin JA, Watkins H, Wilde A, Wolpert C, Zipes DP: HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA). Heart Rhythm. 2011, 8: 1308-1339. 10.1016/j.hrthm.2011.05.020.

    PubMed  Google Scholar 

  61. 61.

    Chen YH, Xu SJ, Bendahhou S, Wang XL, Wang Y, Xu WY, Jin HW, Sun H, Su XY, Zhuang QN, Yang YQ, Li YB, Liu Y, Xu HJ, Li XF, Ma N, Mou CP, Chen Z, Barhanin J, Huang W: KCNQ1 gain-of-function mutation in familial atrial fibrillation. Science. 2003, 299: 251-254. 10.1126/science.1077771.

    CAS  PubMed  Google Scholar 

  62. 62.

    Das S, Makino S, Melman YF, Shea MA, Goyal SB, Rosenzweig A, Macrae CA, Ellinor PT: Mutation in the S3 segment of KCNQ1 results in familial lone atrial fibrillation. Heart Rhythm. 2009, 6: 1146-1153. 10.1016/j.hrthm.2009.04.015.

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Hong K, Piper DR, Diaz-Valdecantos A, Brugada J, Oliva A, Burashnikov E, Santos-de-Soto J, Grueso-Montero J, Diaz-Enfante E, Brugada P, Sachse F, Sanguinetti MC, Brugada R: De novo KCNQ1 mutation responsible for atrial fibrillation and short QT syndrome in utero. Cardiovasc Res. 2005, 68: 433-440. 10.1016/j.cardiores.2005.06.023.

    CAS  PubMed  Google Scholar 

  64. 64.

    Otway R, Vandenberg JI, Guo G, Varghese A, Castro ML, Liu J, Zhao J, Bursill JA, Wyse KR, Crotty H, Baddeley O, Walker B, Kuchar D, Thorburn C, Fatkin D: Stretch-sensitive KCNQ1 mutation A link between genetic and environmental factors in the pathogenesis of atrial fibrillation?. J Am Coll Cardiol. 2007, 49: 578-586. 10.1016/j.jacc.2006.09.044.

    CAS  PubMed  Google Scholar 

  65. 65.

    Lundby A, Ravn LS, Svendsen JH, Olesen SP, Schmitt N: KCNQ1 mutation Q147R is associated with atrial fibrillation and prolonged QT interval. Heart Rhythm. 2007, 4: 1532-1541. 10.1016/j.hrthm.2007.07.022.

    PubMed  Google Scholar 

  66. 66.

    Schnabel RB, Kerr KF, Lubitz SA, Alkylbekova EL, Marcus GM, Sinner MF, Magnani JW, Wolf PA, Deo R, Lloyd-Jones DM, Lunetta KL, Mehra R, Levy D, Fox ER, Arking DE, Mosley TH, Muller-Nurasyid M, Young TR, Wichmann HE, Seshadri S, Farlow DN, Rotter JI, Soliman EZ, Glazer NL, Wilson JG, Breteler MM, Sotoodehnia N, Newton-Cheh C, Kaab S, Ellinor PT, et al: Large-scale candidate gene analysis in whites and African Americans identifies IL6R polymorphism in relation to atrial fibrillation: the National Heart, Lung, and Blood Institute's Candidate Gene Association Resource (CARe) project. Circ Cardiovasc Genet. 2011, 4: 557-564. 10.1161/CIRCGENETICS.110.959197.

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Lai LP, Su MJ, Yeh HM, Lin JL, Chiang FT, Hwang JJ, Hsu KL, Tseng CD, Lien WP, Tseng YZ, Huang SK: Association of the human minK gene 38G allele with atrial fibrillation: evidence of possible genetic control on the pathogenesis of atrial fibrillation. Am Heart J. 2002, 144: 485-490. 10.1067/mhj.2002.123573.

    CAS  PubMed  Google Scholar 

  68. 68.

