ADGRL3 (LPHN3) variants predict substance use disorder

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.

Prepotent response inhibition and reaction times in children with attention deficit/hyperactivity disorder from a Caribbean community

Impairment in inhibitory control has been postulated as an underlying hallmark of attention deficit/hyperactivity disorder (ADHD), which can be utilized as a quantitative trait for genetic studies. Here, we evaluate whether inhibitory control, measured by simple automatized prepotent response (PR) inhibition variables, is a robust discriminant function for the diagnosis of ADHD in children and can be used as an endophenotype for future genetic studies. One hundred fifty-two school children (30.9% female, 67.8% with ADHD) were recruited. The ADHD checklist was used as the screening tool, whilst the DSM-IV Mini International Neuropsychiatry Interview, neurologic interview and neurologic examination, and the WISC III FSIQ test were administered as the gold standard procedure to assert ADHD diagnosis. A Go/No-Go task using a naturalistic and automatized visual signal was administered. A linear multifactor model (MANOVA) was fitted to compare groups including ADHD status, age, and gender as multiple independent factors. Linear discriminant analysis and the receiver operating characteristic curve were used to assess the predictive performance of PR inhibition variables for ADHD diagnosis. We found that four variables of prepotent response reaction time- and prepotent response inhibition established statistically significant differences between children with and without ADHD. Furthermore, these variables generated a strong discriminant function with a total classification capability of 73, 84% specificity, 68% sensitivity, and 90% positive predictive value for ADHD diagnosis, which support reaction times as a candidate endophenotype that could potentially be used in future ADHD genetic research.

Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD

Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients' diagnosis, follow-up, and longitudinal assessment in the clinical setting.

ADHD Endophenotypes in Caribbean Families

Objective: The aim of this study is to contrast the genetics of neuropsychological tasks in individuals from nuclear families clustering ADHD in a Caribbean community. Method: We recruited and clinically characterized 408 individuals using an extensive battery of neuropsychological tasks. The genetic variance underpinning these tasks was estimated by heritability. A predictive framework for ADHD diagnosis was derived using these tasks. Results: We found that individuals with ADHD differed from controls in tasks of mental control, visuospatial ability, visuoverbal memory, phonological and verbal fluency, verbal and semantic fluency, cognitive flexibility, and cognitive ability. Among them, tasks of mental control, visuoverbal memory, phonological fluency, semantic verbal fluency, and intelligence had a significant heritability. A predictive model of ADHD diagnosis using these endophenotypes yields remarkable classification rate, sensitivity, specificity, and precision values (above 80%). Conclusion: We have dissected new cognitive endophenotypes in ADHD that can be suitable to assess the neurobiological and genetic basis of ADHD.

ADGRL3, FGF1 and DRD4: Linkage and Association with Working Memory and Perceptual Organization Candidate Endophenotypes in ADHD

Attention deficit hyperactivity disorder (ADHD) is a highly heritable neurobehavioral disorder that affects children worldwide, with detrimental long-term consequences in affected individuals. ADHD-affected patients display visual-motor and visuospatial abilities and skills that depart from those exhibited by non-affected individuals and struggle with perceptual organization, which might partially explain impulsive responses. Endophenotypes (quantifiable or dimensional constructs that are closely related to the root cause of the disease) might provide a more powerful and objective framework for dissecting the underlying neurobiology of ADHD than that of categories offered by the syndromic classification. In here, we explore the potential presence of the linkage and association of single-nucleotide polymorphisms (SNPs), harbored in genes implicated in the etiology of ADHD (ADGRL3, DRD4, and FGF1), with cognitive endophenotypes related to working memory and perceptual organization in 113 nuclear families. These families were ascertained from a geographical area of the Caribbean coast, in the north of Colombia, where the community is characterized by its ethnic diversity and differential gene pool. We found a significant association and linkage of markers ADGRL3-rs1565902, DRD4-rs916457 and FGF1-rs2282794 to neuropsychological tasks outlining working memory and perceptual organization such as performance in the digits forward and backward, arithmetic, similarities, the completion of figures and the assembly of objects. Our results provide strong support to understand ADHD as a combination of working memory and perceptual organization deficits and highlight the importance of the genetic background shaping the neurobiology, clinical complexity, and physiopathology of ADHD. Further, this study supplements new information regarding an ethnically diverse community with a vast African American contribution, where ADHD studies are scarce.

Uncovering the Genetic and Molecular Features of Huntington's Disease in Northern Colombia

Huntington's disease (HD) is a genetic disorder caused by a CAG trinucleotide expansion in the huntingtin (HTT) gene. Juan de Acosta, Atlántico, a city located on the Caribbean coast of Colombia, is home to the world's second-largest HD pedigree. Here, we include 291 descendants of this pedigree with at least one family member with HD. Blood samples were collected, and genomic DNA was extracted. We quantified the HTT CAG expansion using an amplicon sequencing protocol. The genetic heterogeneity was measured as the ratio of the mosaicism allele's read peak and the slippage ratio of the allele's read peak from our sequence data. The statistical and bioinformatic analyses were performed with a significance threshold of p < 0.05. We found that the average HTT CAG repeat length in all participants was 21.91 (SD = 8.92). Of the 291 participants, 33 (11.3%, 18 females) had a positive molecular diagnosis for HD. Most affected individuals were adults, and the most common primary and secondary alleles were 17/7 (CAG/CCG) and 17/10 (CAG/CCG), respectively. The mosaicism increased with age in the participants with HD, while the slippage analyses revealed differences by the HD allele type only for the secondary allele. The slippage tended to increase with the HTT CAG repeat length in the participants with HD, but the increase was not statistically significant. This study analyzed the genetic and molecular features of 291 participants, including 33 with HD. We found that the mosaicism increased with age in the participants with HD, particularly for the secondary allele. The most common haplotype was 17/7_17/10. The slippage for the secondary allele varied by the HD allele type, but there was no significant difference in the slippage by sex. Our findings offer valuable insights into HD and could have implications for future research and clinical management.

Brain Volumetric Analysis Using Artificial Intelligence Software in Premanifest Huntington's Disease Individuals from a Colombian Caribbean Population

Background and objectives: The premanifest phase of Huntington's disease (HD) is characterized by the absence of motor symptoms and exhibits structural changes in imaging that precede clinical manifestation. This study aimed to analyze volumetric changes identified through brain magnetic resonance imaging (MRI) processed using artificial intelligence (AI) software in premanifest HD individuals, focusing on the relationship between CAG triplet expansion and structural biomarkers. Methods: The study included 36 individuals descending from families affected by HD in the Department of Atlántico. Sociodemographic data were collected, followed by peripheral blood sampling to extract genomic DNA for quantifying CAG trinucleotide repeats in the Huntingtin gene. Brain volumes were evaluated using AI software (Entelai/IMEXHS, v4.3.4) based on MRI volumetric images. Correlations between brain volumes and variables such as age, sex, and disease status were determined. All analyses were conducted using SPSS (v. IBM SPSS Statistics 26), with significance set at p < 0.05. Results: The analysis of brain volumes according to CAG repeat expansion shows that individuals with ≥40 repeats evidence significant increases in cerebrospinal fluid (CSF) volume and subcortical structures such as the amygdalae and left caudate nucleus, along with marked reductions in cerebral white matter, the cerebellum, brainstem, and left pallidum. In contrast, those with <40 repeats show minimal or moderate volumetric changes, primarily in white matter and CSF. Conclusions: These findings suggest that CAG expansion selectively impacts key brain regions, potentially influencing the progression of Huntington's disease, and that AI in neuroimaging could identify structural biomarkers long before clinical symptoms appear.