Meta-Analysis

. 2023 Oct;55(10):1651-1664.

doi: 10.1038/s41588-023-01518-4. Epub 2023 Sep 28.

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

Maryam Kavousi^#¹, Maxime M Bos^#², Hanna J Barnes^#³, Christian L Lino Cardenas^#³, Doris Wong^#^{4

5}, Haojie Lu^#^{2

6}, Chani J Hodonsky^#⁵, Lennart P L Landsmeer^#⁷, Adam W Turner⁵, Minjung Kho^{8

9}, Natalie R Hasbani¹⁰, Paul S de Vries¹⁰, Donald W Bowden¹¹, Sandesh Chopade^{12

13}, Joris Deelen^{14

15}, Ernest Diez Benavente¹⁶, Xiuqing Guo¹⁷, Edith Hofer^{18

19}, Shih-Jen Hwang²⁰, Sharon M Lutz²¹, Leo-Pekka Lyytikäinen²², Lotte Slenders⁷, Albert V Smith^{23

24}, Maggie A Stanislawski²⁵, Jessica van Setten²⁶, Quenna Wong²⁷, Lisa R Yanek²⁸, Diane M Becker²⁸, Marian Beekman¹⁴, Matthew J Budoff¹⁷, Mary F Feitosa²⁹, Chris Finan^{12

13

26}, Austin T Hilliard³⁰, Sharon L R Kardia⁸, Jason C Kovacic^{31

32

33}, Brian G Kral²⁸, Carl D Langefeld³⁴, Lenore J Launer³⁵, Shaista Malik³⁶, Firdaus A A Mohamed Hoesein³⁷, Michal Mokry^{7

16}, Reinhold Schmidt¹⁸, Jennifer A Smith^{8

38}, Kent D Taylor¹⁷, James G Terry³⁹, Jeroen van der Grond⁴⁰, Joyce van Meurs^{2

6}, Rozemarijn Vliegenthart⁴¹, Jianzhao Xu¹¹, Kendra A Young⁴², Nuno R Zilhão²⁴, Robert Zweiker⁴³, Themistocles L Assimes^{30

44}, Lewis C Becker²⁸, Daniel Bos^{2

45}, J Jeffrey Carr⁶, L Adrienne Cupples⁴⁶, Dominique P V de Kleijn⁴⁷, Menno de Winther⁴⁸, Hester M den Ruijter¹⁶, Myriam Fornage⁴⁹, Barry I Freedman⁵⁰, Vilmundur Gudnason^{24

51}, Aroon D Hingorani^{12

13}, John E Hokanson⁴⁴, M Arfan Ikram², Ivana Išgum^{52

53}, David R Jacobs Jr⁵⁴, Mika Kähönen⁵⁵, Leslie A Lange²⁵, Terho Lehtimäki²², Gerard Pasterkamp⁷, Olli T Raitakari^{56

57

58}, Helena Schmidt⁵⁹, P Eline Slagboom¹⁴, André G Uitterlinden^{2

6}, Meike W Vernooij^{2

47}, Joshua C Bis⁶⁰, Nora Franceschini⁶¹, Bruce M Psaty^{60

62}, Wendy S Post^{63

64}, Jerome I Rotter¹⁷, Johan L M Björkegren^{65

66}, Christopher J O'Donnell^{67

68}, Lawrence F Bielak⁸, Patricia A Peyser⁸, Rajeev Malhotra³, Sander W van der Laan⁷, Clint L Miller^{69

