Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci (original) (raw)

References

1. Lim, S.S. et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224–2260 (2012).
   Article PubMed PubMed Central Google Scholar
2. Toka, H.R. & Luft, F.C. Monogenic forms of human hypertension. Semin. Nephrol. 22, 81–88 (2002).
   Article CAS PubMed Google Scholar
3. Toka, H.R., Koshy, J.M. & Hariri, A. The molecular basis of blood pressure variation. Pediatr. Nephrol. 28, 387–399 (2013).
   Article PubMed Google Scholar
4. Garovic, V.D., Hilliard, A.A. & Turner, S.T. Monogenic forms of low-renin hypertension. Nat. Clin. Pract. Nephrol. 2, 624–630 (2006).
   Article CAS PubMed Google Scholar
5. Zhu, X. et al. Combined admixture mapping and association analysis identifies a novel blood pressure genetic locus on 5p13: contributions from the CARe consortium. Hum. Mol. Genet. 20, 2285–2295 (2011).
   Article CAS PubMed PubMed Central Google Scholar
6. Tragante, V. et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci. Am. J. Hum. Genet. 94, 349–360 (2014).
   Article CAS PubMed PubMed Central Google Scholar
7. Wain, L.V. et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat. Genet. 43, 1005–1011 (2011).
   Article CAS PubMed PubMed Central Google Scholar
8. Padmanabhan, S., Newton-Cheh, C. & Dominiczak, A.F. Genetic basis of blood pressure and hypertension. Trends Genet. 28, 397–408 (2012).
   Article CAS PubMed Google Scholar
9. Johnson, A.D. et al. Association of hypertension drug target genes with blood pressure and hypertension in 86,588 individuals. Hypertension 57, 903–910 (2011).
   Article CAS PubMed Google Scholar
10. Johnson, T. et al. Blood pressure loci identified with a gene-centric array. Am. J. Hum. Genet. 89, 688–700 (2011).
    Article CAS PubMed PubMed Central Google Scholar
11. Ganesh, S.K. et al. Loci influencing blood pressure identified using a cardiovascular gene-centric array. Hum. Mol. Genet. 22, 1663–1678 (2013).
    Article CAS PubMed PubMed Central Google Scholar
12. Franceschini, N. et al. Genome-wide association analysis of blood-pressure traits in African-ancestry individuals reveals common associated genes in African and non-African populations. Am. J. Hum. Genet. 93, 545–554 (2013).
    Article CAS PubMed PubMed Central Google Scholar
13. Newton-Cheh, C. et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666–676 (2009).
    Article CAS PubMed PubMed Central Google Scholar
14. Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 (2009).
    Article CAS PubMed PubMed Central Google Scholar
15. Ehret, G.B. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).
    Article CAS PubMed Google Scholar
16. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP–trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
    Article CAS PubMed Google Scholar
17. Oliver, P.M. et al. Natriuretic peptide receptor 1 expression influences blood pressures of mice in a dose-dependent manner. Proc. Natl. Acad. Sci. USA 95, 2547–2551 (1998).
    Article CAS PubMed PubMed Central Google Scholar
18. Oliver, P.M. et al. Hypertension, cardiac hypertrophy, and sudden death in mice lacking natriuretic peptide receptor A. Proc. Natl. Acad. Sci. USA 94, 14730–14735 (1997).
    Article CAS PubMed PubMed Central Google Scholar
19. 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
20. Barrett, J.C. et al. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat. Genet. 40, 955–962 (2008).
    Article CAS PubMed PubMed Central Google Scholar
21. Fernando, M.M. et al. Transancestral mapping of the MHC region in systemic lupus erythematosus identifies new independent and interacting loci at MSH5, HLA-DPB1 and HLA-G. Ann. Rheum. Dis. 71, 777–784 (2012).
    Article CAS PubMed Google Scholar
