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Meta-Analysis
. 2018 Jun 11;50(9):1225-1233.
doi: 10.1038/s41588-018-0133-9.

Multi-ethnic genome-wide association study for atrial fibrillation

Carolina Roselli  1 Mark D Chaffin  1 Lu-Chen Weng  1   2 Stefanie Aeschbacher  3   4 Gustav Ahlberg  5   6   7 Christine M Albert  8 Peter Almgren  9 Alvaro Alonso  10 Christopher D Anderson  1   11 Krishna G Aragam  1   11 Dan E Arking  12 John Barnard  13 Traci M Bartz  14 Emelia J Benjamin  15   16   17 Nathan A Bihlmeyer  18 Joshua C Bis  19 Heather L Bloom  20 Eric Boerwinkle  21 Erwin B Bottinger  22   23 Jennifer A Brody  19 Hugh Calkins  24 Archie Campbell  25 Thomas P Cappola  26 John Carlquist  27   28 Daniel I Chasman  1   29 Lin Y Chen  30 Yii-Der Ida Chen  31 Eue-Keun Choi  32 Seung Hoan Choi  1 Ingrid E Christophersen  1   2   33 Mina K Chung  13 John W Cole  34   35 David Conen  3   4   36 James Cook  37 Harry J Crijns  38 Michael J Cutler  27 Scott M Damrauer  39   40 Brian R Daniels  1 Dawood Darbar  41 Graciela Delgado  42 Joshua C Denny  43 Martin Dichgans  44   45   46 Marcus Dörr  47   48 Elton A Dudink  38 Samuel C Dudley  49 Nada Esa  50 Tonu Esko  1   51 Markku Eskola  52 Diane Fatkin  53   54   55 Stephan B Felix  47   48 Ian Ford  56 Oscar H Franco  57 Bastiaan Geelhoed  58 Raji P Grewal  59   60 Vilmundur Gudnason  61   62 Xiuqing Guo  31 Namrata Gupta  1 Stefan Gustafsson  63 Rebecca Gutmann  64 Anders Hamsten  65 Tamara B Harris  66 Caroline Hayward  67 Susan R Heckbert  68   69 Jussi Hernesniemi  52   70 Lynne J Hocking  71 Albert Hofman  57 Andrea R V R Horimoto  72 Jie Huang  73 Paul L Huang  2 Jennifer Huffman  67 Erik Ingelsson  63   74 Esra Gucuk Ipek  24 Kaoru Ito  75 Jordi Jimenez-Conde  76   77 Renee Johnson  53 J Wouter Jukema  78   79   80 Stefan Kääb  81   82 Mika Kähönen  83 Yoichiro Kamatani  84 John P Kane  85 Adnan Kastrati  82   86 Sekar Kathiresan  1   11 Petra Katschnig-Winter  87 Maryam Kavousi  57 Thorsten Kessler  86 Bas L Kietselaer  38 Paulus Kirchhof  88   89   90 Marcus E Kleber  42 Stacey Knight  27   91 Jose E Krieger  72 Michiaki Kubo  92 Lenore J Launer  66 Jari Laurikka  93 Terho Lehtimäki  70 Kirsten Leineweber  94 Rozenn N Lemaitre  19 Man Li  95   96 Hong Euy Lim  97 Henry J Lin  31 Honghuang Lin  15   16 Lars Lind  98 Cecilia M Lindgren  99 Marja-Liisa Lokki  100 Barry London  64 Ruth J F Loos  22   101   102 Siew-Kee Low  84 Yingchang Lu  22   101 Leo-Pekka Lyytikäinen  70 Peter W