Genetic Addiction Risk and Psychological Profiling Analyses for "Preaddiction" Severity Index




Blum, Kenneth
Han, David
Bowirrat, Abdalla
Downs, Bernard William
Bagchi, Debasis
Thanos, Panayotis K.
Baron, David
Braverman, Eric R.
Dennen, Catherine A.
Gupta, Ashim

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Since 1990, when our laboratory published the association of the DRD2 Taq A1 allele and severe alcoholism in JAMA, there has been an explosion of genetic candidate association studies, including genome-wide association studies (GWAS). To develop an accurate test to help identify those at risk for at least alcohol use disorder (AUD), a subset of reward deficiency syndrome (RDS), Blum's group developed the genetic addiction risk severity (GARS) test, consisting of ten genes and eleven associated risk alleles. In order to statistically validate the selection of these risk alleles measured by GARS, we applied strict analysis to studies that investigated the association of each polymorphism with AUD or AUD-related conditions, including pain and even bariatric surgery, as a predictor of severe vulnerability to unwanted addictive behaviors, published since 1990 until now. This analysis calculated the Hardy–Weinberg Equilibrium of each polymorphism in cases and controls. Pearson's χ2 test or Fisher's exact test was applied to compare the gender, genotype, and allele distribution if available. The statistical analyses found the OR, 95% CI for OR, and the post risk for 8% estimation of the population's alcoholism prevalence revealed a significant detection. Prior to these results, the United States and European patents on a ten gene panel and eleven risk alleles have been issued. In the face of the new construct of the "preaddiction" model, similar to "prediabetes", the genetic addiction risk analysis might provide one solution missing in the treatment and prevention of the neurological disorder known as RDS.



genetic addiction risk analysis, reward deficiency syndrome (RDS), behavioral octopus, neurobiology, epigenetics, dopamine homeostasis, preaddiction


Journal of Personalized Medicine 12 (11): 1772 (2022)


Management Science and Statistics