22. Plenge, R.M. et al. TRAF1-C5 as a risk locus for rheumatoid arthritis—a genomewide study. N. Engl. J. Med. 357, 1199–1209 (2007).
    Article CAS PubMed PubMed Central Google Scholar
23. Lippert, C. et al. An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data. Sci. Rep. 3, 1099 (2013).
    Article CAS PubMed PubMed Central Google Scholar
24. Qiu, L. et al. Quantitative assessment of the effect of KCNJ11 gene polymorphism on the risk of type 2 diabetes. PLoS One 9, e93961 (2014).
    Article CAS PubMed PubMed Central Google Scholar
25. Phani, N.M. et al. Population specific impact of genetic variants in KCNJ11 gene to type 2 diabetes: a case–control and meta-analysis study. PLoS One 9, e107021 (2014).
    Article CAS PubMed PubMed Central Google Scholar
26. Chambers, J.C. et al. Genetic loci influencing kidney function and chronic kidney disease. Nat. Genet. 42, 373–375 (2010).
    Article CAS PubMed PubMed Central Google Scholar
27. Elks, C.E. et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat. Genet. 42, 1077–1085 (2010).
    Article CAS PubMed PubMed Central Google Scholar
28. Eijgelsheim, M. et al. Genome-wide association analysis identifies multiple loci related to resting heart rate. Hum. Mol. Genet. 19, 3885–3894 (2010).
    Article CAS PubMed PubMed Central Google Scholar
29. Heid, I.M. et al. Meta-analysis identifies 13 new loci associated with waist–hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).
    Article CAS PubMed PubMed Central Google Scholar
30. Paré, G. et al. Novel associations of CPS1, MUT, NOX4, and DPEP1 with plasma homocysteine in a healthy population: a genome-wide evaluation of 13 974 participants in the Women's Genome Health Study. Circ Cardiovasc Genet 2, 142–150 (2009).
    Article CAS PubMed PubMed Central Google Scholar
31. Brooks, J.D. et al. Variants in tamoxifen metabolizing genes: a case–control study of contralateral breast cancer risk in the WECARE study. Int. J. Mol. Epidemiol. Genet. 4, 35–48 (2013).
    CAS PubMed PubMed Central Google Scholar
32. Geller, F. et al. Genome-wide association analyses identify variants in developmental genes associated with hypospadias. Nat. Genet. 46, 957–963 (2014).
    Article CAS PubMed Google Scholar
33. Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977–983 (2011).
34. Tetsuro, M. et al. Identification of group of hypertension-susceptibility genes. Chinese patent CN103667326 B (2016).
35. Ingelsson, E., Syvänen, A.C. & Lind, L. Endothelium-dependent vasodilation in conduit and resistance vessels in relation to the endothelial nitric oxide synthase gene. J. Hum. Hypertens. 22, 569–578 (2008).
    Article CAS PubMed Google Scholar
36. Chasman, D.I. et al. Genome-wide association study reveals three susceptibility loci for common migraine in the general population. Nat. Genet. 43, 695–698 (2011).
    Article CAS PubMed PubMed Central Google Scholar
37. Arndt, A.K. et al. Fine mapping of the 1p36 deletion syndrome identifies mutation of PRDM16 as a cause of cardiomyopathy. Am. J. Hum. Genet. 93, 67–77 (2013).
    Article CAS PubMed PubMed Central Google Scholar
38. Cohen, P. et al. Ablation of PRDM16 and beige adipose causes metabolic dysfunction and a subcutaneous to visceral fat switch. Cell 156, 304–316 (2014).
    Article CAS PubMed PubMed Central Google Scholar
39. Castaño Betancourt, M.C. et al. Genome-wide association and functional studies identify the DOT1L gene to be involved in cartilage thickness and hip osteoarthritis. Proc. Natl. Acad. Sci. USA 109, 8218–8223 (2012).
    Article PubMed PubMed Central Google Scholar
40. Morgenthaler, S. & Thilly, W.G. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat. Res. 615, 28–56 (2007).
    Article CAS PubMed Google Scholar
41. Li, B. & Leal, S.M. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am. J. Hum. Genet. 83, 311–321 (2008).
