Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts


Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797–22,562 persons, aged 18–104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 × 10−8), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 × 10−11; LDL, P = 2.6 × 10−10), TMEM57 (TC, P = 5.4 × 10−10), CTCF-PRMT8 region (HDL, P = 8.3 × 10−16), DNAH11 (LDL, P = 6.1 × 10−9), FADS3-FADS2 (TC, P = 1.5 × 10−10; LDL, P = 4.4 × 10−13) and MADD-FOLH1 region (HDL, P = 6 × 10−11). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1
Figure 2
Figure 3


  1. Pilia, G. et al. Heritability of cardiovascular and personality traits in 6,148 Sardinians. PLoS Genet. 2, e132 (2006).

    Article  Google Scholar 

  2. Kannel, W.B.D.T., Kagan, A., Revotskie, N. & Stokes, J.I. Factors of risk in the development of coronary heart disease–six year follow-up experience. The Framingham Study. Ann. Intern. Med. 55, 33–50 (1961).

    Article  CAS  Google Scholar 

  3. Miller, N.E. & Letter, M.G. High-density lipoprotein and atherosclerosis. Lancet 1, 1033 (1975).

    Article  CAS  Google Scholar 

  4. Friedlander, Y.A.A., Newman, B., Edwards, K., Mayer-Davis, E.J. & King, M.-C. Heritability of longitudinal changes in coronary-heart-disease risk factors in women twins. Am. J. Hum. Genet. 60, 1502–1512 (1997).

    Article  CAS  Google Scholar 

  5. Kathiresan, S., Musunuru, K. & Orho-Melander, M. Defining the spectrum of alleles that contribute to blood lipid concentrations in humans. Curr. Opin. Lipidol. 19, 122–127 (2008).

    Article  CAS  Google Scholar 

  6. Kooner, J. et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat. Genet. 40, 149–151 (2008).

    Article  CAS  Google Scholar 

  7. Kathiresan, S. et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 40, 189–197 (2008).

    Article  CAS  Google Scholar 

  8. Willer, C.J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet. 40, 161–169 (2008).

    Article  CAS  Google Scholar 

  9. Wallace, C. et al. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. Am. J. Hum. Genet. 82, 139–149 (2008).

    Article  CAS  Google Scholar 

  10. Sandhu, M., Waterworth, D., Debenham, S., Wheeler, W. & Papadakis, K. LDL-cholesterol concentrations: a genome-wide association study. Lancet 371, 483–491 (2008).

    Article  CAS  Google Scholar 

  11. Heid, I. et al. A genome-wide association analysis of HDL cholesterol in the population-based KORA Study sheds new light on intergenic regions. Circulation. Cardiovas.Genet. 1, 10–20 (2008).

    Article  CAS  Google Scholar 

  12. Sing, C. & Davignon, J. Role of the apolipoprotein E polymorphism in determining normal plasma lipid and lipoprotein variation. Am. J. Hum. Genet. 37, 268–285 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Law, S. et al. The molecular biology of human apoA-I, apoA-II, apoC-II and apoB. Adv. Exp. Med. Biol. 201, 151–162 (1986).

    CAS  PubMed  Google Scholar 

  14. Kathiresan, S. et al. Polymorphisms associated with cholesterol and risk of cardiovascular events. N. Engl. J. Med. 358, 1240–1249 (2008).

    Article  CAS  Google Scholar 

  15. Wang, K., Li, M. & Bucan, M. Pathway-based approaches for analysis of genome-wide association studies. Am. J. Hum. Genet. 81, 1278–1283 (2007).

    Article  CAS  Google Scholar 

  16. Rudkowska, I. & Jones, P. Polymorphisms in ABCG5/G8 transporters linked to hypercholesterolemia and gallstone disease. Nutr. Rev. 66, 343–348 (2008).

    Article  Google Scholar 

  17. Sabatti, C. et al. Genome-wide association analysis of metabolic phenotypes in a birth cohort from a founder population. Nat. Genet. advance online publication, doi:10.1038/ng.271 (7 December 2008).

  18. Weiss, L., Pan, L., Abney, M. & Ober, C. The sex-specific genetic architecture of quantitative traits in humans. Nat. Genet. 38, 218–222 (2006).

