The findings on genetic influences on the risk of acute myocardial infarction represent a revolution in our understanding of its mechanisms. Combining this new perspective into risk prediction strategies will help revise the goals of population-based primary prevention and make it more cost-effective. The genetic variants identified so far that can influence the risk of acute myocardial infarction have three characteristics: they are numerous (13 to date, and this number may increase to dozens); each risk variant increases the risk by 10-30% (i.e., the same as a small amount of daily smoking); and the risk variants are very common (many people carry such variants). The potential preventive effect of these findings can be considered at two levels: the population level and the individual level. For the population level, the number of risks associated with each variant and the fact that the variants are prevalent suggest that they can be a very useful part of a prevention strategy. For example, considering 10 risk variants, a group carrying 7 variants has more than twice the risk of a group carrying fewer than 3 variants. One can imagine a scenario in which the risk variants of an individual are detected to increase the means of risk assessment, which can influence the decision to initiate a primary prevention strategy, such as statin or aspirin therapy. At the individual level specifically for a given person, it is not enough to know whether he carries a particular gene (or even a group of genes) for the risk variant. Their total genetic risk needs to be determined by knowing the proportion of risk genes they carry to the total number of genes affecting acute myocardial infarction, because we still do not know all such genes, and the situation becomes relatively more complex. The biggest result of my pharmacogenomic findings is that no polymorphism in a particular genome was found to be associated with lipid levels and modulated drug treatment dose size. It is evident that for the individual level, the existing genetic knowledge is not sufficient to make accurate risk predictions.