Abstract
Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell–cell communication at 139 loci. Furthermore, we assayed chromatin accessibility using assay for transposase-accessible chromatin with sequencing and histone H3 lysine 4 trimethylation in stem cell-derived atrial cardiomyocytes. We observed a marked increase in chromatin accessibility for our sentinel variants and prioritized genes in atrial cardiomyocytes. Finally, a polygenic risk score (PRS) based on our updated effect estimates improved AF risk prediction compared to the CHARGE-AF clinical risk score and a previously reported PRS for AF. The doubling of known risk loci will facilitate a greater understanding of the pathways underlying AF.
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Data availability
The summary-level results file as well as the weights file for the PRSAF are available for download at the Cardiovascular Disease Knowledge Portal under the weblinks https://cvd.hugeamp.org/downloads.html#polygenic and https://cvd.hugeamp.org/downloads.html#summary. The raw and processed ATAC–seq and H3K4me3 data have been deposited at the NCBI Gene Expression Omnibus under accession number GSE225293. The following datasets were used in this study and are publicly available under the listed weblinks: GENCODE: https://www.gencodegenes.org; 1000G LD reference, MAGMA gene annotations and precomputed files for PoPS algorithm: https://www.dropbox.com/sh/o6t5jprvxb8b500/AADZ8qD6Rpz4uvCk0b5nUnPaa/data?dl=0; GTEx: https://www.gtexportal.org/home; ENCODE: https://www.encodeproject.org; OpenTargets: https://www.opentargets.org.
Code availability
All software programs used in the study are publicly available and described in the Methods and Reporting Summary.
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Acknowledgements
The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this manuscript were obtained from the GTEx Portal on 02/10/2021. This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration and was supported by award no. I01-BX004821. This publication does not necessarily represent the views of the Department of Veteran Affairs or the United States Government. Study-specific acknowledgements, including grants, are listed in the Supplementary Note.
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C.R., C.W., D.G., G.S., H.H., I.S., K.S., M.C., M.C.H., M.S.O., N.A.M. and P.T.E. crafted and finalized the manuscript. A.A., A.C., A.C.P., A.N., A.T., B.B., B.G., B.H.S., B.M.P., B.S., C.D.A., C.E., C.G., C. Haggerty, C. Hayward, C.R., C.R.P., C.T.R., C.W., D.O.A., D.C., D.D.M., D.E.A., D.F., D.F.F., D.G., D.I.C., D.J.R., D.L., D.S., E.-K.C., E.J.B., E.D., E.S., E.Z.S., G.M.M., J.G.S., G.S., H.-N.P., H.A., H.B., H.H., H.J.C., H.J.L., H.M., H.S., I.E.C., I.S., J.B., J.E.K., J.G., J.I.R., J.P.K., J.P.P., J.S., J.T.R., J.v.M., K.C., K.H., K.I., K.L.L., K.S., L.-C.W., L.J.L., L.L., L.M.M., L.R., L.X., L.Z., M.C., M. Dichgans, M. Dörr, M.E.K., M.F., M.F.S., J.M.G., M.C.H., M.J.C., M.K., M.K.C., M.M.-N., M.P., M.R., M.S.O., M.S.S., M.T.-L., N.A.M., N.A.N., N.C., N.L.P., N.L.S., N.S., O.B.P., O.M., P.-S.Y., P.K., P.K.E.M., P.B.M., P.T.E., P.v.d.H., P.W.W., P.W.M., R.A.J.S., R.J., R.J.F.L., R.M., R.B.T., S.K.G., S.H.C., S.H.S., S.J.J., S. Kääb, S. Kany, S. Khurshid, S. Knight, S.A.L., S.M., S.M.D., S.N., S.R.O., S.P., S.R.H., S.W., S.Z., T.C., T.D., T.E., T.Z., U.T., V.G., J.W.J., W.M., X.G., X.Z. and Y.V.S. contributed to or revised the manuscript. B.W., C.R., D.G., E.-K.C., F.G., F.K.K., G.E.M.M., G.S., H.L., I.S., J.A.S., J.Y., K.M., L.-C.W., M.C., M.F., M.P., S.E.G., S.J.J., S.T., T.D. and W.Z. performed statistical analyses. A.A., C.D.A., C.G., C. Haggerty, C.M.A., C.T.R., C.W., D.C., D.I.C., D.L., D.M.R., D.S., E.J.B., E.B., F.G., H.-N.P., H.B., I.K., J.D.F., K.I., K.L.L., K.S., L.L., L.R., M.B.S., M.F., M.M.-N., M.P., M.S.S., N.A.M., P.M.R., P.T.E., Q.S.W., R.J.F.L., S.A.L., S.L.R.K. and Z.T.Y. contributed or managed samples and phenotype data. C.M.A., C.T.R., C.W., D.G., D.I.C., H.B., H.H., K.S. and P.T.E. conceived, designed and supervised the overall project.
