Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Christophersen, Ingrid E.; Rienstra, Michiel; Roselli, Carolina; Yin, Xiaoyan; Geelhoed, Bastiaan; Barnard, John; Lin, Honghuang; Arking, Dan E.; Smith, Albert V.; Albert, Christine M.; Chaffin, Mark; Tucker, Nathan R.; Li, Molong; Klarin, Derek; Bihlmeyer, Nathan A.; Low, Siew Kee; Weeke, Peter; Müller‐Nurasyid, Martina; Smith, J. G.; Brody, Jennifer A.; Niemeijer, Maartje N.; Dörr, Marcus; Trompet, Stella; Huffman, Jennifer E.; Gustafsson, Stefan; Schurmann, Claudia; Kleber, Marcus E.; Lyytikäinen, Leo Pekka; Seppälä, Ilkka; Malik, Rainer; Horimoto, Andrea R V R; Pérez, Marco; Sinisalo, Juha; Aeschbacher, Stefanie; Thériault, Sébastien; Yao, Jie; Radmanesh, Farid; Weiß, Stefan; Teumer, Alexander; Choi, Seung Hoan; Weng, Lu Chen; Clauß, Sebastian; Deo, Rajat; Rader, Daniel J.; Shah, Svati H.; Sun, Albert Y.; Hopewell, Jemma C.; Debette, Stéphanie; Chauhan, Ganesh; Yang, Qiong; Worrall, Bradford B.; Paré, Guillaume; Kamatani, Yoichiro; Hagemeijer, Yanick; Verweij, Niek; Siland, Joylene E.; Kubo, Michiaki; Smith, Jonathan; Wagoner, David R. Van; Bis, Joshua C.; Perz, Siegfried; Psaty, Bruce M.; Ridker, Paul M.; Magnani, Jared W.; Harris, Tamara B.; Launer, Lenore J.; Shoemaker, M. Benjamin; Padmanabhan, Sandosh; Haessler, Jeffrey; Bartz, Traci M.; Waldenberger, Mélanie; Lichtner, Peter; Arendt, Marina; Krieger, José Eduardo; Kähönen, Mika; Risch, L.; Mansur, Alfredo José; Peters, Annette; Smith, Blair H.; Lind, Lars; Scott, Stuart A.; Lu, Yingchang; Böttinger, Erwin; Hernesniemi, Jussi; Lindgren, Cecilia M.; Wong, Jorge; Huang, Jie; Eskola, Markku; Morris, Andrew P.; Ford, Ian; Reiner, Alex P.; Delgado, Graciela; Chen, Lin Y.; Chen, Yii Der Ida; Sandhu, Roopinder K.; Li, Man; Boerwinkle, Eric; Eisele, Lewin; Lannfelt, Lars; Rost, Natalia S.; Anderson, Chris; Taylor, Kent D.; Campbell, Archie; Magnusson, Patrik K. E.; Porteous, David J.; Hocking, Lynne J.; Vlachopoulou, Efthymia; Pedersen, Nancy L.; Nikus, Kjell; Orho‐Melander, Marju; Hamsten, Anders; Heeringa, Jan; Denny, Joshua C.; Kriebel, Jennifer; Darbar, Dawood; Newton‐Cheh, Christopher; Shaffer, Christian M.; Macfarlane, Peter W.; Nöthen, Markus M.; Almgren, Peter; Huang, Paul L.; Sotoodehnia, Nona; Soliman, Elsayed Z.; Uitterlinden, André G.; Hofman, Albert; Franco, Oscar H.; Völker, Uwe; Jöckel, Karl Heinz; Sinner, Moritz F.; Lin, Henry J.; Guo, Xiuqing; Dichgans, Martin; Ingelsson, Erik; Kooperberg, Charles; Melander, Olle; Loos, Ruth J.F.; Laurikka, Jari; Conen, David; Rosand, Jonathan; Harst, Pim van der; Lokki, Marja Liisa; Kathiresan, Sekar; Pereira, Alexandre C.; Jukema, J. Wouter; Hayward, Caroline; Rotter, Jerome I.; März, Winfried; Lehtimäki, Terho; Stricker, Bruno H.; Chung, Mina K.; Felix, Stephan B.; Gudnason, Vilmundur; Alonso, Álvaro; Roden, Dan M.; Kääb, Stefan; Chasman, Daniel I.; Heckbert, Susan R.; Benjamin, Emelia J.; Tanaka, Toshihiro; Lunetta, Kathryn L.; Lubitz, Steven A.; Ellinor, Patrick T.

doi:10.1038/ng.3843

Cited by 292 publications

(274 citation statements)

References 39 publications

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“…At a locus centered on a cluster of sodium channel genes, we identified 3 regions that independently associate with AF within SCN10A , SCN5A and a third signal between both genes. At the previously described TBX5 locus, 8 we detected a novel independent signal close to TBX3 . Pairwise linkage disequilibrium (LD) estimates between the independent variants at both loci were extremely low (r 2 <0.03; Supplementary Table 7 ).…”

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confidence: 55%

“…In recent pre-publication results, Nielsen et al, reported 111 loci from 60,620 AF cases and more than 970,000 referents, 15 including more than 18,000 AF cases from our prior report. 8 We therefore performed a preliminary meta-analysis for the top loci in nonoverlapping participants from these two large efforts with a resulting total of over 93,000 AF cases and more than 1 million referents. In aggregate, we identified at least 134 distinct AFassociated loci ( Supplementary Table 16 ).…”

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confidence: 99%

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Multi-ethnic genome-wide association study for atrial fibrillation

et al. 2018

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Atrial fibrillation (AF) affects over 33 million individuals worldwide1 and has a complex heritability.2 We conducted the largest meta-analysis of genome-wide association studies for AF to date, consisting of over half a million individuals including 65,446 with AF. In total, we identified 97 loci significantly associated with AF including 67 of which were novel in a combined-ancestry analysis, and 3 in a European specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait loci (eQTL) analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.