    Article CAS PubMed PubMed Central Google Scholar
42. Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).
    Article CAS PubMed PubMed Central Google Scholar
43. Ji, W. et al. Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat. Genet. 40, 592–599 (2008).
    Article CAS PubMed PubMed Central Google Scholar
44. Febbo, P.G. et al. Literature Lab: a method of automated literature interrogation to infer biology from microarray analysis. BMC Genomics 8, 461 (2007).
    Article PubMed PubMed Central Google Scholar
45. Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).
    Article CAS PubMed PubMed Central Google Scholar
46. Ward, L.D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012).
    Article CAS PubMed Google Scholar
47. Naiche, L.A., Harrelson, Z., Kelly, R.G. & Papaioannou, V.E. T-box genes in vertebrate development. Annu. Rev. Genet. 39, 219–239 (2005).
    Article CAS PubMed Google Scholar
48. Chapman, D.L. et al. Expression of the T-box family genes, Tbx1_–_Tbx5, during early mouse development. Dev. Dyn. 206, 379–390 (1996).
    Article CAS PubMed Google Scholar
49. Leslie, R., O'Donnell, C.J. & Johnson, A.D. GRASP: analysis of genotype–phenotype results from 1390 genome-wide association studies and corresponding open access database. Bioinformatics 30, i185–i194 (2014).
    Article CAS PubMed PubMed Central Google Scholar
50. Westra, H.J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
    CAS PubMed PubMed Central Google Scholar
51. Kabakchiev, B. & Silverberg, M.S. Expression quantitative trait loci analysis identifies associations between genotype and gene expression in human intestine. Gastroenterology 144, 1488–1496 (2013).
    Article CAS PubMed Google Scholar
52. Murphy, A. et al. Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes. Hum. Mol. Genet. 19, 4745–4757 (2010).
    CAS PubMed PubMed Central Google Scholar
53. Zeller, T. et al. Genetics and beyond—the transcriptome of human monocytes and disease susceptibility. PLoS One 5, e10693 (2010).
    Article CAS PubMed PubMed Central Google Scholar
54. Heap, G.A. et al. Complex nature of SNP genotype effects on gene expression in primary human leucocytes. BMC Med. Genomics 2, 1 (2009).
    Article CAS PubMed PubMed Central Google Scholar
55. Stranger, B.E. et al. Patterns of cis regulatory variation in diverse human populations. PLoS Genet. 8, e1002639 (2012).
    Article CAS PubMed PubMed Central Google Scholar
56. Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).
    Article CAS PubMed PubMed Central Google Scholar
57. Schadt, E.E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).
    Article CAS PubMed PubMed Central Google Scholar
58. 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).
59. Wu, D.A. et al. Quantitative trait locus mapping of human blood pressure to a genetic region at or near the lipoprotein lipase gene locus on chromosome 8p22. J. Clin. Invest. 97, 2111–2118 (1996).
    Article CAS PubMed PubMed Central Google Scholar
60. Goodarzi, M.O. et al. Lipoprotein lipase is a gene for insulin resistance in Mexican Americans. Diabetes 53, 214–220 (2004).
    Article CAS PubMed Google Scholar
61. Goodarzi, M.O. et al. The 3′ untranslated region of the lipoprotein lipase gene: haplotype structure and association with post-heparin plasma lipase activity. J. Clin. Endocrinol. Metab. 90, 4816–4823 (2005).
    Article CAS PubMed Google Scholar
62. Goodarzi, M.O. et al. Haplotypes in the lipoprotein lipase gene influence fasting insulin and discovery of a new risk haplotype. J. Clin. Endocrinol. Metab. 92, 293–296 (2007).
    Article CAS PubMed Google Scholar
63. Kraja, A.T. et al. A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium. Diabetes 60, 1329–1339 (2011).