    Fatini C, Sticchi E, Genuardi M, Sofi F, Gensini F, Gori AM, Lenti M, Michelucci A, Abbate R, Gensini GF: Analysis of minK and eNOS genes as candidate loci for predisposition to non-valvular atrial fibrillation. Eur Heart J. 2006, 27: 1712-1718. 10.1093/eurheartj/ehl087.

    CAS  PubMed  Google Scholar 

  69. 69.

    Prystupa A, Dzida G, Myslinski W, Malaj G, Lorenc T: MinK gene polymorphism in the pathogenesis of lone atrial fibrillation. Kardiol Pol. 2006, 64: 1205-1211; discussion 1212-1203.

    PubMed  Google Scholar 

  70. 70.

    Yang Y, Xia M, Jin Q, Bendahhou S, Shi J, Chen Y, Liang B, Lin J, Liu Y, Liu B, Zhou Q, Zhang D, Wang R, Ma N, Su X, Niu K, Pei Y, Xu W, Chen Z, Wan H, Cui J, Barhanin J: Identification of a KCNE2 gain-of-function mutation in patients with familial atrial fibrillation. Am J Hum Genet. 2004, 75: 899-905. 10.1086/425342.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Ravn LS, Aizawa Y, Pollevick GD, Hofman-Bang J, Cordeiro JM, Dixen U, Jensen G, Wu Y, Burashnikov E, Haunso S, Guerchicoff A, Hu D, Svendsen JH, Christiansen M, Antzelevitch C: Gain of function in IKs secondary to a mutation in KCNE5 associated with atrial fibrillation. Heart Rhythm. 2008, 5: 427-435. 10.1016/j.hrthm.2007.12.019.

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Hong K, Bjerregaard P, Gussak I, Brugada R: Short QT syndrome and atrial fibrillation caused by mutation in KCNH2. J Cardiovasc Electrophysiol. 2005, 16: 394-396. 10.1046/j.1540-8167.2005.40621.x.

    PubMed  Google Scholar 

  73. 73.

    Sinner MF, Pfeufer A, Akyol M, Beckmann BM, Hinterseer M, Wacker A, Perz S, Sauter W, Illig T, Nabauer M, Schmitt C, Wichmann HE, Schomig A, Steinbeck G, Meitinger T, Kaab S: The non-synonymous coding IKr-channel variant KCNH2-K897T is associated with atrial fibrillation: results from a systematic candidate gene-based analysis of KCNH2 (HERG). Eur Heart J. 2008, 29: 907-914. 10.1093/eurheartj/ehm619.

    CAS  PubMed  Google Scholar 

  74. 74.

    Xia M, Jin Q, Bendahhou S, He Y, Larroque MM, Chen Y, Zhou Q, Yang Y, Liu Y, Liu B, Zhu Q, Zhou Y, Lin J, Liang B, Li L, Dong X, Pan Z, Wang R, Wan H, Qiu W, Xu W, Eurlings P, Barhanin J: A Kir2.1 gain-of-function mutation underlies familial atrial fibrillation. Biochem Biophys Res Commun. 2005, 332: 1012-1019. 10.1016/j.bbrc.2005.05.054.

    CAS  PubMed  Google Scholar 

  75. 75.

    Olson TM, Alekseev AE, Liu XK, Park S, Zingman LV, Bienengraeber M, Sattiraju S, Ballew JD, Jahangir A, Terzic A: Kv1.5 channelopathy due to KCNA5 loss-of-function mutation causes human atrial fibrillation. Hum Mol Genet. 2006, 15: 2185-2191. 10.1093/hmg/ddl143.

    CAS  PubMed  Google Scholar 

  76. 76.

    McNair WP, Ku L, Taylor MR, Fain PR, Dao D, Wolfel E, Mestroni L: SCN5A mutation associated with dilated cardiomyopathy, conduction disorder, and arrhythmia. Circulation. 2004, 110: 2163-2167. 10.1161/01.CIR.0000144458.58660.BB.

