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- Grimes, Poppy, et al.
(författare)
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UNVEILING GENETIC ARCHITECTURES FOR STRATIFYING TRAJECTORIES OF ADOLESCENT DEPRESSION
- 2023
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Ingår i: European Neuropsychopharmacology. - : Elsevier. - 0924-977X .- 1873-7862. ; 75:Suppl. 1, s. S127-S127
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Adolescent-onset depression is characterised by high levels of inter-individual variability and genetic heritability. Investigating the genetic factors that underlie trajectories of depression is crucial to enhancing mechanistic understanding of the onset, persistence and severity of adolescent depression. However, predicting trajectories from complex and heterogeneous genetic architectures in psychopathology poses challenges due to the high genetic correlation among traits. It remains unclear whether multi-trait models provide a superior prediction of depression trajectories compared to univariate models focused solely on depression. Addressing this question is important for effective stratification and targeted treatments.Methods: To validate depression trajectories during adolescence, we conducted growth mixture modelling in two longitudinal cohorts (ABCD and ALSPAC; total N=20,509). We then computed polygenic risk scores for seven traits: major depressive disorder (MDD), anxiety, neuroticism, schizophrenia, bipolar disorder, attention-deficit-hyperactivity disorder (ADHD), and autism for participants with European ancestry. We also generated MDD scores for individuals of African, East Asian, and Hispanic ancestries in ABCD. Using genomic structural equation modelling, we compared three multi-trait factor models (common, correlated, hierarchical) to assess the relationships among the seven traits. To generate multi-trait risk scores from these models in our target cohorts, we conducted multivariate genome-wide association analysis to determine the effects of single nucleotide polymorphisms on genetic latent p-factors. Finally, we examined the association between all polygenic risk scores for univariate traits and multi-trait models with depression trajectories.Results: Four distinct trajectories were replicated across two cohorts with partial age-range overlap encompassing adolescents from two generations. Trajectories included stable low, adolescent acute, increasing and decreasing. The hierarchical factor model was the best fit for multi-trait genetic information and was most predictive of adolescent acute trajectories (odds ratio [OR], 1.46; 95% CI, 1.27-1.68), with increasing and decreasing showing comparable risk (OR, 1.32; 95% CI, 1.16-1.50). Multi-trait models showed a similar genetic risk for depression trajectory as MDD-only risk across trajectories. Anxiety was associated with the adolescent acute trajectories (OR, 1.27; 95% CI, 1.10-1.45). Psychotic conditions were associated with later-onset depression patterns and ADHD with earlier-onset, aligning with the developmental timelines of these respective conditions.Discussion: The investigated genetic traits collectively contribute to diverse longitudinal patterns of depression, varying in severity, onset, and duration. A hierarchical factor model of multivariate genetic psychopathology demonstrated a comparable prediction of genetic risk to univariate depression scores for stratifying longitudinal depression in adolescence. It is important to acknowledge the genetic influence of multiple conditions on depression outcomes, particularly at different stages of vulnerability. Taking into account detailed and integrated genetic information will be valuable in effectively stratifying trajectories across adolescence and mental health conditions.
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| 2. |
- Grimes, Poppy Z., et al.
(författare)
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Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts
- 2024
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Ingår i: JAMA psychiatry. - : American Medical Association (AMA). - 2168-6238 .- 2168-622X.
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Tidskriftsartikel (refereegranskat)abstract
- IMPORTANCE: Adolescent depression is characterized by diverse symptom trajectories over time and has a strong genetic influence. Research has determined genetic overlap between depression and other psychiatric conditions; investigating the shared genetic architecture of heterogeneous depression trajectories is crucial for understanding disease etiology, prediction, and early intervention.OBJECTIVE: To investigate univariate and multivariate genetic risk for adolescent depression trajectories and assess generalizability across ancestries.DESIGN, SETTING, AND PARTICIPANTS: This cohort study entailed longitudinal growth modeling followed by polygenic risk score (PRS) association testing for individual and multitrait genetic models. Two longitudinal cohorts from the US and UK were used: the Adolescent Brain and Cognitive Development (ABCD; N = 11 876) study and the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 8787) study. Included were adolescents with genetic information and depression measures at up to 8 and 4 occasions, respectively. Study data were analyzed January to July 2023.MAIN OUTCOMES AND MEASURES: Trajectories were derived from growth mixture modeling of longitudinal depression symptoms. PRSs were computed for depression, anxiety, neuroticism, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism in European ancestry. Genomic structural equation modeling was used to build multitrait genetic models of psychopathology followed by multitrait PRS. Depression PRSs were computed in African, East Asian, and Hispanic ancestries in the ABCD cohort only. Association testing was performed between all PRSs and trajectories for both cohorts.RESULTS: A total sample size of 14 112 adolescents (at baseline: mean [SD] age, 10.5 [0.5] years; 7269 male sex [52%]) from both cohorts were included in this analysis. Distinct depression trajectories (stable low, adolescent persistent, increasing, and decreasing) were replicated in the ALSPAC cohort (6096 participants; 3091 female [51%]) and ABCD cohort (8016 participants; 4274 male [53%]) between ages 10 and 17 years. Most univariate PRSs showed significant uniform associations with persistent trajectories, but fewer were significantly associated with intermediate (increasing and decreasing) trajectories. Multitrait PRSs-derived from a hierarchical factor model-showed the strongest associations for persistent trajectories (ABCD cohort: OR, 1.46; 95% CI, 1.26-1.68; ALSPAC cohort: OR, 1.34; 95% CI, 1.20-1.49), surpassing the effect size of univariate PRS in both cohorts. Multitrait PRSs were associated with intermediate trajectories but to a lesser extent (ABCD cohort: hierarchical increasing, OR, 1.27; 95% CI, 1.13-1.43; decreasing, OR, 1.23; 95% CI, 1.09-1.40; ALSPAC cohort: hierarchical increasing, OR, 1.16; 95% CI, 1.04-1.28; decreasing, OR, 1.32; 95% CI, 1.18-1.47). Transancestral genetic risk for depression showed no evidence for association with trajectories.CONCLUSIONS AND RELEVANCE: Results of this cohort study revealed a high multitrait genetic loading of persistent symptom trajectories, consistent across traits and cohorts. Variability in univariate genetic association with intermediate trajectories may stem from environmental factors. Multitrait genetics may strengthen depression prediction models, but more diverse data are needed for generalizability.
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