We envision the Smokescreen array driving translational research by facilitating the development of algorithms, derived from multiple genetic and clinical factors for risk prediction and treatment approach assignments. Previously, genome-wide allelotyping analyses of smoking cessation trials revealed associations of common variants with prospective abstinence [67]. This research lead to the design of a clinical trial analysis model incorporating a “quit-success” genetic score, which retrospectively predicted abstinence in a randomized trial stratifying smokers by nicotine replacement therapy dose and dependence [53]. This model used both genetic (“quit-success” score) and clinical (FTCD score) information. We re-envision this model based on a Smokescreen analysis platform that incorporates individual level genotype data, additional clinical factors, and the multi-stage process of validation and utility assessment in large sample sizes derived from meta-analysis of multiple trials [68]. For example, genotyping samples with multiple addiction-related phenotypes will permit genome-wide correlation [69–71] and estimation of the extent of shared variance and polygenicity among dependence, attributable disease, and treatment response; the proportion of shared variance among dependencies using genome-wide correlation is substantial [6].