    Article CAS PubMed PubMed Central Google Scholar
64. Kraja, A.T. et al. Pleiotropic genes for metabolic syndrome and inflammation. Mol. Genet. Metab. 112, 317–338 (2014).
    Article CAS PubMed PubMed Central Google Scholar
65. Adzhubei, I., Jordan, D.M. & Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. Chapter 7, Unit 7.20 (2013).
66. Das, S., Au, E., Krazit, S.T. & Pandey, K.N. Targeted disruption of guanylyl cyclase-A/natriuretic peptide receptor-A gene provokes renal fibrosis and remodeling in null mutant mice: role of proinflammatory cytokines. Endocrinology 151, 5841–5850 (2010).
    Article CAS PubMed PubMed Central Google Scholar
67. Robertson, D. et al. Isolated failure of autonomic noradrenergic neurotransmission. Evidence for impaired β-hydroxylation of dopamine. N. Engl. J. Med. 314, 1494–1497 (1986).
    Article CAS PubMed Google Scholar
68. Biaggioni, I., Goldstein, D.S., Atkinson, T. & Robertson, D. Dopamine-β-hydroxylase deficiency in humans. Neurology 40, 370–373 (1990).
    Article CAS PubMed Google Scholar
69. Kim, C.H. et al. Mutations in the dopamine β-hydroxylase gene are associated with human norepinephrine deficiency. Am. J. Med. Genet. 108, 140–147 (2002).
    Article PubMed Google Scholar
70. Kapoor, A., Shandilya, M. & Kundu, S. Structural insight of dopamine β-hydroxylase, a drug target for complex traits, and functional significance of exonic single nucleotide polymorphisms. PLoS One 6, e26509 (2011).
    Article CAS PubMed PubMed Central Google Scholar
71. Velasco, M., Gilbert, C.A., Rutledge, C.O. & McNay, J.L. Antihypertensive effect of a dopamine β hydroxylase inhibitor, bupicomide: a comparison with hydralazine. Clin. Pharmacol. Ther. 18, 145–153 (1975).
    Article CAS PubMed Google Scholar
72. Dhalla, N.S., Adameova, A. & Kaur, M. Role of catecholamine oxidation in sudden cardiac death. Fundam. Clin. Pharmacol. 24, 539–546 (2010).
    Article CAS PubMed Google Scholar
73. Leon, A.S. & Abrams, W.B. The role of catecholamines in producing arrhythmias. Am. J. Med. Sci. 262, 9–13 (1971).
    Article CAS PubMed Google Scholar
74. Pagliarini, D.J. et al. Involvement of a mitochondrial phosphatase in the regulation of ATP production and insulin secretion in pancreatic beta cells. Mol. Cell 19, 197–207 (2005).
    Article CAS PubMed Google Scholar
75. Grove, M.L. et al. Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One 8, e68095 (2013).
    Article CAS PubMed PubMed Central Google Scholar
76. Gauderman, W.J. Sample size requirements for association studies of gene-gene interaction. Am. J. Epidemiol. 155, 478–484 (2002).
    Article PubMed Google Scholar
77. Borenstein, M., Hedges, L.V., Higgins, J.P.T. & Rothstein, H.R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).
    Article PubMed Google Scholar
78. Liu, D.J. et al. Meta-analysis of gene-level tests for rare variant association. Nat. Genet. 46, 200–204 (2014).
    Article CAS PubMed Google Scholar
79. Zaykin, D.V. Optimally weighted _Z_-test is a powerful method for combining probabilities in meta-analysis. J. Evol. Biol. 24, 1836–1841 (2011).
    Article CAS PubMed PubMed Central Google Scholar
80. Stergachis, A.B. et al. Exonic transcription factor binding directs codon choice and affects protein evolution. Science 342, 1367–1372 (2013).
    Article CAS PubMed PubMed Central Google Scholar
81. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
    Google Scholar
82. Zhong, H., Yang, X., Kaplan, L.M., Molony, C. & Schadt, E.E. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am. J. Hum. Genet. 86, 581–591 (2010).
    Article CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank the two anonymous reviewers and editors for their helpful comments. Study-specific funding sources and acknowledgments are reported in the Supplementary Note.

