Skip to main content
SpringerLink
Log in

Review: Genetic epidemiological approaches in the study of risk factors for cardiovascular disease

  • Published:
European Journal of Epidemiology Aims and scope Submit manuscript

Abstract

The importance of genetic factors for the susceptibility to disease has been widely recognized in the last years. Genes have been identified for monogenic diseases and the challenge lying ahead is the identification of genetic components of importance and the environments in which they are expressed for complex diseases, that is, multiple genetic factors act and interact with each other or environmental factors to add to the complexity. This paper gives a brief overview of some genetic epidemiological approaches, concepts and recent methodological developments related to the study of risk factors for cardiovascular disease in twin and family studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet 2003; 33(Suppl): 228-237.

    Google Scholar 

  2. Dekker MCJ, van Duijn CM. Prospects of genetic epidemiology in the 21st century. Eur J Epidemiol 2003; 18: 607-616.

    Google Scholar 

  3. Morton NE, Chung CS. In: Genetic epidemiology. New York: Academic Press, 1978: 3-11.

    Google Scholar 

  4. Martin N, Boomsma D, Machin G. A twin-pronged attack on complex traits. Nat Genet 1997; 17: 387-392.

    Google Scholar 

  5. Neale MC, Cardon LR. Methodology for genetic studies of twins and families. Dordrecht: Kluwer Academic Publications. 1992.

    Google Scholar 

  6. Kendler KS, Neale MC, Kessler RC, Heath AC, Eaves LJ. A test of the equal-environment assumption in twin studies of psychiatric illness. Behav Genet 1993; 23: 21-27.

    Google Scholar 

  7. Scarr S, Carter-Saltzman L. Twin method: Defense of a critical assumption. Behav Genet 1979; 9: 527-542.

    Google Scholar 

  8. Evans DM, Gillespie NA, Martin NG. Biometrical genetics. Biol Psychol 2002; 61: 33-51.

    Google Scholar 

  9. Falconer DS. Introduction to quantitative genetics. 3rd edn. Essex, UK: Longman Scientific and Technical. 1989.

    Google Scholar 

  10. Neale MC, Boker SM, Xie G, Maes HH. Mx: Statistical modeling. In: 5th edn. Richmond, VA: Department of Psychiatry, Virginia Commonwealth University. 1999.

    Google Scholar 

  11. Iliadou A, Lichtenstein P, de Faire U, Pedersen NL. Variation in genetic and environmental influences in serum lipid and apolipoprotein levels across the lifespan in Swedish male and female twins. Am J Med Genet 2001; 102: 48-58.

    Google Scholar 

  12. Boomsma DI, Kempen HJ, Gevers Leuven JA, Havekes L, de Knijff P, Frants RR. Genetic analysis of sex and generation differences in plasma lipid, lipoprotein, and apolipoprotein levels in adolescent twins and their parents. Genet Epidemiol 1996; 13: 49-60.

    Google Scholar 

  13. Heller DA, de Faire U, Pedersen NL, Dahlen G, McClearn GE. Genetic and environmental influences on serum lipid levels in twins. N Engl J Med 1993; 328: 1150-1156.

    Google Scholar 

  14. Snieder H, van Doornen LJ, Boomsma DI. Dissecting the genetic architecture of lipids, lipoproteins, and apolipoproteins: Lessons from twin studies. Arterioscler Thromb Vasc Biol 1999; 19: 2826-2834.

    Google Scholar 

  15. Beekman M, Heijmans BT, Martin NG, Pedersen NL, Whitfield JB, DeFaire U, et al. Heritabilities of apolipoprotein and lipid levels in three countries. Twin Res 2002; 5: 87-97.

    Google Scholar 

  16. Snieder H, van Doornen LJ, Boomsma DI. The age dependency of gene expression for plasma lipids, lipoproteins, and apolipoproteins. Am J Hum Genet 1997; 60: 638-650.

    Google Scholar 

  17. Sham P. Statistics in human genetics. New York: Oxford University Press Inc. 1998.

    Google Scholar 

  18. Wilk JB, Djousse L, Arnett DK, Rich SS, Province MA, Hunt SC, et al. Evidence for major genes influencing pulmonary function in the NHLBI family heart study. Genet Epidemiol 2000; 19: 81-94.

    Google Scholar 

  19. Cheng LS, Carmelli D, Hunt SC, Williams RR. Segregation analysis of cardiovascular reactivity to laboratory stressors. Genet Epidemiol 1997; 14: 35-49.

    Google Scholar 

  20. Coresh J, Beaty TH, Kwiterovich Jr. PO. Inheritance of plasma apolipoprotein B levels in families of patients undergoing coronary arteriography at an early age. Genet Epidemiol 1993; 10: 159-176.