    Article  CAS  Google Scholar 

  19. Tunstall-Pedoe, H. et al. MONICA Monograph and Multimedia Sourcebook (World Health Organization, Geneva, 2003).

    Google Scholar 

  20. Endo, A. The discovery and development of HMG-CoA reductase inhibitors. J. Lipid Res. 33, 1569–1582 (1992).

    CAS  Google Scholar 

  21. Kasper, D. Harrison's Principles of Internal Medicine (McGraw-Hill, New York, 2005).

    Google Scholar 

  22. Chasman, D. et al. Pharmacogenetic study of statin therapy and cholesterol reduction. J. Am. Med. Assoc. 291, 2821–2827 (2004).

    Article  CAS  Google Scholar 

  23. Howard, B., Ruotolo, G. & Robbins, D. Obesity and dyslipidemia. Endocrinol. Metab. Clin. North Am. 32, 855–867 (2003).

    Article  CAS  Google Scholar 

  24. Janssens, A. & van Duijn, C. Genome-based prediction of common diseases: advances and prospects. Hum. Mol. Genet. 17, 166–173 (2008).

    Article  Google Scholar 

  25. Anderson, K., Odell, P., Wilson, P. & Kannel, W. Cardiovascular disease risk profiles. Am. Heart J. 121, 293–298 (1991).

    Article  CAS  Google Scholar 

  26. Hippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J. & Brindle, P. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart 94, 34–39 (2008).

    Article  CAS  Google Scholar 

  27. Peltonen, L. GenomEUtwin: a strategy to identify genetic influences on health and disease. Twin Res. 6, 354–360 (2003).

    Article  Google Scholar 

  28. Rantakallio, P. Groups at risk in low birth weight infants and perinatal mortality. Acta Paediatr. Scand. 193, 1 (1969).

    Google Scholar 

  29. Hofman, A. et al. The Rotterdam Study: objectives and design update. Eur. J. Epidemiol. 22, 819–829 (2007).

    Article  Google Scholar 

  30. Friedewald, W.T., Levy, R.I. & Fredrickson, D.S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 18, 499–502 (1972).

    CAS  PubMed  Google Scholar 

  31. Pardo, L.M., MacKay, I., Oostra, B., van Duijn, C.M. & Aulchenko, Y.S. The effect of genetic drift in a young genetically isolated population. Ann. Hum. Genet. 69, 288–295 (2005).

    Article  CAS  Google Scholar 

  32. Pattaro, C. et al. The genetic study of three population microisolates in South Tyrol (MICROS): study design and epidemiological perspectives. BMC Med. Genet. 8, 29 (2007).

    Article  Google Scholar 

  33. Rudan, I., Campbell, H. & Rudan, P. Genetic epidemiological studies of eastern Adriatic Island isolates, Croatia: objective and strategies. Coll. Antropol. 23, 531–546 (1999).

    CAS  PubMed  Google Scholar 

  34. Boomsma, D. et al. Genome-wide association of major depression: description of samples for the GAIN Major Depressive Disorder Study: NTR and NESDA biobank projects. Eur. J. Hum. Genet. 16, 335–342 (2008).

    Article  CAS  Google Scholar 

  35. Wichmann, H., Gieger, C. & Illig, T. KORA-gen–resource for population genetics, controls and a broad spectrum of disease phenotypes. Gesundheitswesen 67, S26–S30 (2005).

    Article  Google Scholar 

  36. Steemers, F. et al. Whole-genome genotyping with the single-base extension assay. Nat. Methods 3, 31–33 (2006).

    Article  CAS  Google Scholar 

  37. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  38. Aulchenko, Y.S., Ripke, S., Isaacs, A. & van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    Article  CAS  Google Scholar 

  39. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  40. Bacanu, S.A., Devlin, B. & Roeder, K. The power of genomic control. Am. J. Hum. Genet. 66, 1933–1944 (2000).

    Article  CAS  Google Scholar 

  41. Scott, L. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007).

    Article  CAS  Google Scholar 

Download references


Above all, we thank study participants for making this work possible. We thank H.-Y. Shen and M. Allen for ENGAGE project management and M. Krestyaninova for IT-system setup.

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme 'Quality of Life and Management of the Living Resources' of 5th Framework Programme (no. QLG2-CT-2002-01254). The Finnish Heart Association (M.P., L.P.) is gratefully acknowledged for its financial support. L.P. and J.K. have been supported by the Academy of Finland Centre of Excellence in Complex Disease Genetics.

NFBC genotyping was supported on NHLBI grant 5R01HL087679-02 through the STAMPEED program.

EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947).