Author information

Author notes

1. Chunyu Liu, Aldi T Kraja, Jennifer A Smith, Jennifer A Brody, Nora Franceschini and Christopher Newton-Cheh: These authors contributed equally to this work.
2. Georg B Ehret, Christopher Newton-Cheh, Daniel Levy and Daniel I Chasman: These authors jointly directed this work.

Authors and Affiliations

1. Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts, USA
   Chunyu Liu, Audrey Y Chu, Martin G Larson, Shih-Jen Hwang, Tianxiao Huan, Ramachandran S Vasan, Christopher J O'Donnell & Daniel Levy
2. Department of Biostatistics, School of Public Health, Boston University, Boston, Massachusetts, USA
   Chunyu Liu & Martin G Larson
3. Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
   Chunyu Liu, Audrey Y Chu, Shih-Jen Hwang, Tianxiao Huan & Daniel Levy
4. Division of Statistical Genomics, Department of Genetics and Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, USA
   Aldi T Kraja, E Warwick Daw & Ingrid B Borecki
5. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
   Jennifer A Smith, Wei Zhao & Sharon L R Kardia
6. Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA
   Jennifer A Brody, Joshua C Bis & Bruce M Psaty
7. Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
   Nora Franceschini
8. Department of Biostatistics, University of Washington, Seattle, Washington, USA
   Kenneth Rice
9. Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
   Alanna C Morrison, Megan L Grove & Eric Boerwinkle
10. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Yingchang Lu, Erwin P Bottinger, Omri Gottesman & Ruth J F Loos
11. DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
    Stefan Weiss, Marcus Dörr, Stephan B Felix, Rainer Rettig, Henry Völzke & Uwe Völker
12. Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany
    Stefan Weiss & Uwe Völker
13. Los Angeles Biomedical Research Institute and Department of Pediatrics, Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, California, USA
    Xiuqing Guo, Yii-Der Ida Chen, Jie Yao, Kent D Taylor, Eric Kim & Jerome I Rotter
14. Division of General Medicine, Columbia University Medical Center, New York, New York, USA
    Walter Palmas
15. George Washington University School of Medicine and Health Sciences, Washington, DC, USA
    Lisa W Martin
16. Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
    Praveen Surendran
17. Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK
    Fotios Drenos
18. MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
    Fotios Drenos
19. Department of Biostatistics, University of Liverpool, Liverpool, UK
    James P Cook
20. Department of Health Sciences, University of Leicester, Leicester, UK
    James P Cook
21. Joseph J. Zilber School of Public Health, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin, USA
    Paul L Auer
22. Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
    Audrey Y Chu, Franco Giulianini, Paul M Ridker & Daniel I Chasman
23. Vanderbilt Epidemiology Center, Vanderbilt Genetics Institute, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, Tennessee, USA
    Ayush Giri, Krystal S Tsosie, Digna R Velez Edwards & Todd L Edwards
24. Icelandic Heart Association, Kopavogur, Iceland
    Johanna Jakobsdottir, Albert V Smith & Vilmundur Gudnason
25. Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA
    Li-An Lin & Myriam Fornage
26. Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
    Jeanette M Stafford
27. Department of Epidemiology, Genetic Epidemiology Unit, Erasmus MC, Rotterdam, the Netherlands
    Najaf Amin & Cornelia M van Duijn
28. Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, Mississippi, USA
    Hao Mei
29. Bill and Melinda Gates Foundation, Seattle, Washington, USA
    Arend Voorman
30. Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, USA
    Martin G Larson
31. Faculty of Medicine, University of Iceland, Reykjavik, Iceland
    Albert V Smith & Vilmundur Gudnason
32. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
    Han Chen
33. Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
    Gulum Kosova, Sekar Kathiresan & Christopher Newton-Cheh
34. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, Massachusetts, USA
    Gulum Kosova, Sekar Kathiresan & Christopher Newton-Cheh
35. Division of Cardiology, Department of Medicine and Department of Genetics, Washington University School of Medicine, Missouri, St. Louis, USA
    Nathan O Stitziel
36. Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
    Nilesh Samani
37. NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, UK
