Depression Polygenic Risk Scores Research Articles

Genetic Risk For Depression and Quality of Life in Patients With Head and Neck Cancer

Although patients with head and neck cancer (HNC) have been shown to experience high distress, few longitudinal studies include a comprehensive evaluation of biopsychosocial factors affecting quality of life (QoL), including genetic risk for depression. To identify factors at the time of cancer diagnosis associated with QoL scores at 3 months after treatment in patients newly diagnosed with a first occurrence of HNC. This prospective longitudinal study of 1464 participants with a 3-month follow-up, including structured clinical interviews and self-administered measures was carried out at the Department of Otolaryngology Head and Neck Surgery at 2 tertiary care McGill University Affiliated Hospitals, McGill University Health Centre, and Jewish General Hospital. Eligible patients were adults newly diagnosed within 2 weeks with a primary first occurrence of HNC, had a Karnofsky Performance Scale score higher than 60, and an expected survival of more than 6 months. Two hundred and twenty-three patients (72%) consented to participate and completed the baseline questionnaire, and 71% completed the 3-month follow-up measures. An a priori conceptual model including sociodemographics, medical variables, psychosocial risk factors, and a polygenic risk score for depression (PRS-D) was tested. The Functional Assessment of Cancer Therapy-Head and Neck measured QoL at baseline and at 3 months. Participants were mostly men (68.7%), with a mean (range) age of 62.9 (31-92) years, 36.6% having a university degree, 35.6% living alone, and 71.4% diagnosed with advanced HNC with mostly cancers being of the oropharynx (42.2%), oral cavity (17%), and larynx (16.3%). QoL at 3 months after HNC diagnosis was associated with higher PRS-D (B = -4.71; 95% CI, -9.18 to -0.23), and a diagnosis of major depressive disorder within 2 weeks of an HNC diagnosis (B = -32.24; 95% CI, -51.47 to 13.02), lifetime suicidal ideation (B = -22.39; 95% CI, -36.14 to -8.65), living with someone (B = 12.48; 95% CI, 3.43-21.52), having smoked cigarettes in the past 30 days pre-HNC diagnosis (B = -15.50; 95% CI, -26.07 to -4.93), chemotherapy type (B = -11.13; 95% CI, -21.23 to -1.02), and total radiotherapy dose (Gy) (B = -0.008; 95% CI, -0.01 to -0.002). This study identified the predictive value of a genetic predisposition to depression on QoL and function immediately after oncologic treatments. These findings highlight the potential importance of genetic profiling pretreatment to identify those most susceptible to experience QoL and functional compromise. Depression is a clear area of public health concern and should be a central focus in the treatment of patients with HNC.

Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts

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. To investigate univariate and multivariate genetic risk for adolescent depression trajectories and assess generalizability across ancestries. 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. 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. 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. 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.

Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function.

Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (β = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (β = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (β = 0.088, FDR = 0.010), whereas no such association was found with PRS (β = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (β = -0.042, FDR = 6.30 × 10-9), but not with PRS (β = -0.003, FDR = 0.857). Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder

Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Patients with MDD and healthy controls. Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

The impact of COVID-19 on a college freshman sample reveals genetic and nongenetic forms of susceptibility and resilience to stress.

Using a longitudinal approach, we sought to define the interplay between genetic and nongenetic factors in shaping vulnerability or resilience to COVID-19 pandemic stress, as indexed by the emergence of symptoms of depression and/or anxiety. University of Michigan freshmen were characterized at baseline using multiple psychological instruments. Subjects were genotyped, and a polygenic risk score for depression (MDD-PRS) was calculated. Daily physical activity and sleep were captured. Subjects were sampled at multiple time points throughout the freshman year on clinical rating scales, including GAD-7 and PHQ-9 for anxiety and depression, respectively. Two cohorts (2019 to 2021) were compared to a pre-COVID-19 cohort to assess the impact of the pandemic. Across cohorts, 26 to 40% of freshmen developed symptoms of anxiety or depression (N = 331). Depression symptoms significantly increased in the pandemic years and became more chronic, especially in females. Physical activity was reduced, and sleep was increased by the pandemic, and this correlated with the emergence of mood symptoms. While low MDD-PRS predicted lower risk for depression during a typical freshman year, this genetic advantage vanished during the pandemic. Indeed, females with lower genetic risk accounted for the majority of the pandemic-induced rise in depression. We developed a model that explained approximately half of the variance in follow-up depression scores based on psychological trait and state characteristics at baseline and contributed to resilience in genetically vulnerable subjects. We discuss the concept of multiple types of resilience, and the interplay between genetic, sex, and psychological factors in shaping the affective response to different types of stressors.

