Background: Children with ADHD frequently engage in higher rates of externalizing behaviors in adulthood relative to children without. However, externalizing behaviors vary across development. Little is known about how this risk unfolds across development. Phenotypic and polygenic models of childhood ADHD were used to predict individual differences in adult externalizing trajectories. Supportive parenting, school connectedness, and peer closeness were then examined as causal mechanisms.
Methods: Data were from the National Longitudinal Study of Adolescent to Adult Health (N = 7,674). Externalizing behavior was measured using data from age 18 to 32 and modeled using latent class growth analysis. Child ADHD was measured using retrospective self-report (phenotypic model) and genome-wide polygenic risk scores (polygenic model). Multiple mediation models examined the direct and indirect effects of the phenotypic and polygenic models (separately) on externalizing trajectories through the effects of adolescent supportive parenting, school connectedness, and peer closeness.
Results: Phenotypic and polygenic models of ADHD were associated with being in the High Decreasing (3.2% of sample) and Moderate (16.1%) adult externalizing trajectories, but not the severe Low Increasing trajectory (2.6%), relative to the Normal trajectory (78.2%). Associations between both models of ADHD on the High Decreasing and Moderate trajectories were partially mediated through the effects of school connectedness, but not supportive parenting or peer closeness.
Conclusions: Findings shed light on how childhood ADHD affects downstream psychosocial processes that then predict specific externalizing outcomes in adulthood. They also reinforce the importance of fostering a strong school environment for adolescents with (and without) ADHD, as this context plays a critical role in shaping the development of externalizing behaviors in adulthood.
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http://dx.doi.org/10.1111/jcpp.13071 | DOI Listing |
Nat Neurosci
January 2025
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Psychiatric disorders are multifactorial and effective treatments are lacking. Probable contributing factors to the challenges in therapeutic development include the complexity of the human brain and the high polygenicity of psychiatric disorders. Combining well-powered genome-wide and brain-wide genetics and transcriptomics analyses can deepen our understanding of the etiology of psychiatric disorders.
View Article and Find Full Text PDFGac Med Mex
January 2025
Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Departamento de Bioquímica Clínica, Laboratorio de Lípidos y Aterosclerosis, Ciudad Autónoma de Buenos Aires.
Introduction: LDL-cholesterol greater than 190 mg/dL indicates severe hypercholesterolemia (HS) of monogenic and/or polygenic origin. Genetic risk scores (GRS) evaluate potential polygenic causes.
Objective: we applied a GRS of 6-SNP (GRS-6) in HS individuals.
The growing availability of pre-trained polygenic risk score (PRS) models has enabled their integration into real-world applications, reducing the need for extensive data labeling, training, and calibration. However, selecting the most suitable PRS model for a specific target population remains challenging, due to issues such as limited transferability, data het-erogeneity, and the scarcity of observed phenotype in real-world settings. Ensemble learning offers a promising avenue to enhance the predictive accuracy of genetic risk assessments, but most existing methods often rely on observed phenotype data or additional genome-wide association studies (GWAS) from the target population to optimize ensemble weights, limiting their utility in real-time implementation.
View Article and Find Full Text PDFGene networks encapsulate biological knowledge, often linked to polygenic diseases. While model system experiments generate many plausible gene networks, validating their role in human phenotypes requires evidence from human genetics. Rare variants provide the most straightforward path for such validation.
View Article and Find Full Text PDFHum Genomics
January 2025
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Richards Building B304, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
Background: Disease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.
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