    Nilesh Samani
38. Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
    Heribert Schunkert
39. DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance, Munich, Germany
    Heribert Schunkert
40. Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
    Panos Deloukas
41. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
    Panos Deloukas
42. Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
    Man Li
43. Center for Biomedicine, European Academy of Bozen/Bolzano (EURAC), Bolzano, Italy (affiliated with the University of Lübeck, Lübeck, Germany).,
    Christian Fuchsberger & Cristian Pattaro
44. Department of Genetic Epidemiology, Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
    Mathias Gorski
45. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
    Charles Kooperberg
46. Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA
    George J Papanicolaou & Jacques E Rossouw
47. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
    Jessica D Faul & David R Weir
48. Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA
    Claude Bouchard
49. Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
    Leslie J Raffel
50. Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
    André G Uitterlinden & Oscar H Franco
51. Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands
    André G Uitterlinden
52. Department of Preventive Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
    Ramachandran S Vasan
53. Department of Medicine, Cardiology Section, Boston Veterans Administration Healthcare, Boston, Massachusetts, USA
    Christopher J O'Donnell
54. Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA
    Christopher J O'Donnell
55. Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
    Christopher J O'Donnell
56. Northwestern University School of Medicine, Chicago, Illinois, USA
    Kiang Liu
57. Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
    Santhi Ganesh
58. Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
    Santhi Ganesh
59. Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
    Elias Salfati, Aravinda Chakravarti & Georg B Ehret
60. Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, US National Institutes of Health, Bethesda, Maryland, USA
    Tamara B Harris
61. Neuroepidemiology Section, National Institute on Aging, US National Institutes of Health, Bethesda, Maryland, USA
    Lenore J Launer
62. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
    Marcus Dörr & Stephan B Felix
63. Institute of Physiology, University of Greifswald, Greifswald, Germany
    Rainer Rettig
64. DZD (German Center for Diabetes Research), site Greifswald, Greifswald, Germany
    Henry Völzke
65. Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
    Henry Völzke
66. Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
    Wen-Jane Lee
67. Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
    I-Te Lee & Wayne H-H Sheu
68. School of Medicine, National Yang-Ming University, Taipei, Taiwan
    I-Te Lee & Wayne H-H Sheu
69. School of Medicine, Chung Shan Medical University, Taichung, Taiwan
    I-Te Lee
70. Institute of Medical Technology, National Chung-Hsing University, Taichung, Taiwan
    Wayne H-H Sheu
71. School of Medicine, National Defense Medical Center, Taipei, Taiwan
    Wayne H-H Sheu
72. Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
    Digna R Velez Edwards
73. Epidemiology and Prevention Center for Genomics and Personalized Medicine Research, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA
    Yongmei Liu
74. Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
    Adolfo Correa
75. Harvard Medical School, Boston, Massachusetts, USA
    Paul M Ridker & Daniel I Chasman
76. Department of Epidemiology, University of Washington, Seattle, Washington, USA
    Alexander P Reiner & Bruce M Psaty
77. Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
    Todd L Edwards
78. Los Angeles Biomedical Research Institute and Department of Medicine, Institute for Translational Genomics and Population Sciences, Harbor-UCLA Medical Center, Torrance, California, USA
    Jerome I Rotter
79. Department of Health Services, University of Washington, Seattle, Washington, USA
    Bruce M Psaty
80. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Bruce M Psaty
81. Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Ruth J F Loos
82. Cardiology, Geneva University Hospitals, Geneva, Switzerland
    Georg B Ehret
83. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
    Christopher Newton-Cheh

