A comparison of discrete versus continuous environment in a variance components-based linkage analysis of the COGA data

BACKGROUND: The information content of a continuous variable exceeds that of its categorical counterpart. The parameterization of a model may diminish the benefit of using a continuous variable. We explored the use of continuous versus discrete environment in variance components based analyses examining gene × environment interaction in the electrophysiological phenotypes from the Collaborative Study on the Genetics of Alcoholism. RESULTS: The parameterization using the continuous environment produced a greater number of significant gene × environment interactions and lower AICs (Akaike's information criterion). In these cases, the genetic variance increased with increasing cigarette pack-years, the continuous environment of interest. This did not, however, result in enhanced LOD scores when linkage analyses incorporated the gene × continuous environment interaction. CONCLUSION: Alternative parameterizations may better represent the functional relationship between the continuous environment and the genetic variance.