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B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. C.D.A. receives sponsored research support from Bayer AG and has consulted for ApoPharma. C. Haggerty receives research support from Tempus Labs, outside the scope of the present work. C.M.A. receives sponsored research support from St. Jude, Abbott and Roche. C.R. is supported by a grant from Bayer AG to the Broad Institute focused on the development of therapeutics for cardiovascular disease. C.R. is a full-time employee of GSK as of 1 July 2024. C.T.R. reports research grants through Brigham and Women’s Hospital from Amgen, Anthos, AstraZeneca, Daiichi Sankyo, Janssen, Merck and Novartis and has received honoraria for scientific advisory boards and consulting from Anthos, Bayer, Bristol Myers Squibb, Daiichi Sankyo, Janssen, Pfizer, Regeneron and Sirius. The spouse of C.W. works at Regeneron Pharmaceuticals. D.O.A., D.G., G. Sveinbjornsson, H.A., H.H., K.S., R.B.T. and U.T. are employees of deCODE genetics/Amgen. D.C. has received consultancy fees from Roche Diagnostics and Trimedics, and speaker’s fees from BMS/Pfizer and Servier, all outside of the current work. D.F.F. is a full-time employee of Bayer. E.B. performs uncompensated consultancies and lectures with The Medicines Company. E.-K.C. reports research grants or speaking fees from Abbott, Bayer, BMS/Pfizer, Biosense Webster, Chong Kun Dang, Daewoong Pharmaceutical, Daiichi Sankyo, DeepQure, Dreamtech, Jeil Pharmaceutical, Medtronic, Samjinpharm, Seers Technology and Skylabs. L.-C.W. receives sponsored research support from IBM to the Broad Institute. L.Z. is a full-time employee of Bayer AG. M.E.K. is employed by SYNLAB Holding Deutschland. M.S.S. receives research grant support through Brigham and Women’s Hospital from Abbott, Amgen, Anthos Therapeutics, AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Intarcia, Ionis, Medicines Company, MedImmune, Merck, Novartis, Pfizer and Quark Pharmaceuticals and consults for Althera, Amgen, Anthos Therapeutics, AstraZeneca, Beren Therapeutics, Bristol Myers Squibb, DalCor, Dr. Reddy’s Laboratories, Fibrogen, Intarcia, Merck, Moderna, Novo Nordisk and Silence Therapeutics; additionally, M.S.S. is a member of the TIMI Study Group, which has also received institutional research grant support through Brigham and Women’s Hospital from ARCA Biopharma, Janssen Research and Development, Siemens Healthcare Diagnostics, Softcell Medical Limited, Regeneron, Roche and Zora Biosciences. N.A.M. reports involvement in clinical trials with Amgen, Pfizer, Ionis, Novartis and AstraZeneca without personal fees, payments or increase in salary. P.M.R. has received investigator-initiated research grant support for unrelated projects from NHLBI, Operation Warp Speed, Novartis, Kowa, Amarin and Pfizer and has served as a consultant on unrelated issues to Novo Nordisk, Flame, Agepha, Uppton, Novartis, Jansen, Health Outlook, Civi Biopharm, Alnylam and SOCAR. P.T.E. receives sponsored research support from Bayer AG, Bristol Myers Squibb, Pfizer and Novo Nordisk; he has also served on advisory boards or consulted for Bayer AG. S.A.L. is a full-time employee of Novartis Institutes for BioMedical Research as of 18 July 2022. Previously, S.A.L. received sponsored research support from Bristol Myers Squibb/Pfizer, Bayer, Boehringer Ingelheim, Fitbit, IBM, Medtronic and Premier, and consulted for Bristol Myers Squibb/Pfizer, Bayer, Blackstone Life Sciences and Invitae. S.M.D. receives research support from RenalytixAI and personal consulting fees from Calico Labs, outside the scope of the current research. W.M. reports grants and/or personal fees from Siemens Healthineers, Aegerion Pharmaceuticals, AMGEN, AstraZeneca, Sanofi, Alexion Pharmaceuticals, BASF, Abbott Diagnostics, Numares, Berlin-Chemie, Akzea Therapeutics, Bayer Vital, bestbion dx, Boehringer Ingelheim Pharma, Immundiagnostik, Merck Chemicals, MSD Sharp and Dohme, Novartis Pharma and Olink Proteomics, and other from Synlab Holding Deutschland, all outside the submitted work. A.N. is a consultant for Abbott, Biosrnse Webster, Biotronik, Boston Sci, iRhythm, Field Medical, Pulse Bioselect and Medtronic. S. Khurshid receives sponsored research support from Bayer AG. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Evaluation of effect estimates and allele frequencies for main GWAS meta-analysis and, in comparison, to the validation analysis.
