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Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

 


  • Timmis, A. et al. European Society of Cardiology: Cardiovascular Disease Statistics 2017. Eur. Heart J. 39, 508–579 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Tsao, C. W. et al. Heart Disease and Stroke Statistics—2022 Update: a report from the American Heart Association. Circulation 145, e153–e639 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Libby, P., Ridker, P. M. & Hansson, G. K. Progress and challenges in translating the biology of atherosclerosis. Nature 473, 317–325 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Baber, U. et al. Prevalence, impact, and predictive value of detecting subclinical coronary and carotid atherosclerosis in asymptomatic adults: the BioImage study. J. Am. Coll. Cardiol. 65, 1065–1074 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Polonsky, T. S. et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA 303, 1610–1616 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kavousi, M. et al. Evaluation of newer risk markers for coronary heart disease risk classification. Ann. Intern. Med. 156, 438–444 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Bielak, L. F., Rumberger, J. A., Sheedy, P. F. 2nd, Schwartz, R. S. & Peyser, P. A. Probabilistic model for prediction of angiographically defined obstructive coronary artery disease using electron beam computed tomography calcium score strata. Circulation 102, 380–385 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Grundy, S. M. et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 139, e1082–e1143 (2019).

    PubMed 

    Google Scholar
     

  • Jin, H.-Y. et al. The relationship between coronary calcification and the natural history of coronary artery disease. JACC Cardiovasc. Imaging 14, 233–242 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Jinnouchi, H. et al. Calcium deposition within coronary atherosclerotic lesion: implications for plaque stability. Atherosclerosis 306, 85–95 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Durham, A. L., Speer, M. Y., Scatena, M., Giachelli, C. M. & Shanahan, C. M. Role of smooth muscle cells in vascular calcification: implications in atherosclerosis and arterial stiffness. Cardiovasc. Res. 114, 590–600 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nakahara, T. et al. Coronary artery calcification: from mechanism to molecular imaging. JACC Cardiovasc. Imaging 10, 582–593 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Fujiyoshi, A. et al. Coronary artery calcium and risk of dementia in mesa (multi-ethnic study of atherosclerosis). Circ. Cardiovasc. Imaging 10, e005349 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Handy, C. E. et al. The association of coronary artery calcium with noncardiovascular disease: the multi-ethnic study of atherosclerosis. JACC Cardiovasc. Imaging 9, 568–576 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hermann, D. M. et al. Coronary artery calcification is an independent stroke predictor in the general population. Stroke 44, 1008–1013 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Peyser, P. A. et al. Heritability of coronary artery calcium quantity measured by electron beam computed tomography in asymptomatic adults. Circulation 106, 304–308 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Wojczynski, M. K. et al. Genetics of coronary artery calcification among African Americans, a meta-analysis. BMC Med. Genet. 14, 75 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Natarajan, P. et al. Multiethnic exome-wide association study of subclinical atherosclerosis. Circ. Cardiovasc. Genet. 9, 511–520 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • O’Donnell, C. J. et al. Genome-wide association study for coronary artery calcification with follow-up in myocardial infarction. Circulation 124, 2855–2864 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Van Setten, J. et al. Genome-wide association study of coronary and aortic calcification implicates risk loci for coronary artery disease and myocardial infarction. Atherosclerosis 228, 400–405 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Psaty, B. M. et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ. Cardiovasc. Genet. 2, 73–80 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Agatston, A. S. et al. Quantification of coronary artery calcium using ultrafast computed tomography. J. Am. Coll. Cardiol. 15, 827–832 (1990).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shen, H. et al. Familial defective apolipoprotein B-100 and increased low-density lipoprotein cholesterol and coronary artery calcification in the Old Order Amish. Arch. Intern. Med. 170, 1850–1855 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wellcome Trust Case Control Consortium et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

  • Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • De Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Weeks, E. M. et al. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat. Genet. 55, 1267–1276 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Franzén, O. et al. Cardiometabolic risk loci share downstream cis– and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Franceschini, N. et al. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes. Nat. Commun. 9, 5141 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hao, K. et al. Integrative prioritization of causal genes for coronary artery disease. Circ. Genom. Precis. Med. 15, e003365 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cano-Gamez, E. & Trynka, G. From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 11, 424 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, B. et al. Genetic regulatory mechanisms of smooth muscle cells map to coronary artery disease risk loci. Am. J. Hum. Genet. 103, 377–388 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, Y., Sarkar, A., Kheradpour, P., Ernst, J. & Kellis, M. Evidence of reduced recombination rate in human regulatory domains. Genome Biol. 18, 193 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Turner, A. W. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat. Genet. 54, 804–816 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shen, J. et al. Regulation of vascular calcification by growth hormone-releasing hormone and its agonists. Circ. Res. 122, 1395–1408 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pan, H. et al. Single-cell genomics reveals a novel cell state during smooth muscle cell phenotypic switching and potential therapeutic targets for atherosclerosis in mouse and human. Circulation 142, 2060–2075 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wirka, R. C. et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat. Med. 25, 1280–1289 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alsaigh, T., Evans, D., Frankel, D. & Torkamani, A. Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution. Commun. Biol. 5, 1084 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Slenders, L. et al. Intersecting single-cell transcriptomics and genome-wide association studies identifies crucial cell populations and candidate genes for atherosclerosis. Eur. Heart J. Open 2, oeab043 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Depuydt, M. A. C. et al. Microanatomy of the human atherosclerotic plaque by single-cell transcriptomics. Circ. Res. 127, 1437–1455 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Van der Laan, S. W. et al. Genetic susceptibility loci for cardiovascular disease and their impact on atherosclerotic plaques. Circ. Genom. Precis. Med. 11, e002115 (2018).

    PubMed 

    Google Scholar
     

  • Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tcheandjieu, C. et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. 28, 1679–1692 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Donovan, K. et al. Fibroblast growth factor-23 and risk of cardiovascular diseases: a Mendelian randomization study. Clin. J. Am. Soc. Nephrol. 18, 17–27 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Hall, K. T. et al. Catechol-O-methyltransferase and cardiovascular disease: MESA. J. Am. Heart Assoc. 8, e014986 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nitschke, Y. et al. Generalized arterial calcification of infancy and pseudoxanthoma elasticum can be caused by mutations in either ENPP1 or ABCC6. Am. J. Hum. Genet. 90, 25–39 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ralph, D. et al. ENPP1 variants in patients with GACI and PXE expand the clinical and genetic heterogeneity of heritable disorders of ectopic calcification. PLoS Genet. 18, e1010192 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sinnott-Armstrong, N. et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat. Genet. 53, 185–194 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Herrera-Rivero, M. et al. Single- and multimarker genome-wide scans evidence novel genetic risk modifiers for venous thromboembolism. Thromb. Haemost. 121, 1169–1180 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582, 240–245 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kato, N. et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat. Genet. 47, 1282–1293 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Thériault, S. et al. Identification of circulating proteins associated with blood pressure using Mendelian randomization. Circ. Genom. Precis. Med. 13, e002605 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hernandez Cordero, A. I. et al. Genome-wide associations reveal human-mouse genetic convergence and modifiers of myogenesis, CPNE1 and STC2. Am. J. Hum. Genet. 105, 1222–1236 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, S. K. Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture. PLoS ONE 13, e0200785 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schuler-Lüttmann, S. et al. Insulin-like growth factor-binding protein-3 is associated with the presence and extent of coronary arteriosclerosis. Arterioscler. Thromb. Vasc. Biol. 20, e10–e15 (2000).