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Multi-ethnic genome-wide association study for atrial fibrillation

et al. 2018

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“…The largest to date was recently led by our group and others in the Atrial Fibrillation Genetics Consortium or AFGen, and included about 18 000 individuals with AF and over 1 00 000 without, in which 23 genomic loci associated with AF were reported 6. The genes closest to the top signal at each locus implicate transcription factors, cardiac expressed ion channels, myocardial or cytoskeletal genes and other pathways in the pathogenesis of AF (figure 1).…”

Section: Genetic Mapping Of Afmentioning

confidence: 99%

Genomic basis of atrial fibrillation

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Anderson

Ellinor

et al. 2017

Heart

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Atrial fibrillation (AF) is a prevalent arrhythmia associated with substantial morbidity, mortality and costs. Available management strategies generally have limited efficacy and are associated with potential adverse effects. In part, the limited efficacy of approaches to managing AF reflect an incomplete understanding of the biological mechanisms underlying the arrhythmia, and only a partial understanding of how best to individualise management. Over the last several decades, a greater understanding of genome biology has led to recognition of a widespread genetic susceptibility to AF. Through genome-wide association studies, at least 30 genetic loci have been identified in association with AF, most of which implicate mechanisms not previously appreciated to be involved in the development of AF. We now recognise that AF is a polygenic condition, yet a great deal of work lies ahead to better understand the precise mechanisms by which genomic variation causes AF. Understanding the genetic basis of AF could provide a better understanding of AF mechanisms and cardiovascular biology, inform the management of patients through risk-guided approaches and facilitate the development of novel therapeutics.

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“…For example, the SCN10A locus that was shown to be associated with P wave duration in the current study 6 has also been robustly associated with AF. 10,11 Yet, although this framework is attractive, the complex relationship between P wavederived markers and AF complicates the process of relating the identified genetic variants to mechanisms of AF. This is, in part, related to the multifactorial nature of the arrhythmia.…”

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confidence: 99%

“…Other transcriptomic approaches, such as coexpression analysis as recently implemented by us in a functional follow up study of a locus identified for Brugada syndrome, may also provide functional hypotheses to be tested experimentally. 20 The progress being made in recent years in dissecting genetic factors contributing to AF, both by means of genetic studies in large cohorts of AF 10 and by the intermediate phenotype approach, is remarkable. With their large collaborative study on P wave indices as intermediate phenotypes, Christophersen et al 6 have made another important contribution along a journey that will eventually lead us through the maze of AF.…”

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confidence: 99%

Dissecting the Genetic Basis of the ECG as a Means of Understanding Mechanisms of Arrhythmia

Tadros

Coronel

Bezzina

2017

Circ Cardiovasc Genet

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T he surface ECG records the electrical potential of the heart at the surface of the body as the electrical impulse travels throughout the heart with each heartbeat. The P wave represents depolarization of the atria, which spreads from the sinoatrial node toward the atrioventricular node and from the right to the left atrium. The PR interval reflects the time the electrical impulse takes to travel from the sinus node through the atrioventricular node. The QRS complex represents the rapid depolarization of the ventricles, and the T wave represents the repolarization of the ventricles. See Article by Christophersen et alThe ECG is one of the most accessible and valuable diagnostic tests. It informs about the presence of cardiac chamber enlargement/hypertrophy, conduction disturbance, arrhythmia, myocardial infarction/ischemia, and even provides indications for electrolyte imbalance or drug intoxication. Since about a decade, researchers have turned to the ECG as a tool to dissect the genetic basis of cardiac electrical function with the idea that the biological processes that underlie or impinge on the different parameters of the ECG are mediators (intermediate phenotypes, endophenotypes) of arrhythmia risk.1 This concept, which in itself is clearly intuitive, is supported by population-based studies that demonstrated that ECG parameters are associated with risk of arrhythmia or sudden cardiac death.2 The approach is furthermore rationalized by the fact that ECG parameters have been shown to have a heritable component (reviewed in reference 3 ). The first ECG parameter that was studied in this way by the genome-wide association study (GWAS) approach was the QT interval 1 ; in fact the QT interval was one of the first traits to be studied at the outset of the GWAS era 10 years ago. 4 Facilitated by the availability of large cohorts with ECG recordings and the ability to measure ECG parameters reproducibly in an automated fashion, GWAS has since been applied to several ECG parameters in increasingly larger cohorts of individuals primarily of European descent with >150 loci being identified as modulators of these traits. 5In the current issue of Circulation: Cardiovascular Genetics, Christophersen et al 6 report findings from a GWAS that they conducted on parameters related to the P wave in 44 456 individuals originating from 12 cohort studies. These parameters were namely P wave duration, so far understudied by GWAS, and P wave terminal force, studied by GWAS for the first time. Concordant with the intermediate phenotype paradigm, and the relationship between these parameters and atrial electrophysiology 7 and risk for atrial fibrillation (AF), 8 these investigators posited that elucidating genetic variants related to these parameters will help in understanding atrial electrophysiology and possibly uncover genetic determinants and mechanisms of AF. The authors further postulate that this may in turn allow identification of new therapeutic targets for AF. They report 15 loci that include loci previously identified fo...

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Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Cited by 292 publications

References 39 publications

Multi-ethnic genome-wide association study for atrial fibrillation

Multi-ethnic genome-wide association study for atrial fibrillation

Genomic basis of atrial fibrillation

Dissecting the Genetic Basis of the ECG as a Means of Understanding Mechanisms of Arrhythmia

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