Shared Genetic Risk in the Association of Screen Time With Psychiatric Problems in Children

Children's exposure to screen time has been associated with poor mental health outcomes, yet the role of genetic factors remains largely unknown. To assess the extent of genetic confounding in the associations between screen time and attention problems or internalizing problems in preadolescent children. This cohort study analyzed data obtained between 2016 and 2019 from the Adolescent Brain Cognitive Development Study at 21 sites in the US. The sample included children aged 9 to 11 years of genetically assigned European ancestry with self-reported screen time. Data were analyzed between November 2021 and September 2023. Child-reported daily screen time (in hours) was ascertained from questionnaires completed by the children at baseline. Child psychiatric problems, specifically attention and internalizing problems, were measured with the parent-completed Achenbach Child Behavior Checklist at the 1-year follow-up. Genetic sensitivity analyses model (Gsens) was used, which incorporated polygenic risk scores (PRSs) of both exposure and outcomes as well as either single-nucleotide variant (SNV; formerly single-nucleotide polymorphism)-based heritability or twin-based heritability to estimate genetic confounding. The 4262 children in the sample included 2269 males (53.2%) with a mean (SD) age of 9.9 (0.6) years. Child screen time was associated with attention problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD) and internalizing problems (β = 0.03 SD; 95% CI, 0.003-0.06 SD). The television time PRS was associated with child screen time (β = 0.18 SD; 95% CI, 0.14-0.23 SD), the attention-deficit/hyperactivity disorder PRS was associated with attention problems (β = 0.13 SD; 95% CI, 0.10-0.16 SD), and the depression PRS was associated with internalizing problems (β = 0.10 SD; 95% CI, 0.07-0.13 SD). These PRSs were associated with cross-traits, suggesting genetic confounding. Estimates using PRSs and SNV-based heritability showed that genetic confounding accounted for most of the association between child screen time and attention problems and for 42.7% of the association between child screen time and internalizing problems. When PRSs and twin-based heritability estimates were used, genetic confounding fully explained both associations. Results of this study suggest that genetic confounding may explain a substantial part of the associations between child screen time and psychiatric problems. Genetic confounding should be considered in sociobehavioral studies of modifiable factors for youth mental health.

Polygenic risk for depression and anterior and posterior hippocampal volume in children and adolescents

BackgroundDepression has frequently been associated with smaller hippocampal volume. The hippocampus varies in function along its anterior-posterior axis, with the anterior hippocampus more strongly associated with stress and emotion processing. The goals of this study were to examine the associations among parental history of anxiety/depression, polygenic risk scores for depression (PGS-DEP), and anterior and posterior hippocampal volumes in children and adolescents. To examine specificity to PGS-DEP, we examined associations of educational attainment polygenic scores (PGS-EA) with anterior and posterior hippocampal volume. MethodsParticipants were 350 3- to 21-year-olds (46 % female). PGS-DEP and PGS-EA were computed based on recent, large-scale genome-wide association studies. High-resolution, T1-weighted magnetic resonance imaging (MRI) data were acquired, and a semi-automated approach was used to segment the hippocampus into anterior and posterior subregions. ResultsChildren and adolescents with higher polygenic risk for depression were more likely to have a parent with a history of anxiety/depression. Higher polygenic risk for depression was significantly associated with smaller anterior but not posterior hippocampal volume. PGS-EA was not associated with anterior or posterior hippocampal volumes. LimitationsParticipants in these analyses were all of European ancestry. ConclusionsPolygenic risk for depression may lead to smaller anterior but not posterior hippocampal volume in children and adolescents, and there may be specificity of these effects to PGS-DEP rather than PGS-EA. These findings may inform the earlier identification of those in need of support and the design of more effective, personalized treatment strategies.Declarations of interest: none. Declarations of interestNone.