    CAS  PubMed  Google Scholar 

  77. 77.

    Olson TM, Michels VV, Ballew JD, Reyna SP, Karst ML, Herron KJ, Horton SC, Rodeheffer RJ, Anderson JL: Sodium channel mutations and susceptibility to heart failure and atrial fibrillation. JAMA. 2005, 293: 447-454. 10.1001/jama.293.4.447.

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Laitinen-Forsblom PJ, Makynen P, Makynen H, Yli-Mayry S, Virtanen V, Kontula K, Aalto-Setala K: SCN5A mutation associated with cardiac conduction defect and atrial arrhythmias. J Cardiovasc Electrophysiol. 2006, 17: 480-485. 10.1111/j.1540-8167.2006.00411.x.

    PubMed  Google Scholar 

  79. 79.

    Ellinor PT, Nam EG, Shea MA, Milan DJ, Ruskin JN, MacRae CA: Cardiac sodium channel mutation in atrial fibrillation. Heart Rhythm. 2008, 5: 99-105. 10.1016/j.hrthm.2007.09.015.

    PubMed  Google Scholar 

  80. 80.

    Makiyama T, Akao M, Shizuta S, Doi T, Nishiyama K, Oka Y, Ohno S, Nishio Y, Tsuji K, Itoh H, Kimura T, Kita T, Horie M: A novel SCN5A gain-of-function mutation M1875T associated with familial atrial fibrillation. J Am Coll Cardiol. 2008, 52: 1326-1334. 10.1016/j.jacc.2008.07.013.

    CAS  PubMed  Google Scholar 

  81. 81.

    Li Q, Huang H, Liu G, Lam K, Rutberg J, Green MS, Birnie DH, Lemery R, Chahine M, Gollob MH: Gain-of-function mutation of Nav1.5 in atrial fibrillation enhances cellular excitability and lowers the threshold for action potential firing. Biochem Biophys Res Commun. 2009, 380: 132-137. 10.1016/j.bbrc.2009.01.052.

    CAS  PubMed  Google Scholar 

  82. 82.

    Watanabe H, Darbar D, Kaiser DW, Jiramongkolchai K, Chopra S, Donahue BS, Kannankeril PJ, Roden DM: Mutations in sodium channel beta1- and beta2-subunits associated with atrial fibrillation. Circ Arrhythm Electrophysiol. 2009, 2: 268-275. 10.1161/CIRCEP.108.779181.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Ellinor PT, Shin JT, Moore RK, Yoerger DM, MacRae CA: Locus for atrial fibrillation maps to chromosome 6q14-16. Circulation. 2003, 107: 2880-2883. 10.1161/01.CIR.0000077910.80718.49.

    PubMed  Google Scholar 

  84. 84.

    Firouzi M, Ramanna H, Kok B, Jongsma HJ, Koeleman BP, Doevendans PA, Groenewegen WA, Hauer RN: Association of human connexin40 gene polymorphisms with atrial vulnerability as a risk factor for idiopathic atrial fibrillation. Circ Res. 2004, 95: e29-33. 10.1161/01.RES.0000141134.64811.0a.

    CAS  PubMed  Google Scholar 

  85. 85.

    Gollob MH, Jones DL, Krahn AD, Danis L, Gong XQ, Shao Q, Liu X, Veinot JP, Tang AS, Stewart AF, Tesson F, Klein GJ, Yee R, Skanes AC, Guiraudon GM, Ebihara L, Bai D: Somatic mutations in the connexin 40 gene (GJA5) in atrial fibrillation. N Engl J Med. 2006, 354: 2677-2688. 10.1056/NEJMoa052800.

    CAS  PubMed  Google Scholar 

  86. 86.

    Juang JM, Chern YR, Tsai CT, Chiang FT, Lin JL, Hwang JJ, Hsu KL, Tseng CD, Tseng YZ, Lai LP: The association of human connexin 40 genetic polymorphisms with atrial fibrillation. Int J Cardiol. 2007, 116: 107-112. 10.1016/j.ijcard.2006.03.037.