    Article 
    PubMed 

    Google Scholar
     

  • Claussnitzer, M. et al. FTO obesity variant circuitry and adipocyte browning in humans. N. Engl. J. Med. 373, 895–907 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Whitson, R. H. Jr, Li, S.-L., Zhang, G., Larson, G. P. & Itakura, K. Mice with Fabp4-Cre ablation of Arid5b are resistant to diet-induced obesity and hepatic steatosis. Mol. Cell. Endocrinol. 528, 111246 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Watanabe, M. et al. Regulation of smooth muscle cell differentiation by AT-rich interaction domain transcription factors Mrf2α and Mrf2β. Circ. Res. 91, 382–389 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, G. et al. Genetic variations of Mrf-2/ARID5B confer risk of coronary atherosclerosis in the Japanese population. Int. Heart J. 49, 313–327 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, G. et al. Associations of variations in the MRF2/ARID5B gene with susceptibility to type 2 diabetes in the Japanese population. J. Hum. Genet. 57, 727–733 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Hata, K. et al. Arid5b facilitates chondrogenesis by recruiting the histone demethylase Phf2 to Sox9-regulated genes. Nat. Commun. 4, 2850 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Paganelli, F., Gaudry, M., Ruf, J. & Guieu, R. Recent advances in the role of the adenosinergic system in coronary artery disease. Cardiovasc. Res. 117, 1284–1294 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Xu, Y. et al. Regulation of endothelial intracellular adenosine via adenosine kinase epigenetically modulates vascular inflammation. Nat. Commun. 8, 943 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, M. et al. Ablation of myeloid ADK (adenosine kinase) epigenetically suppresses atherosclerosis in ApoE−/− (apolipoprotein E deficient) mice. Arterioscler. Thromb. Vasc. Biol. 38, 2780–2792 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kauffenstein, G. et al. Alteration of extracellular nucleotide metabolism in pseudoxanthoma elasticum. J. Invest. Dermatol. 138, 1862–1870 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boison, D. Adenosine kinase: exploitation for therapeutic gain. Pharmacol. Rev. 65, 906–943 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ozkok, A. et al. FGF-23 associated with the progression of coronary artery calcification in hemodialysis patients. BMC Nephrol. 14, 241 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Murali, S. K. et al. FGF23 regulates bone mineralization in a 1,25(OH)2 D3 and Klotho-independent manner. J. Bone Miner. Res. 31, 129–142 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Donovan, K. et al. Fibroblast growth factor-23 and risk of cardiovascular diseases: a Mendelian randomisation study. Clin. J. Am. Soc. Nephrol. 18, 17–27 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Alexopoulos, N. & Raggi, P. Calcification in atherosclerosis. Nat. Rev. Cardiol. 6, 681–688 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Arbab-Zadeh, A. & Fuster, V. The myth of the ‘vulnerable plaque’: transitioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assessment. J. Am. Coll. Cardiol. 65, 846–855 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schoenhagen, P. et al. Extent and direction of arterial remodeling in stable versus unstable coronary syndromes: an intravascular ultrasound study. Circulation 101, 598–603 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mauriello, A. et al. Coronary calcification identifies the vulnerable patient rather than the vulnerable plaque. Atherosclerosis 229, 124–129 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nicoll, R., Zhao, Y., Ibrahimi, P., Olivecrona, G. & Henein, M. Diabetes and hypertension consistently predict the presence and extent of coronary artery calcification in symptomatic patients: a systematic review and meta-analysis. Int. J. Mol. Sci. 17, 1481 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Van der Toorn, J. E. et al. Arterial calcification at multiple sites: sex-specific cardiovascular risk profiles and mortality risk-the Rotterdam Study. BMC Med. 18, 263 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kronmal, R. A. et al. Risk factors for the progression of coronary artery calcification in asymptomatic subjects: results from the multi-ethnic study of atherosclerosis (MESA). Circulation 115, 2722–2730 (2007).

    Article 
    PubMed 

    Google Scholar
     

  • Saleheen, D. et al. Loss of cardioprotective effects at the ADAMTS7 locus as a result of gene–smoking interactions. Circulation 135, 2336–2353 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Polfus, L. M. et al. Genome-wide association study of gene by smoking interactions in coronary artery calcification. PLoS ONE 8, e74642 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mägi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet. 26, 3639–3650 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Winkler, T. W. et al. EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data. Bioinformatics 31, 259–261 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Randall, J. C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Verhoeven, B. A. N. et al. Athero-express: differential atherosclerotic plaque expression of mRNA and protein in relation to cardiovascular events and patient characteristics. Rationale and design. Eur. J. Epidemiol. 19, 1127–1133 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Van Lammeren, G. W. et al. Atherosclerotic plaque vulnerability as an explanation for the increased risk of stroke in elderly undergoing carotid artery stenting. Stroke 42, 2550–2555 (2011).

