1. |
- Jami, E. S., et al.
(author)
-
Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms
- 2022
-
In: Journal of the American Academy of Child and Adolescent Psychiatry. - : Elsevier BV. - 0890-8567 .- 1527-5418. ; 61:7, s. 934-945
-
Journal article (peer-reviewed)abstract
- Objective: To investigate the genetic architecture of internalizing symptoms in childhood and adolescence. Method: In 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument. Results: The meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, n(effective) = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (vertical bar r(g)vertical bar > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range vertical bar r(g)vertical bar = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Conclusion: Genetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.
|
|
2. |
- Akingbuwa, W. A., et al.
(author)
-
Genetic Associations between Childhood Psychopathology and Adult Depression and Associated Traits in 42998 Individuals: A Meta-Analysis
- 2020
-
In: JAMA Psychiatry. - : American Medical Association (AMA). - 2168-622X .- 2168-6238. ; 77:7, s. 715-728
-
Journal article (peer-reviewed)abstract
- Importance: Adult mood disorders are often preceded by behavioral and emotional problems in childhood. It is yet unclear what explains the associations between childhood psychopathology and adult traits. Objective: To investigate whether genetic risk for adult mood disorders and associated traits is associated with childhood disorders. Design, Setting, and Participants: This meta-analysis examined data from 7 ongoing longitudinal birth and childhood cohorts from the UK, the Netherlands, Sweden, Norway, and Finland. Starting points of data collection ranged from July 1985 to April 2002. Participants were repeatedly assessed for childhood psychopathology from ages 6 to 17 years. Data analysis occurred from September 2017 to May 2019. Exposures: Individual polygenic scores (PGS) were constructed in children based on genome-wide association studies of adult major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI). Main Outcomes and Measures: Regression meta-analyses were used to test associations between PGS and attention-deficit/hyperactivity disorder (ADHD) symptoms and internalizing and social problems measured repeatedly across childhood and adolescence and whether these associations depended on childhood phenotype, age, and rater. Results: The sample included 42998 participants aged 6 to 17 years. Male participants varied from 43.0% (1040 of 2417 participants) to 53.1% (2434 of 4583 participants) by age and across all cohorts. The PGS of adult major depression, neuroticism, BMI, and insomnia were positively associated with childhood psychopathology (β estimate range, 0.023-0.042 [95% CI, 0.017-0.049]), while associations with PGS of subjective well-being and educational attainment were negative (β, -0.026 to -0.046 [95% CI, -0.020 to -0.057]). There was no moderation of age, type of childhood phenotype, or rater with the associations. The exceptions were stronger associations between educational attainment PGS and ADHD compared with internalizing problems (Δβ, 0.0561 [Δ95% CI, 0.0318-0.0804]; ΔSE, 0.0124) and social problems (Δβ, 0.0528 [Δ95% CI, 0.0282-0.0775]; ΔSE, 0.0126), and between BMI PGS and ADHD and social problems (Δβ, -0.0001 [Δ95% CI, -0.0102 to 0.0100]; ΔSE, 0.0052), compared with internalizing problems (Δβ, -0.0310 [Δ95% CI, -0.0456 to -0.0164]; ΔSE, 0.0074). Furthermore, the association between educational attainment PGS and ADHD increased with age (Δβ, -0.0032 [Δ 95% CI, -0.0048 to -0.0017]; ΔSE, 0.0008). Conclusions and Relevance: Results from this study suggest the existence of a set of genetic factors influencing a range of traits across the life span with stable associations present throughout childhood. Knowledge of underlying mechanisms may affect treatment and long-term outcomes of individuals with psychopathology.. © 2020 Lippincott Williams and Wilkins. All rights reserved.
|
|
3. |
- Akingbuwa, W. A., et al.
(author)
-
Multivariate analyses of molecular genetic associations between childhood psychopathology and adult mood disorders and related traits
- 2022
-
In: American Journal of Medical Genetics Part B-Neuropsychiatric Genetics. - : Wiley. - 1552-4841 .- 1552-485X. ; 192:1-2, s. 3-12
-
Journal article (peer-reviewed)abstract
- Ubiquitous associations have been detected between different types of childhood psychopathology and polygenic risk scores based on adult psychiatric disorders and related adult outcomes, indicating that genetic factors partly explain the association between childhood psychopathology and adult outcomes. However, these analyses in general do not take into account the correlations between the adult trait and disorder polygenic risk scores. This study aimed to further clarify the influence of genetic factors on associations between childhood psychopathology and adult outcomes by accounting for these correlations. Using a multivariate multivariable regression, we analyzed associations of childhood attention-deficit/hyperactivity disorder (ADHD), internalizing, and social problems, with polygenic scores (PGS) of adult disorders and traits including major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI), derived for 20,539 children aged 8.5-10.5 years. After correcting for correlations between the adult phenotypes, major depression PGS were associated with all three childhood traits, that is, ADHD, internalizing, and social problems. In addition, BMI PGS were associated with ADHD symptoms and social problems, while neuroticism PGS were only associated with internalizing problems and educational attainment PGS were only associated with ADHD symptoms. PGS of bipolar disorder, subjective well-being, and insomnia were not associated with any childhood traits. Our findings suggest that associations between childhood psychopathology and adult traits like insomnia and subjective well-being may be primarily driven by genetic factors that influence adult major depression. Additionally, specific childhood phenotypes are genetically associated with educational attainment, BMI and neuroticism.
|
|
4. |
- Tate, A. E., et al.
