Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder

Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes

Current Mendelian randomization (MR) methods do not reflect complex relationships among multiple exposures and outcomes as is typical for real-life applications. We introduce MrDAG, a Bayesian causal graphical model for summary-level MR analysis to detect dependency relations within the exposures, the outcomes, and between them to improve causal effects estimation. MrDAG combines three causal inference strategies. It uses genetic variation as instrumental variables to account for unobserved confounders. It performs structure learning to detect and orientate the direction of the dependencies within the exposures and the outcomes. Finally, interventional calculus is employed to derive principled causal effect estimates. In MrDAG the directionality of the causal effects between the exposures and the outcomes is assumed known, i.e., the exposures can only be potential causes of the outcomes, and no reverse causation is allowed. In the simulation study, MrDAG outperforms recently proposed one-outcome-at-a-time and multi-response multi-variable Bayesian MR methods as well as causal graphical models under the constraint on edges' orientation from the exposures to the outcomes. MrDAG was motivated to unravel how lifestyle and behavioral exposures impact mental health. It highlights first, education and second, smoking as effective points of intervention given their important downstream effects on mental health. It also enables the identification of a novel path between smoking and the genetic liability to schizophrenia and cognition, demonstrating the complex pathways toward mental health. These insights would have been impossible to delineate without modeling the paths between multiple exposures and outcomes at once.

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.

Factors associated with attention-deficit/hyperactivity disorder among Tunisian children

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a chronic neurodevelopmental condition that affects millions of children and adolescents worldwide. Knowledge of risk factors associated with ADHD may reduce its prevalence and its severe impact on patient's quality of life. The aim of this study was to identify risk factors associated with ADHD and to discuss their involvement in the genesis of the disorder.

Methods

This is a case-control study involving a first group of 74 children (mean age = 9 years) diagnosed with ADHD. The second group included 80 healthy control children. They were randomly selected and matched for age and gender. A literature-based questionnaire assessing the socio-demographic data, biological and environmental factors associated with ADHD was administered to the parents. The diagnosis of ADHD group was made by a trained child psychiatrist according to the DSM-5 criteria supplemented by the Conners scales of parents and teachers. For the control group, we added to the questionnaire the MINI-kid section of ADHD to screen for possible presence of ADHD symptoms. Univariate then multivariate analyses were conducted to identify factors associated with ADHD.

Results

Several factors were more prevalent in children with ADHD than in controls: disturbed family dynamics, low socio-economic status, family history of psychiatric and organic pathologies, and particularly several early environmental factors, including passive smoking during pregnancy, prematurity, fetal distress, caesarean delivery and low birth weight. In the early childhood period, early exposure to television was also strongly associated with ADHD. However, the multivariate model conducted to determine the variables independently associated with ADD/ADHD revealed only three determining factors: passive smoking during pregnancy (OR = 4.60 [2.14, 9.94]; p < 0.001), acute fetal distress (OR = 5.08 [1.47, 17.52]; p = 0.01), and familial psychiatric history (OR = 9.37 [2.46, 35.59]; p = 0.001).

Discussion

The recognition of factors involved in the genesis of ADHD within different ethnic populations may help understanding and broaden our knowledge of this disorder to develop targeted strategies for prevention and early intervention. Further participants with more robust statistical output are required to confirm our findings to a more generalized population.

Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression

Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.

Precision pharmacotherapy of atomoxetine in children with ADHD: how to ensure the right dose for the right person?

Non-stimulant atomoxetine is recognized in various current clinical guidelines as an important alternative to stimulants for the pharmacological treatment of attention deficit/hyperactivity disorder (ADHD) in children. While its efficacy and tolerability for core symptoms are established, there is considerable inter-individual variability in response and exposure, highlighting the need for personalized dosing. In this review, we evaluated existing studies and summarized comprehensive evidence supporting the clinical implementation of therapeutic drug monitoring (TDM) and personalized dosing of atomoxetine, organized around a series of logically structured questions. Although there are notable gaps in achieving personalized dosing across multiple critical elements, the available evidence is helpful to endorse personalized dose adjustments based on TDM and CYP2D6 genotyping "whenever possible." We advocate for ongoing improvement and enhancement in clinical practice. Future advancements will rely on a deeper understanding of ADHD, facilitating more precise diagnoses and personalized treatment strategies.

Decomposition of phenotypic heterogeneity in autism reveals distinct and coherent genetic programs

Unraveling the phenotypic and genetic complexity of autism is extremely challenging yet critical for understanding the biology, inheritance, trajectory, and clinical manifestations of the many forms of the condition. Here, we leveraged broad phenotypic data from a large cohort with matched genetics to characterize classes of autism and their patterns of core, associated, and co-occurring traits, ultimately demonstrating that phenotypic patterns are associated with distinct genetic and molecular programs. We used a generative mixture modeling approach to identify robust, clinically-relevant classes of autism which we validate and replicate in a large independent cohort. We link the phenotypic findings to distinct patterns of de novo and inherited variation which emerge from the deconvolution of these genetic signals, and demonstrate that class-specific common variant scores strongly align with clinical outcomes. We further provide insights into the distinct biological pathways and processes disrupted by the sets of mutations in each class. Remarkably, we discover class-specific differences in the developmental timing of genes that are dysregulated, and these temporal patterns correspond to clinical milestone and outcome differences between the classes. These analyses embrace the phenotypic complexity of children with autism, unraveling genetic and molecular programs underlying their heterogeneity and suggesting specific biological dysregulation patterns and mechanistic hypotheses.