The completion of the human genome project in combination with the extensive sequencing performed for the International Haplotype Map (HapMap) project has produced a huge amount of information on variations in the human genome. Together with the rapid technological developments in molecular biology it is now possible to screen large populations for large numbers of genetic variants. Inevitably, these developments will yield new infor mation about the role of the many genes involved in common chronic disorders, and will increase our understanding of their pathogeneses. Translating genomic information into medical care and public health is one of the next challenges in genomics research [1]. It has been recognized for long that testing single genetic variants has limited value for the prediction of common chronic diseases such as type 2 diabetes and cardiovascular disease [2, 3], as these are typically caused by a complex interplay of multiple genetic and non-genetic factors, in which each variant is only associated with a minor increase in the risk of disease [4]. While testing at multiple loci, genomic profiling, may be valuable for disease prediction, it is still far from clear how genomic profiles and at-risk profiles should be defined and evaluated. A genomic profile basically concerns the genotype status on multiple genetic markers. The identification of at-risk profiles is not straightforward, because the number of unique profiles increases exponentially with the number of genes tested. When genes each have two genotypes, testing 5 genes yields 32 unique profiles and testing 50 genes yields 1,125,899,906,842,624 (_1015) profiles. In this issue of the European Journal of Epidemi ology, Yiannakouris and colleagues report about the joint effect of 11 genetic variants in 9 genes on the risk of coronary infarcts [5]. They investigated the simultaneous effect through the development of the genetic predisposition score, which sums for each subject the number of risk alleles present in these 1 1 polymorphisms. They assign for each polymorphism a score of 0 to non-carriers, 0.5 to heterozygotes and 1 to homozygous carriers of the risk alleles. The ge netic predisposition score has a possible range from 0 to 11. Yiannakouris et al. showed that the odds ratio of developing a coronary event was 1.55 in those with a score ?3.0 and 2.02 in those with a score ?3.5. While the study of Yiannakouris et al. is an important first step towards the genetic prediction of coronary infarcts, there are certainly improvements possible in their risk score as Yiannakouris et al. also point out themselves. First and foremost, their score does not take into account the strength of association of individual genes nor the different modes of inheritance. If one gene is associated with a 2-fold risk increase and others increase the risk of coronary infarcts only by 1.2-fold, they all obtain a similar weight in their approach. Also, when the risk increase is not only seen in homozygous carriers, but also in heterozygotes, the latter get only half of the weight. The present approach therefore seems to underutilize the predictive value of the individual polymorphisms and hence it is likely that their genetic predisposition score would be even more predictive if these issues were taken proper care of. Considering the exact predictive value of the individual risk vari ants is of utmost importance when working towards applications in preventive and individualized medi cine, where a more accurate estimation of the risks is essential. An alternative approach would be to model the effects of genes using regression analysis, as was recently used in a study of age-related macular degeneration [6]. This method takes better account of the differences in risk associated with genetic vari ants, but may require large series of patients when the number of genetic variants becomes large. Another issue is that the authors did not consider the presence of gene-gene interactions. Although one may argue that such interactions may average out when a large number of genes are involved, it is important to realize that such interactions may have an impact on the risk of disease when the number of variants is still limited. The expansion of scale in genomics research, such as genome wide association studies and biobank initiatives, will lead to a rapid but still step by step progression in the identification of susceptibility genes. This implies that genomic profiles will be defined and redefined in an iterative way when new susceptibility genes are identified or when the