Successful identification of genetic risk factors in genomewide association studies typically has depended on meta-analyses combining data from large numbers of studies involving tens or hundreds of thousands of participants. This poses a challenge for research on Gene × Environment interaction (G × E) effects, where characterization of environmental exposures is quite limited in most studies and often varies idiosyncratically between studies. Yet the importance of environmental exposures in the etiology of many disorders-and especially alcohol, tobacco, and drug use disorders-is undeniable. We discuss the potential for "big-data" approaches (e.g., aggregating data from state databases) to generate consistent measures of neighborhood environment across multiple studies, requiring only information about residential address (or ideally residential history) to make progress in G × E analyses. Big-data approaches may also help address limits to the generalizability of existing research literature, such as those that arise because of the limited numbers of severely alcohol-dependent mothers represented in prospective research studies.