Traversing the biological complexity in the hierarchy between genome and CAD endpoints in the population at large.

An emerging challenge facing those who are concerned about the efficacy of public health programs is to understand how information from the DNA revolution might be used to improve our ability to predict the initiation, progression and severity of a common disease having a complex multifactorial etiology. In the course of research to evaluate the role of information about DNA, combinations of genome types and environmental exposures that predispose to disease will be identified. Such information is expected to be useful in efforts to identify individuals and families at higher risk of disease and to predict their responses to a proposed therapy. This paper begins with a discussion of the features of a realistic biological model for the study of a common multifactorial disease. We present evidence for the complexity in the relationship between genome type variation and variation in risk of coronary artery disease (CAD) and review the preliminary results of our studies to determine whether information about genome type variation can improve our ability to predict the distribution of CAD among individuals in the population at large. Such studies make it apparent that new analytical strategies are necessary to deal with the plethora of genome type information available for the evaluation of risk of a common disease like CAD. This shift in the research paradigm will build upon new strategies to understand the organization of natural systems that are coming from outside the mainstream of genetic research.