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Título : ADGRL3 (LPHN3) Variants Predict Substance Use Disorder
Autor : Arcos Burgos, Oscar Mauricio
Vélez Valbuena, Jorge Iván
Martínez, F.
Ribasés, Marta
Ramos Quiroga, Josep A.
Sánchez Mora, Cristina
Richarte, Vanesa
Roncero, Carlos
Cormand, Bru
Fernández Castillo, Noelia
Casas, Miguel
Lopera Restrepo, Francisco Javier
Pineda Salazar, David Antonio
Palacio Ortiz, Juan David
Acosta López, Johan
Cervantes Henríquez, Martha Lucía
Sánchez Rojas, Manuel
Puentes Rozo, Pedro
Molina, Brooke
Boden, Margaret T.
Wallis, Deeann
Lidbury, Brett
Newman, Saul
Easteal, Simon
Swanson, James
Patel, Hardip
Volkow, Nora
Acosta, María T.
Castellanos, Francisco X.
De León, Jose
Mastronardi, Claudio Alberto
Muenke, Maximilian
metadata.dc.subject.*: Public Health
Salud Pública
Health Policy
Política de Salud
Psychiatry
Psiquiatría
Neurosciences
Neurociencias
Translational Medical Research
Investigación en Medicina Traslacional
Fecha de publicación : 2019
Editorial : Nature Pub. Grupo
Citación : Arcos Burgos M., Vélez JI., Martinez AF. et al. ADGRL3 (LPHN3) variants predict substance use disorder. Transl Psychiatry 9, 42 (2019). https://doi.org/10.1038/s41398-019-0396-7
Resumen : ABSTRACT: Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attentiondeficit/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 comorbid 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.
metadata.dc.identifier.eissn: 2158-3188
metadata.dc.identifier.doi: 10.1038/s41398-019-0396-7
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