One of the crucial steps on the way to individualized medicine to treat cardiovascular disease (CVD) is to better understand the identities, roles, extent and at least the major patterns of interaction between influential genomic and environmental factors. It is clear that such a bold goal can hardly be achieved without a major upgrade of our conceptualization of the phenomena studied, taking advantage of recent developments of novel technological and computational tools. Firstly, the search for the genomic components of the most common multifactorial CVDs is no longer restricted to protein-coding genes; truly genome-wide investigations should replace them in both humans and animal models. Secondly, the 'environment' has also undergone semantic expansion, incorporating such remote constituents as developmental plasticity and epigenetics on one side, and socioeconomic status on the other. To elucidate and analyze the resulting complex picture, appropriate statistical models and approaches need to be designed to tackle issues such as population stratification and admixture, multiple testing, and multidimensionality reduction in models involving multiple genes and environmental factors. Eventually, an integrated platform bringing together all of the above will probably be necessary to secure relevant information specific to a particular combination of conditions and settings (age, geo-ethnicity and exposure), which may perhaps become visible only after a step back, through systems (network) biology.