    PubMed  Google Scholar 

  87. 87.

    Wirka RC, Gore S, Van Wagoner DR, Arking DE, Lubitz SA, Lunetta KL, Benjamin EJ, Alonso A, Ellinor PT, Barnard J, Chung MK, Smith JD: A common connexin-40 gene promoter variant affects connexin-40 expression in human atria and is associated with atrial fibrillation. Circ Arrhythm Electrophysiol. 2011, 4: 87-93. 10.1161/CIRCEP.110.959726.

    CAS  PubMed  Google Scholar 

  88. 88.

    Volders PG, Zhu Q, Timmermans C, Eurlings PM, Su X, Arens YH, Li L, Jongbloed RJ, Xia M, Rodriguez LM, Chen YH: Mapping a novel locus for familial atrial fibrillation on chromosome 10p11-q21. Heart Rhythm. 2007, 4: 469-475. 10.1016/j.hrthm.2006.12.023.

    PubMed  Google Scholar 

  89. 89.

    Cunha SR, Hund TJ, Hashemi S, Voigt N, Li N, Wright P, Koval O, Li J, Gudmundsson H, Gumina RJ, Karck M, Schott J-J, Probst V, Le Marec H, Anderson ME, Dobrev D, Wehrens XHT, Mohler PJ: Defects in ankyrin-based membrane protein targeting pathways underlie atrial fibrillation. Circulation. 2011, 124: 1212-1222. 10.1161/CIRCULATIONAHA.111.023986.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Schott JJ, Charpentier F, Peltier S, Foley P, Drouin E, Bouhour JB, Donnelly P, Vergnaud G, Bachner L, Moisan JP: Mapping of a gene for long QT syndrome to chromosome 4q25-27. Am J Hum Genet. 1995, 57: 1114-1122.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Brugada R, Tapscott T, Czernuszewicz GZ, Marian AJ, Iglesias A, Mont L, Brugada J, Girona J, Domingo A, Bachinski LL, Roberts R: Identification of a genetic locus for familial atrial fibrillation. N Engl J Med. 1997, 336: 905-911. 10.1056/NEJM199703273361302.

    CAS  PubMed  Google Scholar 

  92. 92.

    Fatkin D, MacRae C, Sasaki T, Wolff MR, Porcu M, Frenneaux M, Atherton J, Vidaillet HJ, Spudich S, De Girolami U, Seidman JG, Seidman C, Muntoni F, Muehle G, Johnson W, McDonough B: Missense mutations in the rod domain of the lamin A/C gene as causes of dilated cardiomyopathy and conduction-system disease. N Engl J Med. 1999, 341: 1715-1724. 10.1056/NEJM199912023412302.

    CAS  PubMed  Google Scholar 

  93. 93.

    Sebillon P, Bouchier C, Bidot LD, Bonne G, Ahamed K, Charron P, Drouin-Garraud V, Millaire A, Desrumeaux G, Benaiche A, Charniot JC, Schwartz K, Villard E, Komajda M: Expanding the phenotype of LMNA mutations in dilated cardiomyopathy and functional consequences of these mutations. J Med Genet. 2003, 40: 560-567. 10.1136/jmg.40.8.560.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Brauch KM, Chen LY, Olson TM: Comprehensive mutation scanning of LMNA in 268 patients with lone atrial fibrillation. Am J Cardiol. 2009, 103: 1426-1428. 10.1016/j.amjcard.2009.01.354.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Pan H, Richards AA, Zhu X, Joglar JA, Yin HL, Garg V: A novel mutation in LAMIN A/C is associated with isolated early-onset atrial fibrillation and progressive atrioventricular block followed by cardiomyopathy and sudden cardiac death. Heart Rhythm. 2009, 6: 707-710. 10.1016/j.hrthm.2009.01.037.