70

71}

Affiliations

¹ Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. m.kavousi@erasmusmc.nl.
² Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
³ Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁴ Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
⁵ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
⁶ Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁷ Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
⁸ Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
⁹ Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
¹⁰ Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Center at Houston, Houston, TX, USA.
¹¹ Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
¹² Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
¹³ University College London British Heart Foundation Research Accelerator Centre, London, UK.
¹⁴ Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
¹⁵ Max Planck Institute for Biology of Aging, Cologne, Germany.
¹⁶ Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
¹⁷ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁸ Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria.
¹⁹ Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
²⁰ Population Sciences, NHLBI/NIH, Framingham, MA, USA.
²¹ Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA.
²² Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
²³ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
²⁴ Icelandic Heart Association, Kopavogur, Iceland.
²⁵ Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA.
²⁶ Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
²⁷ Department of Biostatistics, University of Washington, Seattle, WA, USA.
²⁸ GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²⁹ Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA.
³⁰ VA Palo Alto Healthcare System, Palo Alto, CA, USA.
³¹ Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia.
³² St Vincent's Clinical School, University of NSW, Sydney, New South Wales, Australia.
³³ The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
³⁴ Department of Biostatistical Sciences and Data Science, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
³⁵ Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
³⁶ Susan Samueli Integrative Health Institute, Department of Medicine, University of California Irvine, Irvine, CA, USA.
³⁷ Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
³⁸ Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
³⁹ Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴⁰ Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
⁴¹ Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
⁴² Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Denver, CO, USA.
⁴³ Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria.
⁴⁴ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁴⁵ Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁴⁶ Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
⁴⁷ Department of Vascular Surgery, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
⁴⁸ Department of Medical Biochemistry, Experimental Vascular Biology, Amsterdam Cardiovascular Sciences: Atherosclerosis and Ischemic syndromes, Amsterdam Infection and Immunity: Inflammatory diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
⁴⁹ Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁵⁰ Department of Internal Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
⁵¹ Faculty of Medicine, School of Public Health, University of Iceland, Reykjavik, Iceland.
⁵² Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
⁵³ Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
⁵⁴ Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
⁵⁵ Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
⁵⁶ Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.
⁵⁷ Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
⁵⁸ Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.
⁵⁹ Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria.
⁶⁰ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
⁶¹ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
⁶² Departments of Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA.
⁶³ Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁶⁴ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁶⁵ Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
⁶⁶ Department of Medicine, Integrated Cardio Metabolic Centre, Karolinska Institutet, Huddinge, Sweden.
⁶⁷ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶⁸ Cardiology Section, Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, MA, USA.
⁶⁹ Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA. clintm@virginia.edu.
⁷⁰ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. clintm@virginia.edu.
⁷¹ Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA. clintm@virginia.edu.

^# Contributed equally.

PMID: 37770635
PMCID: PMC10601987
DOI: 10.1038/s41588-023-01518-4

Meta-Analysis

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

Maryam Kavousi et al. Nat Genet. 2023 Oct.

. 2023 Oct;55(10):1651-1664.

doi: 10.1038/s41588-023-01518-4. Epub 2023 Sep 28.

Authors

Affiliations

¹ Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands. m.kavousi@erasmusmc.nl.
² Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
³ Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
⁴ Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
⁵ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
⁶ Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁷ Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
⁸ Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
⁹ Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea.
¹⁰ Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Center at Houston, Houston, TX, USA.
¹¹ Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
¹² Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK.
¹³ University College London British Heart Foundation Research Accelerator Centre, London, UK.
¹⁴ Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
¹⁵ Max Planck Institute for Biology of Aging, Cologne, Germany.
¹⁶ Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
¹⁷ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA.
¹⁸ Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria.
¹⁹ Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
²⁰ Population Sciences, NHLBI/NIH, Framingham, MA, USA.
²¹ Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA.
²² Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
²³ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
²⁴ Icelandic Heart Association, Kopavogur, Iceland.
²⁵ Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA.
²⁶ Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
²⁷ Department of Biostatistics, University of Washington, Seattle, WA, USA.
²⁸ GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²⁹ Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA.
³⁰ VA Palo Alto Healthcare System, Palo Alto, CA, USA.
³¹ Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia.
³² St Vincent's Clinical School, University of NSW, Sydney, New South Wales, Australia.
³³ The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
³⁴ Department of Biostatistical Sciences and Data Science, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
³⁵ Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
³⁶ Susan Samueli Integrative Health Institute, Department of Medicine, University of California Irvine, Irvine, CA, USA.
³⁷ Department of Radiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
³⁸ Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
³⁹ Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
⁴⁰ Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
⁴¹ Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
⁴² Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Denver, CO, USA.
⁴³ Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria.
⁴⁴ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁴⁵ Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
⁴⁶ Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.
⁴⁷ Department of Vascular Surgery, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
⁴⁸ Department of Medical Biochemistry, Experimental Vascular Biology, Amsterdam Cardiovascular Sciences: Atherosclerosis and Ischemic syndromes, Amsterdam Infection and Immunity: Inflammatory diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
⁴⁹ Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
⁵⁰ Department of Internal Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA.
⁵¹ Faculty of Medicine, School of Public Health, University of Iceland, Reykjavik, Iceland.
⁵² Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
⁵³ Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
⁵⁴ Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
⁵⁵ Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
⁵⁶ Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.
⁵⁷ Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.
⁵⁸ Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.
⁵⁹ Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria.
⁶⁰ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.
⁶¹ Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
⁶² Departments of Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA.
⁶³ Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁶⁴ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
⁶⁵ Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
⁶⁶ Department of Medicine, Integrated Cardio Metabolic Centre, Karolinska Institutet, Huddinge, Sweden.
⁶⁷ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
⁶⁸ Cardiology Section, Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, MA, USA.
⁶⁹ Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA. clintm@virginia.edu.
⁷⁰ Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA. clintm@virginia.edu.
⁷¹ Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA. clintm@virginia.edu.