a,b, Plots showing data for the 299 sentinel variants from the common variant analysis (MAF ≥ 1%) that were also available in the validation set. a, Correlation of allele frequencies between the meta-analysis and the validation cohort from MVP. b, Correlation of effect estimates between meta-analysis and the validation cohort from MVP. The red line is the identity line (x = y). Labelled in red are the variants with discordant direction of effect between meta-analysis and validation. c, Plot showing the relationship between effect allele frequency and strength of effect for sentinel variants of the meta-analysis. The effect estimates are from the inverse variance weighted method for meta-analysis. The dotted vertical lines show the cutoff for rare variants with MAF < 1%. The genome-wide significance cut-off of P < 5 × 10−8 was applied to correct for multiple testing. d, Plot showing co-occurrence of risk allele for atrial fibrillation and minor allele in blue and the inverse in red for sentinel variants of the meta-analysis. AF, allele frequency; ALL, all-ancestry; MAF, minor allele frequency; Meta, meta-analysis; MVP, Million Veteran Program.
Extended Data Fig. 2 Distribution of PoPS-score.
Red line shows the cutoff for mean + 3 standard deviations of the score (cutoff = 0.8548401). There were 205 genes with a PoPS-score higher than the cutoff.
Extended Data Fig. 3 Partitioned heritability analyses with annotations from human cardiac single-nuclei RNA-sequencing expression data.
a, Plot showing the partitioned heritability results for gene expression of cardiomyocytes by heart chamber (PLeftAtrium = 2.46 × 10−2, PRightAtrium = 1.51 × 10−1, PLeftVentricle = 6.25 × 10−1, PRightVentricle = 1.43 × 10−1). The blue dotted line shows the cutoff for nominal significance P < 0.05. The red dotted line shows the Bonferroni corrected significance cut off 0.05/4. b, Plot showing the partitioned heritability results for gene expression in left atrial tissue by cell type (PCardiomyocyte = 2.26 × 10−3, PFibroblast = 8.18 × 10−1, PEndothelial = 6.07 × 10−1, PPericyte = 4.34 × 10−2, PMacrophage = 3.48 × 10−1, PVSMC = 3.15 × 10−2, PAdipocyte = 8.90 × 10−1, PNeuronal = 1.56 × 10−1, PLymphocyte = 5.63 × 10−1). The blue dotted line shows the cutoff for nominal significance P < 0.05. The red dotted line shows the Bonferroni corrected significance cut off 0.05/9. VSMC, vascular smooth muscle cells.
Extended Data Fig. 4 Venn diagram of consensus genes between GenePrio and nearest protein coding genes.
Venn diagram for the overlap of GenePrio vs. nearest gene (protein coding, in relation to the transcription start position) at 139 loci. In red are the genes identified as the nearest, in blue are the GenePrio genes, and in black are the genes that overlap between the two groups. 56% of genes overlap.
Extended Data Fig. 5 Heatmap for GenePrio genes with two lines of evidence.
GenePrio genes with two lines of evidence. The five categories of evidence that were assessed to prioritize genes at GWAS loci: snRNA-seq (labelled as snRNA), gene was a top 10% marker gene for cardiomyocytes in left atrial tissue; Coding, gene had genome-wide significant loss-of-function variant or missense variant with predicted to be damaging effect; MAGMA, significant result for the gene in MAGMA analysis; PoPs, gene had a high PoPs score; eQTL, sentinel variant at locus had a significant eQTL to that gene in cardiac tissue. The genes are sorted from lowest to highest P-value at the sentinel variant of the locus. AF, atrial fibrillation; eQTL, expression quantitative trait locus; GWAS, genome-wide association study; MAF, minor allele frequency; MAGMA, Multi-marker Analysis of GenoMic Annotation; PoPS, polygenic priority score; snRNA-seq, single-nuclei RNA-sequencing.
