Yiyi Ma1, Jack L Follis2, Caren E Smith3, Toshiko Tanaka4, Ani W Manichaikul5, Audrey Y Chu6, Cecilia Samieri7, Xia Zhou8, Weihua Guan9, Lu Wang6, Mary L Biggs10, Yii-Der I Chen11, Dena G Hernandez12, Ingrid Borecki13, Daniel I Chasman6, Stephen S Rich5, Luigi Ferrucci4, Marguerite Ryan Irvin14, Stella Aslibekyan14, Degui Zhi15, Hemant K Tiwari15, Steven A Claas14, Jin Sha14, Edmond K Kabagambe16, Chao-Qiang Lai3, Laurence D Parnell3, Yu-Chi Lee3, Philippe Amouyel17, Jean-Charles Lambert18, Bruce M Psaty19, Irena B King20, Dariush Mozaffarian21, Barbara McKnight10, Stefania Bandinelli22, Michael Y Tsai23, Paul M Ridker6, Jingzhong Ding24, Kurt Lohmant Mstat25, Yongmei Liu25, Nona Sotoodehnia26, Pascale Barberger-Gateau7, Lyn M Steffen8, David S Siscovick27, Devin Absher28, Donna K Arnett14, José M Ordovás29, Rozenn N Lemaitre26. 1. Biomedical Genetics, Department of Medicine, Boston University, Boston, MA; Jean Mayer USDA Human Nutrition Research Center on Aging and yiyima@bu.edu. 2. Department of Mathematics, Computer Science and Cooperative Engineering, University of St. Thomas, Houston, TX; 3. Jean Mayer USDA Human Nutrition Research Center on Aging and. 4. Translational Gerontology Branch and. 5. Center for Public Health Genomics, University of Virginia, Charlottesville, VA; 6. Brigham and Women's Hospital, Harvard Medical School, Boston, MA; 7. Inserm, U897, University of Bordeaux, Bordeaux, France; University of Bordeaux, ISPED, Bordeaux, France; 8. Divisions of Epidemiology and Community Health and. 9. Biostatistics, School of Public Health, and. 10. Departments of Biostatistics, Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA; 11. Cedars-Sinai Medical Center, Medical Genetics Research Institute, Los Angeles, CA; Los Angeles Biomedical Institute, Harbor-University of California, Los Angeles Medical Center, Torrance, CA; 12. Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD; 13. Department of Genetics, Washington University School of Medicine, St. Louis, MO; 14. Departments of Epidemiology and. 15. Biostatics, University of Alabama at Birmingham, Birmingham, AL; 16. Department of Medicine, Vanderbilt University, Nashville, TN; 17. Inserm, UMR1167, Lille, France; University of Lille, Lille, France; Institut Pasteur de Lille, Lille, France; Regional University Hospital of Lille, Lille, France; 18. Inserm, UMR1167, Lille, France; University of Lille, Lille, France; Institut Pasteur de Lille, Lille, France; 19. Epidemiology, Health Services, and Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA; 20. Department of Internal Medicine, University of New Mexico, Albuquerque, NM; 21. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA; 22. Geriatric Unit, Azienda Sanitaria Firenze, Florence, Italy; 23. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN; 24. Departments of Internal Medicine and. 25. Epidemiology & Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC; 26. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA; 27. New York Academy of Medicine, New York, NY; 28. Hudson Alpha Institute for Biotechnology, Huntsville, AL; 29. Jean Mayer USDA Human Nutrition Research Center on Aging and Department of Epidemiology and Population Genetics, Cardiovascular Research Center, Madrid, Spain; and IMDEA Food Institute, Madrid, Spain.
Abstract
BACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression. OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation. DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium. RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (β = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05). CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.
BACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression. OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation. DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium. RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (β = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83%, P = 0.007). Higher ABCA1cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05). CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.
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