Unravelling symptom-specific polygenic effects on maternal mental health during the perinatal period and postpartum
Bibliographic record
Abstract
BACKGROUND: While genetic factors are important influences on maternal mental health, few studies have used symptom-level analyses to examine how genetic liability is related to the experience of specific mental health problems in mothers. A symptom-level approach can account for disorder heterogeneity and delineate key associations between genetic liabilities and mental health. METHODS: Three waves of data (30 weeks of gestation, 6 and 18 months postpartum) from the Norwegian Mother, Father and Child Cohort Study (MoBa) were used to assess item-level associations between genetic liabilities to depression, anxiety, neuroticism and positive affect, and maternal mental health phenotypes (i.e., symptoms of anxiety, depression, positive and negative affect) using a network analysis approach. Sample sizes ranged from 46,537 to 59,308 mothers. RESULTS: PGSs exhibited both phenotype-specific associations (e.g., depression PGS linked with hopelessness, anxiety PGS linked with worry) and cross-phenotype (e.g., depression PGS linked with nervousness, positive affect PGS inversely related to anxiety and depressive symptoms) relationships, with partial correlations ranging between r = -0.025 and r = 0.024. Some PGS-phenotype associations were consistent (e.g., depression PGS linked with feeling like screaming or banging on something across all waves) and others inconsistent (e.g., anxiety PGS linked with nervousness only at 6 months postpartum) across the perinatal and postpartum periods. CONCLUSIONS: Our findings highlight symptom-level associations between PGSs and maternal mental health, which may be obscured when global measures of mental health (e.g., overall scores) are used. Identifying symptom-specific PGS associations could advance current understanding of aetiological influences on maternal mental health.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Polygenic score analysis of maternal mental health symptoms; a substantive genetics question.
The study investigates genetic associations with maternal mental-health symptoms.
Genetic epidemiology of maternal mental health symptoms; clinical/psychiatric genetics object, not metaresearch.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".