Extended Data Fig. 6 Gene set enrichment analysis for all GenePrio genes.
Results of the gene set enrichment analysis for all 139 GenePrio genes across several databases. The −log10(P-values) are plotted sorted by gene set category. The top 5 gene sets by P-value are listed for each category. The size of each dot is proportional to the term size (n genes) of the gene set, that is larger terms have larger dots. The enrichment testing is done using a Fisher’s one-sided test (cumulative hypergeometric probability). P-values were adjusted for multiple testing using the g:SCS algorithm from the g:Profiler tool. BP, biological process; CC, cellular component; GO, gene ontology; HP, human phenotype ontology; HPA, human protein atlas; MF, molecular function; REAC, reactome; WP, wiki pathways.
Extended Data Fig. 7 Gene set enrichment results for top 10 GenePrio genes and GO:BP.
Heatmap of significant (adjusted P < 5 × 10−6) gene sets for GO:BP, showing top 10 GenePrio genes (GenePrio sum = 4) and their affiliation to each set. The enrichment testing is done using a Fisher’s one-sided test (cumulative hypergeometric probability). P-values were adjusted for multiple testing using the g:SCS algorithm from the g:Profiler tool. BP, biological process; GO, gene ontology.
Extended Data Fig. 8 Gene set enrichment results for top 10 GenePrio genes and GO:MF, GO:CC, HP, HPA and REAC.
Heatmap of significant (adjusted P < 5 × 10−6) gene sets for GO:MF, GO:CC, HP, HPA and REAC, showing top 10 GenePrio genes (GenePrio sum = 4) and their affiliation to each set. The enrichment testing is done using a Fisher’s one-sided test (cumulative hypergeometric probability). P-values were adjusted for multiple testing using the g:SCS algorithm from the g:Profiler tool. CC, cellular component; HP, human phenotype ontology; HPA, human protein atlas; MF, molecular function; REAC, Reactome.
Extended Data Fig. 9 Cluster analysis of the GenePrio genes based on cell type specific expression and Gene Ontology.
Results from a cluster analysis of 112 of the 139 GenePrio genes for Gene Ontology (GO) with clusterProfiler. Genes were annotated to cell types (one or more) based on the top 10% specific genes for each cell type. The over representation analysis uses a one-sided Fisher’s exact test. Multiple testing adjustment is performed with the Benjamini-Hochberg method. GO, gene ontology; VSMCs, vascular smooth muscle cells.
Extended Data Fig. 10 ATAC-seq tracks for TBX5, PITX2 and HSPB7 loci.
a, TBX5 locus. b, PITX2 locus. c, HSPB7 locus. Tracks for ATAC-seq from our iPSC-derived atrial cardiomyocytes and seven publicly available ENCODE ATAC-seq datasets (GM23338, liver, skeletal muscle, NK cells, pancreas, thyroid and bladder), as well as two histone modification tracks: H3K4me3 from a CUT&RUN experiment on our iPSC-derived atrial cardiomyocytes and layered H3K27ac from seven published ENCODE cell lines. The histone modifications H3K27ac and H3K4me3 are both associated with active regions in the genome, and are often located at promoters, enhancers or transcription start sites. GWAS variants indicate the ___location of common variants with genome-wide significance (P < 5 × 10−8) in the meta-analysis. Coordinates are in build GRCh38. ATAC-seq, Assay for Transposase-Accessible Chromatin using sequencing; CMs, cardiomyocytes; CUT&RUN, Cleavage Under Targets & Release Using Nuclease; ENCODE, Encyclopedia of DNA Elements; H3K27ac, histone H3 Lysine 27 acetylation; GWAS, genome-wide association study, H3K4me3, histone H3 lysine 4 trimethylation; iPS cells, induced pluripotent stem cells.
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Roselli, C., Surakka, I., Olesen, M.S. et al. Meta-analysis of genome-wide associations and polygenic risk prediction for atrial fibrillation in more than 180,000 cases. Nat Genet 57, 539–547 (2025). https://doi.org/10.1038/s41588-024-02072-3
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DOI: https://doi.org/10.1038/s41588-024-02072-3