^# Contributed equally.

PMID: 37770635
PMCID: PMC10601987
DOI: 10.1038/s41588-023-01518-4

Abstract

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

PubMed Disclaimer

Conflict of interest statement

Competing Interests

S.W.v.d.L. has received Roche funding for unrelated work. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. R.M. receives research funding from Angea Biotherapeutics and Amgen and serves as a consultant for Myokardia/BMS, Renovacor, Epizon Pharma, and Third Pole, all unrelated to the current project. C.L.M. has received funding from AstraZeneca on an unrelated project. J.C.K. is the recipient of an Agilent Thought Leader Award (January 2022), which includes funding for research that is unrelated to the current manuscript. All other co-authors confirm that they have no conflicts of interest related to the presented work.

Figures

**Figure 1 |. Study summary.**
Schematic of study design and meta-analysis for CAC in European and African ancestry participants (1 and 2), main results identifying 16 lead SNPs in 11 loci (3), and post-GWAS analyses involving variant annotation and gene mapping *in silico*, functional validation in vitro, and druggability analysis (4). Figure created in BioRender.

**Figure 2 |. Prioritization of CAC causal genes using STARNET eQTLs.**
a,b, Summary-based Mendelian Randomization (SMR) to identify causal CAC genes using subclinical atherosclerotic mammary artery (MAM, a) and atherosclerotic aortic root (AOR, b) tissue *cis*-eQTLs in STARNET. Dashed black and red lines indicate SMR P-value significance thresholds for tested CAC and CAD candidate genes, respectively. Teal green dots represent significant genes for both CAC and CAD, while blue dots represent those only significant for CAC. SMR P-value determined by approximate chi-square test statistic for mediating effect of gene expression on CAC or CAD. c,d, Coloc-based colocalization analysis of CAC and CAD candidate genes using STARNET MAM (c) and AOR (d) *cis*-eQTLs. Dashed black lines indicate high colocalization (PP4 > 0.8) and dashed red lines indicate low colocalization (PP4 < 0.5) thresholds, respectively. Teal green dots represent high colocalized genes (PP4 > 0.8) for both CAC and CAD, whereas blue dots represent those highly colocalized for CAC (PP4 > 0.8) but not CAD (PP4 < 0.5).

**Figure 3 |. Single-nucleus coronary epigenomic annotation of *ARID5B* and *IGFBP3* CAC loci.**
a,b, Top, *ARID5B* (a) and *IGFBP3* (b) locus association plots showing CAC meta-analysis results in European and African American ancestry individuals with credible set SNPs color coded by posterior probability (red). Meta-analysis P-values determined from weighted Z-scores in fixed effects model and central association P-values determined from chi-square test statistic. Bottom, overlapping chromatin accessibility profiles in coronary artery cell types determined by bulk and single-nucleus ATAC-seq. Peak2gene links highlight predicted enhancer promoter interactions across all cell types with those overlapping CAC SNPs shown in black. Light grey box highlights lead SNP at each locus. SMC, smooth muscle cells; T/NK: T-cells or natural killer cells.