    Article 
    PubMed 

    Google Scholar
     

  • Verhoeven, B. et al. Carotid atherosclerotic plaques in patients with transient ischemic attacks and stroke have unstable characteristics compared with plaques in asymptomatic and amaurosis fugax patients. J. Vasc. Surg. 42, 1075–1081 (2005).

    Article 
    PubMed 

    Google Scholar
     

  • Hellings, W. E. et al. Intraobserver and interobserver variability and spatial differences in histologic examination of carotid endarterectomy specimens. J. Vasc. Surg. 46, 1147–1154 (2007).

    Article 
    PubMed 

    Google Scholar
     

  • Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lawlor, D. A. Commentary: two-sample Mendelian randomization: opportunities and challenges. Int. J. Epidemiol. 45, 908–915 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in 700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bowden, J. & Holmes, M. V. Meta-analysis and Mendelian randomization: a review. Res. Synth. Methods 10, 486–496 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e005349 (2018).

    Article 

    Google Scholar
     

  • Hartiala, J. A. et al. Genome-wide analysis identifies novel susceptibility loci for myocardial infarction. Eur. Heart J. 42, 919–933 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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    1/ https://Google.com/

    2/ https://www.nature.com/articles/s41588-023-01518-4

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    What Are The Main Benefits Of Comparing Car Insurance Quotes Online

    LOS ANGELES, CA / ACCESSWIRE / June 24, 2020, / Compare-autoinsurance.Org has launched a new blog post that presents the main benefits of comparing multiple car insurance quotes. For more info and free online quotes, please visit https://compare-autoinsurance.Org/the-advantages-of-comparing-prices-with-car-insurance-quotes-online/ The modern society has numerous technological advantages. One important advantage is the speed at which information is sent and received. With the help of the internet, the shopping habits of many persons have drastically changed. The car insurance industry hasn't remained untouched by these changes. On the internet, drivers can compare insurance prices and find out which sellers have the best offers. View photos The advantages of comparing online car insurance quotes are the following: Online quotes can be obtained from anywhere and at any time. Unlike physical insurance agencies, websites don't have a specific schedule and they are available at any time. Drivers that have busy working schedules, can compare quotes from anywhere and at any time, even at midnight. Multiple choices. Almost all insurance providers, no matter if they are well-known brands or just local insurers, have an online presence. Online quotes will allow policyholders the chance to discover multiple insurance companies and check their prices. Drivers are no longer required to get quotes from just a few known insurance companies. Also, local and regional insurers can provide lower insurance rates for the same services. Accurate insurance estimates. Online quotes can only be accurate if the customers provide accurate and real info about their car models and driving history. Lying about past driving incidents can make the price estimates to be lower, but when dealing with an insurance company lying to them is useless. Usually, insurance companies will do research about a potential customer before granting him coverage. Online quotes can be sorted easily. Although drivers are recommended to not choose a policy just based on its price, drivers can easily sort quotes by insurance price. Using brokerage websites will allow drivers to get quotes from multiple insurers, thus making the comparison faster and easier. For additional info, money-saving tips, and free car insurance quotes, visit https://compare-autoinsurance.Org/ Compare-autoinsurance.Org is an online provider of life, home, health, and auto insurance quotes. This website is unique because it does not simply stick to one kind of insurance provider, but brings the clients the best deals from many different online insurance carriers. In this way, clients have access to offers from multiple carriers all in one place: this website. On this site, customers have access to quotes for insurance plans from various agencies, such as local or nationwide agencies, brand names insurance companies, etc. "Online quotes can easily help drivers obtain better car insurance deals. All they have to do is to complete an online form with accurate and real info, then compare prices", said Russell Rabichev, Marketing Director of Internet Marketing Company. CONTACT: Company Name: Internet Marketing CompanyPerson for contact Name: Gurgu CPhone Number: (818) 359-3898Email: [email protected]: https://compare-autoinsurance.Org/ SOURCE: Compare-autoinsurance.Org View source version on accesswire.Com:https://www.Accesswire.Com/595055/What-Are-The-Main-Benefits-Of-Comparing-Car-Insurance-Quotes-Online View photos

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