Authors

1. Chunyu Liu
2. Aldi T Kraja
3. Jennifer A Smith
4. Jennifer A Brody
5. Nora Franceschini
6. Joshua C Bis
7. Kenneth Rice
8. Alanna C Morrison
9. Yingchang Lu
10. Stefan Weiss
11. Xiuqing Guo
12. Walter Palmas
13. Lisa W Martin
14. Yii-Der Ida Chen
15. Praveen Surendran
16. Fotios Drenos
17. James P Cook
18. Paul L Auer
19. Audrey Y Chu
20. Ayush Giri
21. Wei Zhao
22. Johanna Jakobsdottir
23. Li-An Lin
24. Jeanette M Stafford
25. Najaf Amin
26. Hao Mei
27. Jie Yao
28. Arend Voorman
29. Martin G Larson
30. Megan L Grove
31. Albert V Smith
32. Shih-Jen Hwang
33. Han Chen
34. Tianxiao Huan
35. Gulum Kosova
36. Nathan O Stitziel
37. Sekar Kathiresan
38. Nilesh Samani
39. Heribert Schunkert
40. Panos Deloukas
41. Man Li
42. Christian Fuchsberger
43. Cristian Pattaro
44. Mathias Gorski
45. Charles Kooperberg
46. George J Papanicolaou
47. Jacques E Rossouw
48. Jessica D Faul
49. Sharon L R Kardia
50. Claude Bouchard
51. Leslie J Raffel
52. André G Uitterlinden
53. Oscar H Franco
54. Ramachandran S Vasan
55. Christopher J O'Donnell
56. Kent D Taylor
57. Kiang Liu
58. Erwin P Bottinger
59. Omri Gottesman
60. E Warwick Daw
61. Franco Giulianini
62. Santhi Ganesh
63. Elias Salfati
64. Tamara B Harris
65. Lenore J Launer
66. Marcus Dörr
67. Stephan B Felix
68. Rainer Rettig
69. Henry Völzke
70. Eric Kim
71. Wen-Jane Lee
72. I-Te Lee
73. Wayne H-H Sheu
74. Krystal S Tsosie
75. Digna R Velez Edwards
76. Yongmei Liu
77. Adolfo Correa
78. David R Weir
79. Uwe Völker
80. Paul M Ridker
81. Eric Boerwinkle
82. Vilmundur Gudnason
83. Alexander P Reiner
84. Cornelia M van Duijn
85. Ingrid B Borecki
86. Todd L Edwards
87. Aravinda Chakravarti
88. Jerome I Rotter
89. Bruce M Psaty
90. Ruth J F Loos
91. Myriam Fornage
92. Georg B Ehret
93. Christopher Newton-Cheh
94. Daniel Levy
95. Daniel I Chasman

