Dissatisfaction has been voiced over recent decades concerning the lack of a relevant cardiovascular disease (CVD) scoring system for European populations. Recently this deficiency has been repaired with the publication of SCORE,1 although nonfatal events are still not catered for. In addition, the entire sequence of the human genome has recently been published.2 Common chronic diseases, such as coronary heart disease (CHD) and stroke, may have a strong genetic component. They are, however, caused not by a single genetic defect but by the interactions of many genetic and environmental factors. Hence, they are often called complex, multifactorial diseases. Moreover, the biological effects of common genetic variants are likely to be small in magnitude; indeed, variants with large biological effects tend to be rare, for example familial hypercholesterolaemia. Investigators examining the genetic background of complex, multifactorial diseases should, therefore, realize that they are looking for interactions between genetic variants with small, or at most moderate, effects. It is obvious that the reliable detection of these effects requires large sample sizes and abundant statistical power, which can be achieved only in a large collaborative study using highthroughput genotyping. It should be emphasized, however, that moderate and even small effects can carry considerable public health significance if the genetic variants in question are common in the population. The remarkable success of the Human Genome Project has been possible only through the multinational collaboration of several research laboratories and the open exchange of information through the Internet. Developments in genetics open up new possibilities for the prevention and treatment of chronic diseases, but to capitalize on this potential, a better understanding of the significance of genetic variation and the interactions of genetic variants with environmental factors is needed. Towards the end of the WHO MONICA Project3 it was realized that a follow-up of the cohorts recruited by the project would be ideal for exploring both issues mentioned above. This follow-up project was established under the name MORGAM (MONICA, Risk, Genetics, Archiving, and Monograph; www.ktl.fi/morgam) and now also includes cohorts from nonMONICA centres. It was initially funded under the Fourth Framework Programme of the European Union. Two of its components, archiving and monograph, have been completed. This profile describes the remaining two, risk and genetics, both of which are based on the pooling of prospective CVD cohorts. For a subset of these cohorts DNA is available and central collation, preparation and genotyping in a case–cohort setting are well under way. Since 2002, these activities have become a component of GenomEUtwin (www.genomeutwin.org), a Network of Excellence for Genomics in Europe, funded under the Fifth Framework Programme. Centres have recruited their cohorts and organized the follow-up locally using their own funding. MORGAM is pooling these cohorts, and the funding is devoted to co-ordination, pooling of samples and data, quality assessment and control, central preparation of DNA, and laboratory analysis. Support for the participating centres is through access to their own results, the return of surplus prepared DNA, a modest subvention to support data preparation and sample handling, and attendance at an annual workshop. MORGAM has a Coordinator (A.E.) who also chairs the MORGAM Management Group, on which the participating laboratories, the MORGAM Data Centre at the Finnish National Public Health Institute (KTL) in Helsinki, and the GenomEUtwin Coordinator are represented.
[1]
D. Tregouet,et al.
Automated detection of informative combined effects in genetic association studies of complex traits.
,
2003,
Genome research.
[2]
L. Cardon,et al.
Association study designs for complex diseases
,
2001,
Nature Reviews Genetics.
[3]
R. Recker,et al.
Population admixture: detection by Hardy-Weinberg test and its quantitative effects on linkage-disequilibrium methods for localizing genes underlying complex traits.
,
2001,
Genetics.
[4]
Leena Peltonen,et al.
GenomEUtwin: A Strategy to Identify Genetic Influences on Health and Disease
,
2003,
Twin Research.
[5]
L. Excoffier,et al.
Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population.
,
1995,
Molecular biology and evolution.
[6]
W E Barlow,et al.
Analysis of case-cohort designs.
,
1999,
Journal of clinical epidemiology.
[7]
P. Donnelly,et al.
Association mapping in structured populations.
,
2000,
American journal of human genetics.
[8]
Jean-Louis Golmard,et al.
Specific haplotypes of the P-selectin gene are associated with myocardial infarction.
,
2002,
Human molecular genetics.
[9]
H. Tunstall-Pedoe,et al.
MONICA Monograph and Multimedia Sourcebook
,
2003
.
[10]
L. Berkman,et al.
Genetic susceptibility to death from coronary heart disease in a study of twins.
,
1994,
The New England journal of medicine.
[11]
The International HapMap Consortium,et al.
A physical map of the human genome
,
2001
.
[12]
Nicholas G Martin,et al.
The Genetics of Coronary Heart Disease: The Contribution of Twin Studies
,
2003,
Twin Research.
[13]
K. Roeder,et al.
Genomic Control for Association Studies
,
1999,
Biometrics.
[14]
H. Tunstall-Pedoe,et al.
Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project.
,
2003,
European heart journal.