**Figure 4 |. Genetic correlations for CAC and Mendelian randomization for cardiovascular disease risk factors.**
a, Cross-trait LD-score regression-based genetic correlation of CAC quantity with cardiovascular disease risk factors, anthropomorphic risk factors, family history, subclinical and clinical cardiovascular disease using European ancestry CAC and UK Biobank trait associations. Vertical dashed line set at genetic covariance = 0. Values represent the genetic correlation estimates using the slope from the regression of the product of Z-scores from two GWAS studies on the LD score and error bars represent the standard error estimates of the LD score. P-values (two-tailed) are computed from chi-square test statistics. b, Mendelian randomization (MR) results showing causal effects of cardiovascular disease and anthropomorphic risk factors on CAC quantity using the inverse variance-weighted method (IVW). Values represent the mean odds ratio and error bars reflect the 95% confidence intervals. P-values (two-tailed t test) < 7.14 × 10⁻³ (0.05/7) are considered statistically significant. Vertical dashed line set at odds ratio = 1.0. c, MR results showing causal effects of CAC quantity on coronary artery disease (CAD) using either 16 independent lead SNPs at the 11 CAC loci or 5 lead SNPs from the 5 CAC-specific loci (separated by horizontal dashed line). Values represent the mean odds ratio and error bars reflect the 95% confidence intervals. P-values (two-tailed t test) < 0.05 are considered statistically significant. Different MR methods used are shown, including MR-Egger, IVW, and Weighted median. Sample sizes for a-c are provided in Supplementary Tables 15 and 16. LDL, low-density lipoprotein; HDL, high-density lipoprotein; cIMT: carotid intima-media thickness.

**Figure 5 |. Immunofluorescence staining showing localization of ENPP1, IGFBP3, ARID5B and ADK in control and atherosclerotic human coronary arteries.**
a,b, Transverse sections of healthy control (a) and atherosclerotic (b) human coronary arteries were stained for alpha-smooth muscle actin (α-SMA) (green), DAPI nuclei marker (blue), ENPP1/PC-1 (white), IGFBP3 (red), ARID5B (purple), and ADK (yellow). High levels of ENPP1/PC-1, IGFBP3, ARID5B, and ADK were observed in the neointimal layer of atherosclerotic diseased coronary arteries. Whole artery images were captured at 10× magnification and regions of interest were captured at 20×. Images are representative of n = 4 independent donors per group.

**Figure 6 |. Functional assays of *ENPP1*, *IGFBP3*, *ARID5B*, and *ADK* in coronary artery calcification and vascular smooth muscle cell phenotype.**
a, Effects of siRNA-mediated knockdown of *ENPP1*, *IGFBP3*, *ARID5B*, and *ADK* on osteogenic marker *RUNX2* gene expression in human coronary artery smooth muscle cells (HCASMCs) cultured in control or osteogenic media (OM). n = 6 biological replicates per group. b, Western blot analysis of RUNX2 protein levels in HCASMCs cultured in basal control or OM and transfected with siCTRL, siENPP1, siIGFBP3, siARID5B, or siADK. GAPDH was used as a loading control. c, Effects of siRNA knockdown on *CNN1* mRNA expression. n = 6 biological replicates per group. d, Calcification assessed by Alizarin Red staining in HCASMCs cultured in OM and transfected with siCTRL, siENPP1, siIGFBP3, siARID5B, or siADK. Representative images from three independent experiments. e, MTT assay-based proliferation in HCASMCs transfected with siCTRL, siENPP1, siIGFBP3, siARID5B or siADK. n = 12 biological replicates per treatment and control group; each individual sample read three times; value reported represents the mean of three technical replicates. f,g, Scratch wound-based migration assay in HCASMCs transfected with siCTRL, siENPP1, siIGFBP3, siARID5B or siADK, as quantified in g. An initial image of the assay is provided to demonstrate creation of a cell-free gap, prior to incubation with OM. n = 6 biological replicates per treatment and control group. Scale bars, 200 μm. All statistical comparisons shown were made using a two-tailed one-way ANOVA with Sidak’s test for multiple comparisons. Values in a-g represent mean ± standard error of the mean. Full-length blots are provided as **Source Data**.

**Figure 7 |. Schematic of CAC candidate genes and approved or investigational drugs.**
a, Summary of gene-drug interactions for CAC candidate genes using DGIdb and DrugBank databases. Novel CAC-specific genes are shown in blue and other genes are shown in light blue corresponding to each genomic locus. Generic names for the top interacting drugs/compounds are shown for each target in red. b, Schematic showing the cellular location of the protein targets for novel CAC genes. Names of interacting approved drugs or compounds under investigation are shown along with predicted pharmacological interaction if known. ECM, extracellular matrix; ATP, adenosine triphosphate; AMP, adenosine monophosphate; PPi, inorganic pyrophosphate; PEG, polyethylene glycol. Figure created in BioRender.

See this image and copyright information in PMC

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Methods-only References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Molecular Biology Databases

Miscellaneous