Consortia

CHD Exome+ Consortium

ExomeBP Consortium

GoT2DGenes Consortium

T2D-GENES Consortium

Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia

CKDGen Consortium

Contributions

Study design: A.T.K., C.L., N.F., G.B.E., C.N.-C., J.I.R., B.M.P., D.L., D.I.C. Phenotyping: E.B., V.G., B.M.P., D.L., D.R.W., A. Correa, A. Chakravarti, W.P., M.D., R.R., W.H.-H.S., P.M.R., A.P.R., J.E.R., C.K., N.F., K.L., C.B., Y.-D.I.C., A.T.K., M.G.L., L.J.R., E.P.B., O.G., H.V., W.-J.L., J.I.R., O.H.F., R.S.V., R.J.F.L., A. Correa, A. Chakravarti, T.L.E., I.-T.L., L.W.M., G.J.P. Genotyping: E.B., D.L., A.P.R., C.K., Y.-D.I.C., M.F., C.J.O'D., S.L.R.K., U.V., D.I.C., C.N.-C., J.A.B., J.C.B., E.W.D., K.D.T., C.L., J.A.S., W.Z., J.D.F., Y.-D.I.C., S.W., E.K., A.G.U., A.Y.C., J.I.R., B.M.P., D.R.V.E., Y. Liu, C.M.v.D., I.B.B., R.J.F.L., L.J.L., T.B.H., T.L.E., S.B.F., F.G., P.L.A., M.L.G. Quality control: A.P.R., D.I.C., C.N.-C., J.A.B., J.C.B., E.W.D., K.D.T., C.L., S.-J.H., J.A.S., W.Z., J.D.F., S.W., A.Y.C., F.G., P.L.A., M.L.G., M.D., H.V., G.B.E., A.C.M., J.J., A.V.S., L. Lin. Software development: J.A.B., C.L., A.Y.C., F.G., P.L.A., A.T.K., K.R., A.V., H.C., D.I.C. Statistical analysis: A.P.R., D.I.C., C.N.-C., G.K., J.A.B., J.C.B., C.L., Y. Lu, J.A.S., W.Z., J.D.F., S.W., A.Y.C., F.G., P.L.A., G.B.E., A.C.M., J.J., A.V.S., L. Lin, J.M.S., N.A., K.S.T., T.H., A.G., C.K., N.F., A.T.K., M.G.L., S.G., E.S., K.R., H.M., X.G., J.Y., P.S., F.D., J.P.C., S.K., N.O.S., H.S., P.D., N.S., C.F., M.G., M.L., C.P. Manuscript writing: C.L., A.T.K., J.A.S., N.F., J.C.B., Y. Lu, W.P., L.W.M., M.G.L., K.R., T.L.E., M.F., G.B.E., J.I.R., C.N.-C., D.L., D.I.C.

Corresponding authors

Correspondence toChunyu Liu, Daniel Levy or Daniel I Chasman.

Ethics declarations

Competing interests

B.M.P. serves on the DSMB for a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. The other authors declare no competing financial interests.

Additional information

A list of members and affiliations appears in the Supplementary Note

A list of members and affiliations appears in the Supplementary Note

A list of members and affiliations appears in the Supplementary Note

A list of members and affiliations appears in the Supplementary Note

A list of members and affiliations appears in the Supplementary Note

A list of members and affiliations appears in the Supplementary Note

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Tables 7–20 and Supplementary Note. (PDF 3709 kb)

Supplementary Table 1

CHARGE+ Exome Chip BP Consortium: experiment-wide significant associations in meta-analysis. (XLSX 15 kb)

Supplementary Table 2

CHARGE+ Exome Chip BP Consortium: associations with P < 1 × 10−4 in samples of all ancestries. (XLSX 76 kb)

Supplementary Table 3

CHARGE+ Exome Chip BP Consortium: previously identified GWAS loci with P < 0.001 for any blood pressure trait. (XLSX 23 kb)

Supplementary Table 4

Meta-analysis of the discovery and follow-up samples of European ancestry: associations with P < 3.4 × 10−7. (XLSX 20 kb)

Supplementary Table 5

Meta-analysis of the discovery and follow-up samples of all ancestries: associations with P < 3.4 × 10−7. (XLSX 21 kb)

Supplementary Table 6

CHARGE+ Exome Chip BP Consortium: effects of the coded alleles on the five blood pressure traits in all ancestries. (XLSX 23 kb)

Supplementary Table 21

Exome Chip genotyping, data cleaning, and quality control. (XLSX 13 kb)

Rights and permissions

About this article

Cite this article

Liu, C., Kraja, A., Smith, J. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.Nat Genet 48, 1162–1170 (2016). https://doi.org/10.1038/ng.3660

Download citation

• Received: 24 July 2015
• Accepted: 05 August 2016
• Published: 12 September 2016
• Issue date: October 2016
• DOI: https://doi.org/10.1038/ng.3660