Research on the genetics and neurobiology of alcoholism uses a variety of study designs and model organisms. A wealth of data are available, including linkage studies in human alcoholics, microarray studies of inbred mouse strains' brains and rat brains exposed to ethanol, and studies of loss or gain of function of genes in organisms such as C. elegans and Drosophila [1,2]. Although results or information across experiments are often compared by individual researchers in order to generate hypotheses, interpret results, or prioritize targets for follow up investigations [3], these analyses are not always done comprehensively and rarely include a cross-species approach [4-7]. While data integration itself can be challenging, how best to utilize combined results is also unclear. Although pooled results may yield valuable insights, there are potential benefits of using more systematic approaches to generate quantitative rankings that can then, in turn, guide additional studies. In particular, these rankings could be applied to choosing molecular targets for knockdown studies in model organisms or genetic association studies in humans. For this and other approaches, evaluation is often needed in order to determine whether the rankings are effective at the end of the data integration process.