Macfarlane  103 Patrik K Magnusson  104 Anubha Mahajan  99 Rainer Malik  44 Alfredo J Mansur  105 Gregory M Marcus  106 Lauren Margolin  1 Kenneth B Margulies  26 Winfried März  107   108 David D McManus  50 Olle Melander  109 Sanghamitra Mohanty  110   111 Jay A Montgomery  43 Michael P Morley  26 Andrew P Morris  37 Martina Müller-Nurasyid  81   82   112 Andrea Natale  110   111 Saman Nazarian  113 Benjamin Neumann  81 Christopher Newton-Cheh  1   11 Maartje N Niemeijer  57 Kjell Nikus  52 Peter Nilsson  114 Raymond Noordam  115 Heidi Oellers  116 Morten S Olesen  5   6   7 Marju Orho-Melander  9 Sandosh Padmanabhan  117 Hui-Nam Pak  118 Guillaume Paré  36   119 Nancy L Pedersen  104 Joanna Pera  120 Alexandre Pereira  121   122 David Porteous  25 Bruce M Psaty  69   123 Sara L Pulit  1   124   125 Clive R Pullinger  85 Daniel J Rader  126 Lena Refsgaard  5   6   7 Marta Ribasés  127   128   129 Paul M Ridker  8 Michiel Rienstra  58 Lorenz Risch  130   131 Dan M Roden  43 Jonathan Rosand  1   11 Michael A Rosenberg  11   132 Natalia Rost  1   133 Jerome I Rotter  134 Samir Saba  135 Roopinder K Sandhu  136 Renate B Schnabel  137   138 Katharina Schramm  81   112 Heribert Schunkert  82   86 Claudia Schurman  22   101 Stuart A Scott  139 Ilkka Seppälä  70 Christian Shaffer  43 Svati Shah  140 Alaa A Shalaby  135   141 Jaemin Shim  142 M Benjamin Shoemaker  43 Joylene E Siland  58 Juha Sinisalo  143 Moritz F Sinner  81   82 Agnieszka Slowik  120 Albert V Smith  61   62 Blair H Smith  144 J Gustav Smith  1   145 Jonathan D Smith  13 Nicholas L Smith  68   69 Elsayed Z Soliman  146 Nona Sotoodehnia  147 Bruno H Stricker  148   149 Albert Sun  140 Han Sun  13 Jesper H Svendsen  5   7 Toshihiro Tanaka  150 Kahraman Tanriverdi  50 Kent D Taylor  31 Maris Teder-Laving  51 Alexander Teumer  48   151 Sébastien Thériault  36   119 Stella Trompet  78   115 Nathan R Tucker  1   2 Arnljot Tveit  33   152 Andre G Uitterlinden  148 Pim Van Der Harst  58 Isabelle C Van Gelder  58 David R Van Wagoner  13 Niek Verweij  58 Efthymia Vlachopoulou  100 Uwe Völker  48   153 Biqi Wang  154 Peter E Weeke  5   43 Bob Weijs  38 Raul Weiss  155 Stefan Weiss  48   153 Quinn S Wells  43 Kerri L Wiggins  19 Jorge A Wong  156 Daniel Woo  157 Bradford B Worrall  158 Pil-Sung Yang  118 Jie Yao  31 Zachary T Yoneda  43 Tanja Zeller  137   138 Lingyao Zeng  86 Steven A Lubitz  1   2   159 Kathryn L Lunetta  15   154 Patrick T Ellinor  160   161   162
Affiliations
Meta-Analysis

