ArticleTranslational psychiatry2017
Predicting attention-deficit/hyperactivity disorder severity from psychosocial stress and stress-response genes: a random forest regression approach.
Article in Translational psychiatry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 48 citations in OpenAlex.
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- A novel approach to risk exposure and epigenetics-the use of multidimensional context to gain insights into the early origins of cardiometabolic and neurocognitive health.BMC medicine · 2023Article
- Assessment of the Genetic Characteristics of a Generation Born during a Long-Term Socioeconomic Crisis.Genes · 2023Article
- Polymorphism of Estrogen Receptor Genes and Its Interactions With Neurodevelopmental Genes in Attention Deficit Hyperactivity Disorder Among Chinese Han Descent.Psychiatry investigation · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS).Global health research and policy · 2022Article
- Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives.Translational psychiatry · 2022Review
- The role of glucocorticoid receptor gene in the association between attention deficit-hyperactivity disorder and smaller brain structures.Journal of neural transmission (Vienna, Austria : 1996) · 2021Article
- Mapping relationships between ADHD genetic liability, stressful life events, and ADHD symptoms in healthy adults.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2021Article
- Effects of maternal immune activation in porcine transcript isoforms of neuropeptide and receptor genes.Journal of integrative neuroscience · 2021Article
- Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.Scientific reports · 2021Article
- Use of machine learning to classify adult ADHD and other conditions based on the Conners' Adult ADHD Rating Scales.Scientific reports · 2020Article
- Genetic Intersections of Language and Neuropsychiatric Conditions.Current psychiatry reports · 2020Review
- Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis.Psychological test and assessment modeling · 2019Article
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10 authors at 4 institutions in 3 countries.
Funding
Abstract
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression is well suited to explore this complexity, as it allows for the analysis of many predictors simultaneously, taking into account any higher-order interactions among them. Using random forest regression, we predicted ADHD severity, measured by Conners' Parent Rating Scales, from 686 adolescents and young adults (of which 281 were diagnosed with ADHD). The analysis included 17 374 single-nucleotide polymorphisms (SNPs) across 29 genes previously linked to hypothalamic-pituitary-adrenal (HPA) axis activity, together with information on exposure to 24 individual long-term difficulties or stressful life events. The model explained 12.5% of variance in ADHD severity. The most important SNP, which also showed the strongest interaction with stress exposure, was located in a region regulating the expression of telomerase reverse transcriptase (TERT). Other high-ranking SNPs were found in or near NPSR1, ESR1, GABRA6, PER3, NR3C2 and DRD4. Chronic stressors were more influential than single, severe, life events. Top hits were partly shared with conduct problems. We conclude that random forest regression may be used to investigate how multiple genetic and environmental factors jointly contribute to ADHD. It is able to implicate novel SNPs of interest, interacting with stress exposure, and may explain inconsistent findings in ADHD genetics. This exploratory approach may be best combined with more hypothesis-driven research; top predictors and their interactions with one another should be replicated in independent samples.
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