Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25

Abstract

Objective: To investigate whether single nucleotide polymorphisms (SNPs) in the ADGRL3, DRD4, and SNAP25 genes are associated with and predict ADHD severity in families from a Caribbean community. Method: ADHD severity was derived using latent class cluster analysis of DSM-IV symptomatology. Family-based association tests were conducted to detect associations between SNPs and ADHD severity latent phenotypes. Machine learning algorithms were used to build predictive models of ADHD severity based on demographic and genetic data. Results: Individuals with ADHD exhibited two seemingly independent latent class severity configurations. SNPs harbored in DRD4, SNAP25, and ADGRL3 showed evidence of linkage and association to symptoms severity and a potential pleiotropic effect on distinct domains of ADHD severity. Predictive models discriminate severe from non-severe ADHD in specific symptom domains. Conclusion: This study supports the role of DRD4, SNAP25, and ADGRL3 genes in outlining ADHD severity, and a new prediction framework with potential clinical use.