multiple pathways to the same disease and that within each pathway there are multiple causes that work in tandem to lead to the disease. These types of causes are often referred to as “risk factors.”
The risk factor framework generally is “egalitarian” in its assumptions about causation; all types of factors that contribute to disease occurrence can be called a cause. There may be some factors that are necessary causes in the sense that the disease never occurs in their absence, but other causes may not be necessary at all. In addition, even necessary causes require the presence of causal partners to lead to disease occurrence. These causal partners also are considered to be causes of the disease.
The necessity of a causal partner for disease occurrence is what we mean by “biologic interaction.” Thus, the very definition of a cause in risk factor epidemiology places the issue of interaction front and center. It is assumed that virtually all diseases arise from the interaction of two or more causes.
Despite the centrality of interaction to this causal framework, methodologic advances have focused mainly on the isolation of single causes and the identification of individual risk factors that contribute to disease occurrence in a population. New designs were developed to allow us to see the relationships between exposures1 and disease in our data that would provide clues to the identification of these causes. Statistical methods were developed to aid in causal inference.
The identification of the causal partners of particular risk factors, the assessment of interaction, was a more complex notion that awaited conceptual clarification and methodological advances. Considerable progress has been made; however, often a lag occurs between the development of new methods and approaches and their application and appearance in the literature. Thus, the way in which interaction is assessed in epidemiologic studies is only now beginning to reflect these newer methods.
What follows is a discussion of this newer way of thinking about how to identify “biologic interaction.” I prefer the use of the term “synergy” in this discussion because it is more neutral to the level of organization at which interaction is being described. Although these methods have developed separately from those in the field of genetics, they are fully applicable to the field, and while genes have characteristics that are distinct from many of the risk factors studied in epidemiology, an epidemiologic approach to causation easily and naturally encompasses genes as causes. However, this application requires a shift in perspective. From a genetic point of view there is a hierarchy of causes, with “the gene” having centrality as the defining cause and all other factors being ancillary to it. Factors that are