Multi-ethnic genome-wide association study for atrial fibrillation

Carolina Roselli et al. Nat Genet. .

Abstract

Atrial fibrillation (AF) affects more than 33 million individuals worldwide1 and has a complex heritability2. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus 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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Conflict of interest statement

Competing financial interests

Dr. Ellinor is the PI on a grant from Bayer to the Broad Institute focused on the genetics and therapeutics of atrial fibrillation. Dr. Psaty serves on the DSMB of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Dr. Kirchhof receives research support from European Union, British Heart Foundation, Leducq Foundation, Medical Research Council (UK), and German Centre for Cardiovascular Research, from several drug and device companies active in atrial fibrillation, and has received honoraria from several such companies. Dr. Kirchhof is also listed as inventor on two patents held by University of Birmingham (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). Dr. Leineweber is an employee of Bayer. The genotyping of participants in the Broad AF Study and the expression analysis of left atrial tissue samples were supported by a grant from Bayer to the Broad Institute. Dr. Nazarian is a consultant to Biosense Webster, Siemens, and Cardiosolv. Dr. Nazarian also receives research grants from NIH/NHLBI, Siemens, Biosense Webster, and Imricor. S. Kathiresan has received grant support from Bayer and Amarin; holds equity in San Therapeutics and Catabasis; and has received personal fees for participation in scientific advisory boards for Catabasis, Regeneron Genetics Center, Merck, Celera, Genomics PLC, Corvidia Therapeutics, Novo Ventures. S. Kathiresan also received personal fees from consulting services from Novartis, AstraZeneca, Alnylam, Eli Lilly Company, Leerink Partners, Merck, Noble Insights, Bayer, Ionis Pharmaceuticals, Novo Ventures, Haug Partners LLC. Genetic Modifiers Newco, Inc. Dr. Lubitz receives sponsored research support from Bristol Myers Squibb, Bayer, Biotronik, and Boehringer Ingelheim, and has consulted for St. Jude Medical / Abbott and Quest Diagnostics. The remaining authors have no disclosures.

Figures

Figure 1.
Figure 1.. Study and analysis flowchart
Top, overview of the participating studies, number of AF cases and referents, and the percent of samples imputed with each reference panel. Middle, summary of the primary analyses and the newly discovered loci for AF. Bottom, overview of the secondary analyses to evaluate AF risk variants and loci.
Figure 2.
Figure 2.. Manhattan plot of combined-ancestry meta-analysis
The plot shows 67 novel (red) and 27 known (blue) genetic loci associated with AF at a significance level of P < 1×10−8 (dashed line), for the combined-ancestry meta-analysis (n=588,190). The significance level accounts for multiple testing of independent variants with MAF ≥0.1% using a Bonferroni correction. P-values (two-sided) were derived from a meta-analysis using a fixed effects model with an inverse-variance weighted approach. The y-axis has a break between –log10(P) of 30 and 510 to emphasize the novel loci
Figure 3.
Figure 3.. Volcano plot of transcriptome-wide analysis from human heart tissues
The plots show the results from the transcriptome-wide analysis based on left ventricle (a, n=190) and right atrial appendage (b, n=159) tissue from GTEx, calculated with the MetaXcan method based on the combined-ancestry summary level results (n=588,190). Each plotted point represents the association results for an individual gene. The x-axis shows the effect size for associations of predicted gene expression and AF risk for each tested gene. The y-axis shows the –log10(P) for the associations per gene. Genes with positive effect (red) showed an association of increased predicted gene expression with AF risk. Genes with negative effect (blue) showed an association of decreased predicted gene expression with AF risk. The highlighted genes are significant after Bonferroni correction for all tested genes and tissues with a P-value < 5.36×10-6. The result for one gene for right atrial appendage (b) is not shown (SNX4, Effect = 6.94, P = 0.2).
Figure 4.
Figure 4.. Cross-trait associations of AF risk variants with AF risk factors in the UK Biobank
The heatmap shows associations of novel and known sentinel variants at AF risk loci from the combined-ancestry meta-analysis. Shown are variants and phenotypes with significant associations after correcting for 12 phenotypes via Bonferroni with P < 4.17×10-3. P-values (two-sided) were derived from linear and logistic regression models. Listed next to each trait is the number of cases for binary traits or total sample size for quantitative traits. Hierarchical clustering was performed on a variant level using the complete linkage method based on Euclidian distance. Coloring represents Z-scores for each respective trait or disease, oriented toward the AF risk allele. Red indicates an increase in the trait or disease risk while blue indicates a decrease in the trait or disease risk. Abbreviations, BMI, body-mass index, CAD, coronary artery disease, PVD, pulmonary vascular disease.

References

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    1. Lubitz SA et al. Association between familial atrial fibrillation and risk of new-onset atrial fibrillation. JAMA 304, 2263–9 (2010). - PMC - PubMed
    1. January CT et al. 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: Executive Summary. J. Am. Coll. Cardiol. 64, (2014). - PubMed
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    1. Ellinor PT et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat. Genet. 44, 670–5 (2012). - PMC - PubMed

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