    Google Scholar 

  21. Weissbecker KA. Segregation analysis of diastolic blood pressure in a large pedigree. Genet Epidemiol 1993; 10: 659-664.

    Google Scholar 

  22. Risch N. Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs. Am J Hum Genet 1990; 46: 242-253.

    Google Scholar 

  23. Risch N. Linkage strategies for genetically complex traits. II. The power of affected relative pairs. Am J Hum Genet 1990; 46: 229-241.

    Google Scholar 

  24. Risch N. Linkage strategies for genetically complex traits. I. Multilocus models. Am J Hum Genet 1990; 46: 222-228.

    Google Scholar 

  25. Caulfield M, Munroe P, Pembroke J, Samani N, Dominiczak A, Brown M, et al. Genome-wide mapping of human loci for essential hypertension. Lancet 2003; 361(9375): 2118-2123.

    Google Scholar 

  26. Hanis CL, Boerwinkle E, Chakraborty R, Ellsworth DL, Concannon P, Stirling B, et al. A genome-wide search for human non-insulin-dependent (type 2) diabetes genes reveals a major susceptibility locus on chromosome 2. Nat Genet 1996; 13: 161-166.

    Google Scholar 

  27. Horikawa Y, Oda N, Cox NJ, Li X, Orho-Melander M, Hara M, et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 2000; 26: 163-175.

    Google Scholar 

  28. Haseman JK, Elston RC. The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 1972; 2: 3-19.

    Google Scholar 

  29. Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 1998; 62: 1198-1211.

    Google Scholar 

  30. Shete S, Jacobs KB, Elston RC. Adding further power to the Haseman and Elston method for detecting linkage in larger sibships: Weighting sums and differences. Hum Hered 2003; 55: 79-85.

    Google Scholar 

  31. Kaabi B, Elston RC. New multivariate test for linkage, with application to pleiotropy: Fuzzy Haseman-Elston. Genet Epidemiol 2003; 24: 253-264.

    Google Scholar 

  32. Sham PC, Purcell S, Cherny SS, Abecasis GR. Powerful regression-based quantitative-trait linkage analysis of general pedigrees. Am J Hum Genet 2002; 71: 238-253.

    Google Scholar 

  33. Risch NJ. Searching for genetic determinants in the new millennium. Nature 2000; 405(6788): 847-856.

    Google Scholar 

  34. Chagnon YC, Rankinen T, Snyder EE, Weisnagel SJ, Perusse L, Bouchard C. The human obesity gene map: The 2002 update. Obes Res 2003; 11: 313-367.

    Google Scholar 

  35. Eaves L, Meyer J. Locating human quantitative trait loci: Guidelines for the selection of sibling pairs for genotyping. Behav Genet 1994; 24: 443-455.

    Google Scholar 

  36. Risch N, Zhang H. Extreme discordant sib pairs for mapping quantitative trait loci in humans. Science 1995; 268(5217): 1584-1589.

    Google Scholar 

  37. Risch NJ, Zhang H. Mapping quantitative trait loci with extreme discordant sib pairs: sampling considerations. Am J Hum Genet 1996; 58: 836-843.

    Google Scholar 

  38. Zhang H, Risch N. Mapping quantitative-trait loci in humans by use of extreme concordant sib pairs: Selected sampling by parental phenotypes [published erratum appears in Am J Hum Genet 1997 60: 748-749]. Am J Hum Genet 1996; 59: 951-957.

    Google Scholar 

  39. Dolan CV, Boomsma DI. Optimal selection of sib pairs from random samples for linkage analysis of a QTL using the EDAC test. Behav Genet 1998; 28: 197-206.

    Google Scholar 

  40. Gu C, Todorov AA, Rao DC. Genome screening using extremely discordant and extremely concordant sib pairs. Genet Epidemiol 1997; 14: 791-796.

    Google Scholar 

  41. Gu C, Todorov A, Rao DC. Combining extremely concordant sibpairs with extremely discordant sibpairs provides a cost effective way to linkage analysis of quantitative trait loci. Genet Epidemiol 1996; 13: 513-533.

    Google Scholar 

  42. Gu C, Rao DC. A linkage strategy for detection of human quantitative-trait loci. II. Optimization of study designs based on extreme sib pairs and generalized relative risk ratios. Am J Hum Genet 1997; 61: 211-222.

    Google Scholar 

  43. Xu X, Rogus JJ, Terwedow HA, Yang J, Wang Z, Chen C, et al. An extreme-sib-pair genome scan for genes regulating blood pressure. Am J Hum Genet 1999; 64: 1694-1701.

    Google Scholar 

  44. Iliadou A, Lichtenstein P, Ahlberg S, Hoffstedt J, Arner P, Schalling M, et al. No linkage to obesity in candidate regions of chromosome 2 and 10 in a selected sample of Swedish twins. Twin Res 2003; 6: 162-169.