(author)
-
A genetically informed prediction model for suicidal and aggressive behaviour in teens
- 2022
-
In: Translational Psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 12:1
-
Journal article (peer-reviewed)abstract
- Suicidal and aggressive behaviours cause significant personal and societal burden. As risk factors associated with these behaviours frequently overlap, combined approaches in predicting the behaviours may be useful in identifying those at risk for either. The current study aimed to create a model that predicted if individuals will exhibit suicidal behaviour, aggressive behaviour, both, or neither in late adolescence. A sample of 5,974 twins from the Child and Adolescent Twin Study in Sweden (CATSS) was broken down into a training (80%), tune (10%) and test (10%) set. The Netherlands Twin Register (NTR; N = 2702) was used for external validation. Our longitudinal data featured genetic, environmental, and psychosocial predictors derived from parental and self-report data. A stacked ensemble model was created which contained a gradient boosted machine, random forest, elastic net, and neural network. Model performance was transferable between CATSS and NTR (macro area under the receiver operating characteristic curve (AUC) [95% CI] AUC(CATSS(test set)) = 0.709 (0.671-0.747); AUC(NTR) = 0.685 (0.656-0.715), suggesting model generalisability across Northern Europe. The notable exception is suicidal behaviours in the NTR, which was no better than chance. The 25 highest scoring variable importance scores for the gradient boosted machines and random forest models included self-reported psychiatric symptoms in mid-adolescence, sex, and polygenic scores for psychiatric traits. The model's performance is comparable to current prediction models that use clinical interviews and is not yet suitable for clinical use. Moreover, genetic variables may have a role to play in predictive models of adolescent psychopathology.
|
|
5. |
|
|
6. |
- Tate, A. E., et al.
(author)
-
Association and Familial Coaggregation of Type 1 Diabetes and Eating Disorders: A Register-Based Cohort Study in Denmark and Sweden
- 2021
-
In: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 44:5, s. 1143-1150
-
Journal article (peer-reviewed)abstract
- OBJECTIVE To ascertain the association and coaggregation of eating disorders and childhood-onset type 1 diabetes in families. RESEARCH DESIGN AND METHODS Using population samples from national registers in Sweden (n = 2,517,277) and Demark (n = 1,825,920), we investigated the within-individual association between type 1 diabetes and eating disorders and their familial coaggregation among full siblings, half siblings, full cousins, and half cousins. On the basis of clinical diagnoses, we classified eating disorders into any eating disorder (AED), anorexia nervosa (AN) and atypical AN, and other eating disorder (OED). Associations were determined with hazard ratios (HRs) with 95% CIs from Cox regressions. RESULTS Swedish and Danish individuals with a type 1 diabetes diagnosis had a greater risk of receiving an eating disorder diagnosis (HR [95% CI] Sweden: AED 2.02 [1.80-2.27], AN 1.63 [1.36-1.96], OED 2.34 [2.07-2.63]; Denmark: AED 2.19 [1.84-2.61], AN 1.78 [1.36-2.33], OED 2.65 [2.20-3.21]). We also meta-analyzed the results: AED 2.07 (1.88-2.28), AN 1.68 (1.44-1.95), OED 2.44 (2.17-2.72). There was an increased risk of receiving an eating disorder diagnosis in full siblings in the Swedish cohort (AED 1.25 [1.07-1.46], AN 1.28 [1.04-1.57], OED 1.28 [1.07-1.52]); these results were nonsignificant in the Danish cohort. CONCLUSIONS Patients with type 1 diabetes are at a higher risk of subsequent eating disorders; however, there is conflicting support for the relationship between having a sibling with type 1 diabetes and an eating disorder diagnosis. Diabetes health care teams should be vigilant about disordered eating behaviors in children and adolescents with type 1 diabetes.
|
|
7. |
- Tate, A. E., et al.
(author)
-
Predicting mental health problems in adolescence using machine learning techniques
- 2020
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:4
-
Journal article (peer-reviewed)abstract
- Background Predicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms have been flagged as indicators, there is currently no model created to screen the general population for the risk of developing mental health problems. Additionally, the advent of machine learning techniques represents an exciting way to potentially improve upon the standard prediction modelling technique, logistic regression. Therefore, we aimed to I.) develop a model that can predict mental health problems in mid-adolescence II.) investigate if machine learning techniques (random forest, support vector machines, neural network, and XGBoost) will outperform logistic regression. Methods In 7,638 twins from the Child and Adolescent Twin Study in Sweden we used 474 predictors derived from parental report and register data. The outcome, mental health problems, was determined by the Strengths and Difficulties Questionnaire. Model performance was determined by the area under the receiver operating characteristic curve (AUC). Results Although model performance varied somewhat, the confidence interval overlapped for each model indicating non-significant superiority for the random forest model (AUC = 0.739, 95% CI 0.708-0.769), followed closely by support vector machines (AUC = 0.735, 95% CI 0.707-0.764). Conclusion Ultimately, our top performing model would not be suitable for clinical use, however it lays important groundwork for future models seeking to predict general mental health outcomes. Future studies should make use of parent-rated assessments when possible. Additionally, it may not be necessary for similar studies to forgo logistic regression in favor of other more complex methods.
|
|
8. |
|
|
9. |
|
|
10. |
|
|