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Oberti C, Wang L, Li L, Dong J, Rao S, Du W, Wang Q: Genome-wide linkage scan identifies a novel genetic locus on chromosome 5p13 for neonatal atrial fibrillation associated with sudden death and variable cardiomyopathy. Circulation. 2004, 110: 3753-3759. 10.1161/01.CIR.0000150333.87176.C7.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Zhang X, Chen S, Yoo S, Chakrabarti S, Zhang T, Ke T, Oberti C, Yong SL, Fang F, Li L, de la Fuente R, Wang L, Chen Q, Wang QK: Mutation in nuclear pore component NUP155 leads to atrial fibrillation and early sudden cardiac death. Cell. 2008, 135: 1017-1027. 10.1016/j.cell.2008.10.022.

    CAS  PubMed  Google Scholar 

  98. 98.

    Tsai CT, Lai LP, Lin JL, Chiang FT, Hwang JJ, Ritchie MD, Moore JH, Hsu KL, Tseng CD, Liau CS, Tseng YZ: Renin-angiotensin system gene polymorphisms and atrial fibrillation. Circulation. 2004, 109: 1640-1646. 10.1161/01.CIR.0000124487.36586.26.

    CAS  PubMed  Google Scholar 

  99. 99.

    Wang QS, Li YG, Chen XD, Yu JF, Wang J, Sun J, Lu SB, Jin L, Wang XF: Angiotensinogen polymorphisms and acquired atrial fibrillation in Chinese. J Electrocardiol. 2010, 43: 373-377. 10.1016/j.jelectrocard.2009.09.009.

    PubMed  Google Scholar 

  100. 100.

    Bedi M, McNamara D, London B, Schwartzman D: Genetic susceptibility to atrial fibrillation in patients with congestive heart failure. Heart Rhythm. 2006, 3: 808-812. 10.1016/j.hrthm.2006.03.002.

    PubMed  Google Scholar 

  101. 101.

    Fatini C, Sticchi E, Gensini F, Gori AM, Marcucci R, Lenti M, Michelucci A, Genuardi M, Abbate R, Gensini GF: Lone and secondary nonvalvular atrial fibrillation: role of a genetic susceptibility. Int J Cardiol. 2007, 120: 59-65. 10.1016/j.ijcard.2006.08.079.

    PubMed  Google Scholar 

  102. 102.

    Watanabe H, Kaiser DW, Makino S, MacRae CA, Ellinor PT, Wasserman BS, Kannankeril PJ, Donahue BS, Roden DM, Darbar D: ACE I/D polymorphism associated with abnormal atrial and atrioventricular conduction in lone atrial fibrillation and structural heart disease: implications for electrical remodeling. Heart Rhythm. 2009, 6: 1327-1332. 10.1016/j.hrthm.2009.05.014.

    PubMed  PubMed Central  Google Scholar 

  103. 103.

    Hodgson-Zingman DM, Karst ML, Zingman LV, Heublein DM, Darbar D, Herron KJ, Ballew JD, de Andrade M, Burnett JC, Olson TM: Atrial natriuretic peptide frameshift mutation in familial atrial fibrillation. N Engl J Med. 2008, 359: 158-165. 10.1056/NEJMoa0706300.

    CAS  PubMed  PubMed Central  Google Scholar 

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PTE is supported by NIH grants 1RO1HL092577, 1RO1HL104156, 5R21DA027021 and 1K24HL105780. SAL is supported by an AHA grant 12FTF11350014 and a Corrigan Minehan Innovation in Cardiovascular Research Spark Award.

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Correspondence to Steven A Lubitz.

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The authors declare that they have no competing interests.

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SAL and PTE were involved in drafting the manuscript and revising it critically for important intellectual content. Both gave final approval of the manuscript.

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Lubitz, S.A., Ellinor, P.T. Personalized medicine and atrial fibrillation: will it ever happen?. BMC Med 10, 155 (2012).

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  • Atrial fibrillation
  • genetics
  • personalized medicine
  • risk prediction
  • pharmacogenetics