Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues

Author:

Castaneda Andy B1,Petty Lauren E2,Scholz Markus34,Jansen Rick5,Weiss Stefan67,Zhang Xiaoling8910,Schramm Katharina1112,Beutner Frank13,Kirsten Holger34,Schminke Ulf14,Hwang Shih-Jen1015,Marzi Carola1617,Dhana Klodian1819,Seldenrijk Adrie5,Krohn Knut20,Homuth Georg6,Wolf Petra1112,Peters Marjolein J21,Dörr Marcus722,Peters Annette16,van Meurs Joyce B J21,Uitterlinden André G1821,Kavousi Maryam18,Levy Daniel1015,Herder Christian232425,van Grootheest Gerard5,Waldenberger Melanie16,Meisinger Christa1626,Rathmann Wolfgang27,Thiery Joachim428,Polak Joseph29,Koenig Wolfgang303132,Seissler Jochen33,Bis Joshua C34,Franceshini Nora35,Giambartolomei Claudia36,Hofman Albert1837,Franco Oscar H1838,Penninx Brenda W J H5,Prokisch Holger1112,Völzke Henry739,Loeffler Markus34,O'Donnell Christopher J1040,Below Jennifer E2,Dehghan Abbas18414243,de Vries Paul S118,

Affiliation:

1. Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

2. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

3. Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany

4. LIFE Research Center of Civilization Diseases, University of Leipzig, Leipzig, Germany

5. Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands

6. Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany

7. DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany

8. Department of Medicine, Boston University School of Medicine, Boston, MA, USA

9. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

10. The Framingham Heart Study, Framingham, MA, USA

11. Institute of Neurogenomics, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany

12. Institute of Human Genetics, Technical University Munich, Munich, Germany

13. Heart Center Leipzig, Leipzig, Germany

14. Department of Neurology, University Medicine Greifswald, Greifswald, Germany

15. Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA

16. Institute of Epidemiology, Helmholz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany

17. German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany

18. Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands

19. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

20. Interdisciplinary Center of Clinical Research, University of Leipzig, Leipzig, Germany

21. Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands

22. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany

23. Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany

24. German Center for Diabetes Research (DZD e.V.), München-Neuherberg, Germany

25. Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

26. Chair of Epidemiology, Ludwig-Maximilians-Universität München, UNIKA-T Augsburg, Augsburg, Germany

27. Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany

28. Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany

29. Tufts University School of Medicine, Boston, MA, USA

30. Deutsches Herzzentrum München, Technische Universität München, Munich, Germany

31. DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany

32. Department of Internal Medicine II-Cardiology, University of Ulm Medical Center, Ulm, Germany

33. Diabetes Center, Diabetes Research Group, Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-Universität, Munich, Germany

34. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA

35. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA

36. Istituto Italiano di Tecnologia (IIT), Central RNA Lab, Genova, Italy

37. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

38. Institute of Social and Preventive Medicine, University of Bern, Switzerland

39. Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany

40. Cardiology Section, Department of Medicine, Boston Veteran’s Administration Healthcare and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

41. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

42. MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, UK

43. UK Dementia Research Institute at Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN UK

Abstract

Abstract Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.

Funder

American Heart Association

National Heart, Lung, and Blood Institute

National Institute on Aging

Publisher

Oxford University Press (OUP)

Subject

Genetics (clinical),Genetics,Molecular Biology,General Medicine

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