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MTA Cooperative Group, Humans, Substance-Related Disorders, Genetic Predisposition to Disease, Receptors, G-Protein-Coupled, Receptors, Peptide, Risk Factors, Case-Control Studies, Longitudinal Studies, Polymorphism, Single Nucleotide, Adult, Female, Male, Young Adult, Pediatric, Clinical Research, Human Genome, Patient Safety, Attention Deficit Disorder (ADD), Mental Health, Brain Disorders, Genetic Testing, Substance Abuse, Drug Abuse (NIDA only), Genetics, 2.1 Biological and endogenous factors, Receptors, G-Protein-Coupled, Peptide, Polymorphism, Single Nucleotide, Psychology, Clinical Sciences, and Public Health and Health Services

Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. Using family-based, case-control, and longitudinal samples from disparate regions of the world (n = 2698), recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks (classification tree analyses) with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large sample of individuals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive-partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD.