Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental behavioral disorder, affecting about 10% of children and adolescents worldwide. It frequently persists into adulthood and can have serious life-long health consequences. Affected individuals are at increased risk for poor educational achievement, low income, underemployment, legal difficulties, and impaired social relationships. The annual societal burden of ADHD was conservatively estimated to reach $42.5 billion in the U.S. In addition, ADHD increases the risk of substance use disorder (SUD) and disruptive (externalizing) disorders such as oppositional defiant disorder (ODD), conduct disorder (CD). SUD is characterized by compulsive drug seeking behavior and drug use in the face of severe adverse consequences. The World Health Organization estimates that there are worldwide at least two billion alcohol users, one billion tobacco users and almost 185 million illicit drug users. Genetic factors are strongly implicated in ADHD. During the last few years, our research on the genetics of ADHD made seminal advances to understand: 1) the innate susceptibility to ADHD and associated comorbidities, 2) the interaction of genetic, demographic and environmental factors underpinning the risk of developing ADHD, 3) how much these factors shape the response of ADHD patients to pharmacological interventions (pharmacogenetics of ADHD), 4) the overrepresentation of functional and ontological gene-based networks implicated in determining synapse structure, and 5) the use of advanced genetic-epidemiological models with potential for use in clinical practice (translational genomics). Thus far, we have identified variants of the latrophilin 3 gene (LPHN3) predisposing to ADHD86 and showed that LPHN3 variants interact with a haplotype on chromosome 11q, doubling ADHD susceptibility. This haplotype encompasses the NCAM1, TTC12, ANKK1, and DRD2 genes. Characterizing this interaction better predicts ADHD severity, long-term outcome and response to treatment and informs how the over-representation of these genes in particular ontogenetic pathways might be involved in processes related to synapse formation. Further, using classification tree-based recursive partitioning models in four independent cohorts, we not only built but also demonstrated that an oligogenic model influenced by demographic and environmental factors could predict the risk of developing ADHD and disruptive behaviors. These findings represent one of the most robustly replicated genetic study on ADHD ever. Our success has depended on several aspects of our research design: 1) Careful, thorough clinical characterization and use of a highly consistent phenotype in all the studies. This has been fundamental for evaluating genetic components in complex and heterogeneous conditions; 2) The use of a combination of approaches including linkage and segregation studies of large, multigenerational and nuclear families from several cohorts with thousands of individuals (total n=6360, 2627 with ADHD); 3) The development and application of complex and/or novel statistical genetic techniques that address the limitations of subjective evaluations and the lack of biological markers. For example, given the complexity of the genetics of behavior, we chose a multivariate classification method, latent class analysis (LCA) to better serve our genetic studies. This analysis allowed us to include quantitative information of co-morbidities, identify milder phenotypes, and account for correlations between co-morbid and ADHD symptoms; 4) The systematic review of genetic regions of strong genetic signal in our samples. From our previous linkage studies we identified multiple regions of strong genetic signal, namely 4q13.2, 5q33.3, 8p23.1, 11q22, and 17p11). We expect that the discovery of these new molecular substrates will lead to the use of genetic variation as a
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