    Google Scholar 

  45. Lichtenstein P, De Faire U, Floderus B, Svartengren M, Svedberg P, Pedersen NL. The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies. J Intern Med 2002; 252: 184-205.

    Google Scholar 

  46. Spielman RS, McGinnis RE, Ewens WJ. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 1993; 52: 506-516.

    Google Scholar 

  47. Abecasis GR, Cardon LR, Cookson WO. A general test of association for quantitative traits in nuclear families. Am J Hum Genet 2000; 66: 279-292.

    Google Scholar 

  48. van den Oord EJ, Snieder H. Including measured genotypes in statistical models to study the interplay of multiple factors affecting complex traits. Behav Genet 2002; 32: 1-22.

    Google Scholar 

  49. Cambien F, Poirier O, Lecerf L, Evans A, Cambou JP, Arveiler D, et al. Deletion polymorphism in the gene for angiotensin-converting enzyme is a potent risk factor for myocardial infarction. Nature 1992; 359(6396): 641-644.

    Google Scholar 

  50. Keavney B, McKenzie C, Parish S, Palmer A, Clark S, Youngman L, et al. Large-scale test of hypothesised associations between the angiotensin-converting-enzyme insertion/deletion polymorphism and myocardial infarction in about 5000 cases and 6000 controls. International Studies of Infarct Survival (ISIS) Collaborators. Lancet 2000; 355(9202): 434-442.

    Google Scholar 

  51. Zhu X, Chang YP, Yan D, Weder A, Cooper R, Luke A, et al. Associations between hypertension and genes in the renin-angiotensin system. Hypertension 2003; 41: 1027-1034.

    Google Scholar 

  52. Fallin D, Schork NJ. Accuracy of haplotype frequency estimation for biallelic loci, via the expectation-maximization algorithm for unphased diploid genotype data. Am J Hum Genet 2000; 67: 947-959.

    Google Scholar 

  53. Keavney B, McKenzie CA, Connell JM, Julier C, Ratcliffe PJ, Sobel E, et al. Measured haplotype analysis of the angiotensin-I converting enzyme gene. Hum Mol Genet 1998; 7: 1745-1751.

    Google Scholar 

  54. McKenzie CA, Abecasis GR, Keavney B, Forrester T, Ratcliffe PJ, Julier C, et al. Trans-ethnic fine mapping of a quantitative trait locus for circulating angiotensin I-converting enzyme (ACE). Hum Mol Genet 2001; 10: 1077-1084.

    Google Scholar 

  55. Zhu X, Bouzekri N, Southam L, Cooper RS, Adeyemo A, McKenzie CA, et al. Linkage and association analysis of angiotensin i-converting enzyme (ace)-gene polymorphisms with ace concentration and blood pressure. Am J Hum Genet 2001; 68: 1139-1148.

    Google Scholar 

  56. Fulker DW, Cherny SS, Sham PC, Hewitt JK. Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet 1999; 64: 259-267.

    Google Scholar 

  57. Neale MC, Cherny SS, Sham PC, et al. Distinguishing population stratification from genuine allelic effects with Mx: Association of ADH2 with alcohol consumption. Behav Genet 1999; 29: 233-243.

    Google Scholar 

  58. Sham PC, Cherny SS, Purcell S, Hewitt JK. Power of linkage vs. association analysis of quantitative traits, by use of variance-components models, for sibship data. Am J Hum Genet 2000; 66: 1616-1630.

    Google Scholar 

  59. Zhang W, Collins A, Abecasis GR, Cardon LR, Morton NE. Mapping quantitative effects of oligogenes by allelic association. Ann Hum Genet 2002; 66(Pt 3): 211-221.

    Google Scholar 

  60. Davey Smith G, Ebrahim S. 'Mendelian randomization': Can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003; 32: 1-22.

    Google Scholar 

  61. Keavney B. Genetic epidemiological studies of coronary heart disease. Int J Epidemiol 2002; 31: 730-736.

    Google Scholar 

  62. Boomsma D, Busjahn A, Peltonen L. Classical twin studies and beyond. Nat Rev Genet 2002; 3: 872-882.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden

    Anastasia Iliadou

  2. Georgia Prevention Institute, Department of Pediatrics, Medical College of Georgia, Augusta, GA, USA

    Harold Snieder

  3. Twin Research and Genetic Epidemiology Unit, St. Thomas' Hospital, London, UK

    Harold Snieder

Authors
  1. Anastasia Iliadou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harold Snieder
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Iliadou, A., Snieder, H. Review: Genetic epidemiological approaches in the study of risk factors for cardiovascular disease. Eur J Epidemiol 19, 209–217 (2004). https://doi.org/10.1023/B:EJEP.0000020399.19615.6c

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:EJEP.0000020399.19615.6c

Search

Navigation