For the MICROS study in South Tyrol, we thank the primary care practitioners R. Stocker, S. Waldner, T. Pizzecco, J. Plangger, U. Marcadent and the personnel of the Hospital of Silandro (Department of Laboratory Medicine) for their participation and collaboration in the research project. In South Tyrol, the study was supported by the Ministry of Health of the Autonomous Province of Bolzano and the South Tyrolean Sparkasse Foundation.

NTR/NESDA was supported by “Genetic basis of anxiety and depression” program (NWO 904-61-090); Twin-family database for behavior genomics studies (NWO 480-04-004); Center for Medical Systems Biology (NWO Genomics); Spinozapremie (SPI 56-464-14192); Centre for Neurogenomics and Cognitive Research (CNCR-VU); Genome-wide analyses of European twin and population cohorts (EU/QLRT-2001-01254); Geestkracht program of ZonMW (10-000-1002); and matching funds from universities and mental health care institutes involved in NESDA (GGZ Buitenamstel-Geestgronden, Rivierduinen, University Medical Center Groningen, GGZ Lentis, GGZ Friesland, GGZ Drenthe). Major funding for this project is from the Genetic Association Information Network of the Foundation for the US National Institutes of Health, a public-private partnership between the NIH and Pfizer, Affymetrix and Abbott Laboratories.

The MONICA/KORA Augsburg studies were financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany and supported by grants from the German Federal Ministry of Education and Research (BMBF). Part of this work was financed by the German National Genome Research Network (NGFN) and by the “Genomics of Lipid-associated Disorders – GOLD” of the “Austrian Genome Research Programme GEN-AU”. The KORA research was supported within the Munich Center of Health Sciences (MC Health) as part of LMUinnovativ. We gratefully acknowledge the contribution of P. Lichtner, G. Eckstein and T. Strom and all other members of the Helmholtz Zentrum München genotyping staff in generating and analyzing the SNP dataset. We thank all members of field staffs who were involved in the planning and conduct of the MONICA/KORA Augsburg studies. Finally, we express our appreciation to all study participants.

Genome-wide genotyping of the Rotterdam Study was supported by NWO (175.010.2005.011). The Vis study in the Croatian island of Vis was supported through the grants from the Medical Research Council UK to H.C., A.W. and I.R.; and Ministry of Science, Education and Sport of the Republic of Croatia to I.R. (number 108-1080315-0302). The ERF study was supported by grants from The Netherlands Organisation for Scientific Research, Erasmus MC and the Centre for Medical Systems Biology (CMSB). We are grateful to all study participants and their relatives, general practitioners and neurologists for their contributions and to P. Veraart for her help in genealogy, J. Vergeer for the supervision of the laboratory work and P. Snijders for his help in data collection. The Northern Swedish Population Health Study was funded by the Swedish Medical Research Council and the European Commission through the EUROSPAN project. We are greatly indebted to the participants in the study. The ORCADES study was supported by the Scottish Executive Health Department, the Royal Society and the Wellcome Trust Clinical Research Facility. We would like to acknowledge the data collection team in Orkney, the clerical team in Edinburgh and the people of Orkney.

Author information

Authors and Affiliations



The study was designed by L.P., C.M.v.D., Y.S.A. and S.R. Genotype and phenotype information were collected by D.B., I.M.H., C.P., B.W.J.H.P., I.R., T.S., N.G.M., N.L.P., K.O.K., J.K., A.H., N.B.F., M.-R.J., U.G., H.C., J.F.W., Å.J., F.M., C.H., V.V., I.J., P.P.P., A.W., N.H., I.P., A.A.H., M.F., G.W., J.-J.H., E.J.C.d.G., G.W.M., J.W., P.M., M.P., K.S., A.I., E.J.G.S., A.G.U., J.C.M.W., B.A.O., P.E., A.R., C.S., C.G., T.M., F.K., A.D., H.-E.W., J.H.S., M.I.M., C.M.v.D. and L.P. Statistical analysis was performed by Y.S.A., S.R., I.L., A.C.J.W.J., J.-J.H. and J.S. The manuscript was written by Y.S.A., S.R., A.C.J.W.J., C.M.v.D. and L.P. All authors reviewed the manuscript.

Corresponding author

Correspondence to Leena Peltonen.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1 and 2 and Supplementary Figures 1–6 (PDF 1020 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

the ENGAGE Consortium. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet 41, 47–55 (2009).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing