Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
26
04
2021
accepted:
17
10
2022
pubmed:
7
12
2022
medline:
15
12
2022
entrez:
6
12
2022
Statut:
ppublish
Résumé
The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.
Identifiants
pubmed: 36474045
doi: 10.1038/s41588-022-01233-6
pii: 10.1038/s41588-022-01233-6
pmc: PMC9729111
doi:
Types de publication
Meta-Analysis
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1803-1815Subventions
Organisme : Department of Health
ID : BRC-1215-20014
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL086694
Pays : United States
Organisme : NIDDK NIH HHS
ID : UM1 DK105554
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700001I
Pays : United States
Organisme : British Heart Foundation
ID : RG/14/5/30893
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : HHSN268201700002I
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700005I
Pays : United States
Organisme : NHLBI NIH HHS
ID : K08 HL153950
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL087641
Pays : United States
Organisme : British Heart Foundation
ID : RG/13/13/30194
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R35 HL135824
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL125863
Pays : United States
Organisme : Medical Research Council
ID : MR/L003120/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S502443/1
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : K08 HG010155
Pays : United States
Organisme : NHLBI NIH HHS
ID : K08 HL153937
Pays : United States
Organisme : Wellcome Trust
ID : 203141/Z/16/Z
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/14/66/3129
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/18/13/33946
Pays : United Kingdom
Organisme : NHGRI NIH HHS
ID : T32 HG000040
Pays : United States
Organisme : NHLBI NIH HHS
ID : HHSN268201700004I
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR025005
Pays : United States
Organisme : British Heart Foundation
ID : SP/13/2/30111
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/16/4/32697
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : T32 HL007604
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL146860
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL059367
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG004402
Pays : United States
Organisme : British Heart Foundation
ID : SP/19/2/34462
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RE/13/1/30181
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/14/55/30806
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : HHSN268201700003I
Pays : United States
Organisme : British Heart Foundation
ID : SP/09/002
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0800270
Pays : United Kingdom
Investigateurs
John Danesh
(J)
Paul S de Vries
(PS)
Moritz von Scheidt
(M)
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022. The Author(s).
Références
GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1204–1222 (2020).
doi: 10.1016/S0140-6736(20)30925-9
Howson, J. M. et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat. Genet. 49, 1113–1119 (2017).
doi: 10.1038/ng.3874
Ishigaki, K. et al. Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases. Nat. Genet. 52, 669–679 (2020).
doi: 10.1038/s41588-020-0640-3
Klarin, D. et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat. Genet. 49, 1392–1397 (2017).
doi: 10.1038/ng.3914
Koyama, S. et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52, 1169–1177 (2020).
doi: 10.1038/s41588-020-0705-3
Nelson, C. P. et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat. Genet. 49, 1385–1391 (2017).
doi: 10.1038/ng.3913
Nikpay, M. et al. A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).
doi: 10.1038/ng.3396
van der Harst, P. & Verweij, N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ. Res. 122, 433–443 (2018).
doi: 10.1161/CIRCRESAHA.117.312086
Verweij, N. et al. Identification of 15 novel risk loci for coronary artery disease and genetic risk of recurrent events, atrial fibrillation and heart failure. Sci. Rep. 7, 2761 (2017).
doi: 10.1038/s41598-017-03062-8
Webb, T. R. et al. Systematic evaluation of pleiotropy identifies 6 further loci associated with coronary artery disease. J. Am. Coll. Card. 69, 823–836 (2017).
doi: 10.1016/j.jacc.2016.11.056
de Leeuw, C. A. et al. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
doi: 10.1371/journal.pcbi.1004219
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
doi: 10.1038/ncomms6890
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
doi: 10.1186/s13059-020-02252-4
Stacey, D. et al. ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Res. 47, e3 (2019).
doi: 10.1093/nar/gky837
Weeks, E. M. et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Preprint at medRxiv https://doi.org/10.1101/2020.09.08.20190561 (2020).
Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134–1144 (2016).
doi: 10.1056/NEJMoa1507652
Zhang, H., Hu, W. & Ramirez, F. Developmental expression of fibrillin genes suggests heterogeneity of extracellular microfibrils. J. Cell Biol. 129, 1165–1176 (1995).
doi: 10.1083/jcb.129.4.1165
Putnam, E. A. et al. Fibrillin-2 (FBN2) mutations result in the Marfan-like disorder, congenital contractural arachnodactyly. Nat. Genet. 11, 456–458 (1995).
doi: 10.1038/ng1295-456
Takeda, N. et al. Congenital contractural arachnodactyly complicated with aortic dilatation and dissection: case report and review of literature. Am. J. Med. Genet. A 167A, 2382–2387 (2015).
doi: 10.1002/ajmg.a.37162
Deguchi, J. O. et al. Matrix metalloproteinase-13/collagenase-3 deletion promotes collagen accumulation and organization in mouse atherosclerotic plaques. Circulation 112, 2708–2715 (2005).
doi: 10.1161/CIRCULATIONAHA.105.562041
Quillard, T. et al. Selective inhibition of matrix metalloproteinase-13 increases collagen content of established mouse atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 31, 2464–2472 (2011).
doi: 10.1161/ATVBAHA.111.231563
Romeo, S. et al. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nat. Genet. 40, 1461–1465 (2008).
doi: 10.1038/ng.257
Grant, S. F. et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38, 320–323 (2006).
doi: 10.1038/ng1732
Purcell, S. M. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).
doi: 10.1038/nature08185
Vilhjálmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).
doi: 10.1016/j.ajhg.2015.09.001
Sabatine, M. S. et al. Rationale and design of the further cardiovascular outcomes research with PCSK9 inhibition in subjects with elevated risk trial. Am. Heart J. 173, 94–101 (2016).
doi: 10.1016/j.ahj.2015.11.015
Wu, M.-Y. et al. Inhibition of the plasma SCUBE1, a novel platelet adhesive protein, protects mice against thrombosis. Arterioscler. Thromb. Vasc. Biol. 34, 1390–1398 (2014).
doi: 10.1161/ATVBAHA.114.303779
Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).
doi: 10.1371/journal.pgen.1004722
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
doi: 10.1016/j.ajhg.2014.03.004
Weissbrod, O. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet. 52, 1355–1363 (2020).
doi: 10.1038/s41588-020-00735-5
Gupta, R. M. et al. A genetic variant associated with five vascular diseases is a distal regulator of endothelin-1 gene expression. Cell 170, 522–533 (2017).
doi: 10.1016/j.cell.2017.06.049
Prestel, M. et al. The atherosclerosis risk variant rs2107595 mediates allele-specific transcriptional regulation of HDAC9 via E2F3 and Rb1. Stroke 50, 2651–2660 (2019).
doi: 10.1161/STROKEAHA.119.026112
Surakka, I. et al. The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015).
doi: 10.1038/ng.3300
Franzén, O. et al. Cardiometabolic risk loci share downstream cis-and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016).
doi: 10.1126/science.aad6970
GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
doi: 10.1038/nature24277
Alasoo, K. et al. Genetic effects on promoter usage are highly context-specific and contribute to complex traits. Elife 8, e41673 (2019).
doi: 10.7554/eLife.41673
Hamada, M. et al. MafB promotes atherosclerosis by inhibiting foam-cell apoptosis. Nat. Commun. 5, 3147 (2014).
doi: 10.1038/ncomms4147
Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 1527–1533 (2021).
doi: 10.1038/s41588-021-00945-5
Wong, D., Turner, A. W. & Miller, C. L. Genetic insights into smooth muscle cell contributions to coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 39, 1006–1017 (2019).
doi: 10.1161/ATVBAHA.119.312141
Bennett, M. R., Sinha, S. & Owens, G. K. Vascular smooth muscle cells in atherosclerosis. Circ. Res. 118, 692–702 (2016).
doi: 10.1161/CIRCRESAHA.115.306361
Fantuzzi, G. & Mazzone, T. Adipose tissue and atherosclerosis: exploring the connection. Arterioscler. Thromb. Vasc. Biol. 27, 996–1003 (2007).
doi: 10.1161/ATVBAHA.106.131755
Chiang, H.-Y., Chu, P.-H. & Lee, T.-H. MFG-E8 mediates arterial aging by promoting the proinflammatory phenotype of vascular smooth muscle cells. J. Biomed. Sci. 26, 61 (2019).
doi: 10.1186/s12929-019-0559-0
Wang, M., Wang, H. H. & Lakatta, E. G. Milk fat globule epidermal growth factor VIII signaling in arterial wall remodeling. Curr. Vasc. Pharm. 11, 768–776 (2013).
doi: 10.2174/1570161111311050014
Soubeyrand, S. et al. Regulation of MFGE8 by the intergenic coronary artery disease locus on 15q26.1. Atherosclerosis 284, 11–17 (2019).
doi: 10.1016/j.atherosclerosis.2019.02.012
Chambers, J. C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat. Genet. 43, 1131–1138 (2011).
doi: 10.1038/ng.970
Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).
doi: 10.1038/s41588-018-0222-9
Hanley, P. J. et al. Motorized RhoGAP myosin IXb (Myo9b) controls cell shape and motility. Proc. Natl Acad. Sci. USA 107, 12145–12150 (2010).
doi: 10.1073/pnas.0911986107
Lu, Y. et al. Genome-wide identification of genes essential for podocyte cytoskeletons based on single-cell RNA sequencing. Kidney Int. 92, 1119–1129 (2017).
doi: 10.1016/j.kint.2017.04.022
Gough, W. et al. A quantitative, facile, and high-throughput image-based cell migration method is a robust alternative to the scratch assay. J. Biomol. Screen. 16, 155–163 (2011).
doi: 10.1177/1087057110393340
Damask, A. et al. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Circulation 141, 624–636 (2020).
doi: 10.1161/CIRCULATIONAHA.119.044434
Hindy, G. et al. Genome-wide polygenic score, clinical risk factors, and long-term trajectories of coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 40, 2738–2746 (2020).
doi: 10.1161/ATVBAHA.120.314856
Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).
doi: 10.1038/s41588-018-0183-z
Marston, N. A. et al. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER trial. Circulation 141, 616–623 (2020).
doi: 10.1161/CIRCULATIONAHA.119.043805
Amariuta, T. et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52, 1346–1354 (2020).
doi: 10.1038/s41588-020-00740-8
Xu, Y. et al. Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease. Cell Genom. 2, 100086 (2022).
doi: 10.1016/j.xgen.2021.100086
Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).
doi: 10.1038/s41467-019-11112-0
Burbelo, P. D., Martin, G. R. & Yamada, Y. Alpha 1(IV) and alpha 2(IV) collagen genes are regulated by a bidirectional promoter and a shared enhancer. Proc. Natl Acad. Sci. USA 85, 9679–9682 (1988).
doi: 10.1073/pnas.85.24.9679
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
doi: 10.1093/bioinformatics/btq340
Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).
doi: 10.1038/ng.2213
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
doi: 10.1038/ng.3406
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
doi: 10.1038/s41586-020-2308-7
McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
doi: 10.1186/s13059-016-0974-4
Zhou, W. et al. Scalable generalized linear mixed model for region-based association tests in large biobanks and cohorts. Nat. Genet. 52, 634–639 (2020).
doi: 10.1038/s41588-020-0621-6
Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).
doi: 10.1038/s41588-018-0184-y
Muñoz, M. et al. Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank. Nat. Genet. 48, 980–983 (2016).
doi: 10.1038/ng.3618
Witte, J. S., Visscher, P. M. & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014).
doi: 10.1038/nrg3786
1000 Genomes Project Consortiumet al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
doi: 10.1038/nature15393
Berglund, G. et al. The Malmo Diet and Cancer study. Design and feasibility. J. Intern. Med. 233, 45–51 (1993).
doi: 10.1111/j.1365-2796.1993.tb00647.x
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
doi: 10.1186/s13742-015-0047-8
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).
doi: 10.1038/nmeth.1906
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
doi: 10.1038/nature14248
Atri, D. S. et al. CRISPR-Cas9 genome editing of primary human vascular cells in vitro. Curr. Protoc. 1, e291 (2021).
doi: 10.1002/cpz1.291
Buenrostro, J. D. et al. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).
doi: 10.1002/0471142727.mb2129s109