Polygenic risk scores and the prediction of onset of mood and psychotic disorders in adolescents and young adults.

To examine whether polygenic risk scores (PRS) for neuroticism, depression, bipolar disorder and schizophrenia are higher in individuals manifesting trans-diagnostic risk factors for the development of major mental disorders and whether PRS enhance prediction of early onset full-threshold disorders. Using data from the Brisbane Longitudinal Twin Study, we examined individual PRS for neuroticism, depression, bipolar disorder and schizophrenia, recorded evidence of subthreshold syndromes and family history of mood and/or psychotic disorders and noted progression to trans-diagnostic clinical caseness (onset of major mental disorders) at follow-up. We undertook multivariate, receiver operating curve and logistic regression analyses that were adjusted for known variables of influence (age, twin status, and so on). Of 1473 eligible participants (female = 866, 59%; mean age 26.3 years), 28% (n = 409) met caseness criteria for a mood and/or psychotic disorder. All PRS were higher in cases versus non-cases but associations with different levels of risk were inconsistent. The prediction of caseness (reported as area under the curve with 95% confidence intervals [CI]) improved from 0.68 (95% CI: 0.65, 0.71) when estimated using clinical risk factors alone up to 0.71 (95% CI: 0.69, 0.73) when PRS were added to the model. Logistic regression identified five variables that optimally classified individuals according to caseness: age, sex, individual risk characteristics, PRS for depression and mental health case status of cotwins or siblings. The findings need replication. However, this exploratory study suggests that combining PRS with other risk factors has the potential to improve outcome prediction in youth.

Development of risk prediction models for depression combining genetic and early life risk factors.

Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated. Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression. In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively. We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.

Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19

Despite the high contagion and mortality rates that have accompanied the coronavirus disease-19 (COVID-19) pandemic, the clinical presentation of the syndrome varies greatly from one individual to another. Potential host factors that accompany greater risk from COVID-19 have been sought and schizophrenia (SCZ) patients seem to present more severe COVID-19 than control counterparts, with certain gene expression similarities between psychiatric and COVID-19 patients reported. We used summary statistics from the last SCZ, bipolar disorder (BD), and depression (DEP) meta-analyses available on the Psychiatric Genomics Consortium webpage to calculate polygenic risk scores (PRSs) for a target sample of 11,977 COVID-19 cases and 5943 subjects with unknown COVID-19 status. Linkage disequilibrium score (LDSC) regression analysis was performed when positive associations were obtained from the PRS analysis. The SCZ PRS was a significant predictor in the case/control, symptomatic/asymptomatic, and hospitalization/no hospitalization analyses in the total and female samples; and of symptomatic/asymptomatic status in men. No significant associations were found for the BD or DEP PRS or in the LDSC regression analysis. SNP-based genetic risk for SCZ, but not for BD or DEP, may be associated with higher risk of SARS-CoV-2 infection and COVID-19 severity, especially among women; however, predictive accuracy barely exceeded chance level. We believe that the inclusion of sexual loci and rare variations in the analysis of genomic overlap between SCZ and COVID-19 will help to elucidate the genetic commonalities between these conditions.

The impact of genetic predisposition to depression on quality of life in patients with head and neck cancer immediately post-treatment: A longitudinal study.

6063 Background: The primary aim of this study was to investigate the contribution of genetic predisposition to depression, through polygenic risk scores (PRS), on quality of life levels in patients with head and neck cancer (HNC) immediately post-treatment period (i.e., 3 months post-diagnosis). Methods: Prospective longitudinal study of 223 consecutive adult patients with HNC (72% participation) newly diagnosed with a first occurrence of primary HNC, including saliva samples analyzed using the Illumina PsychChip, psychometric measures, Structured Clinical DSM Interviews, and medical chart reviews. Results: Level of quality of life at 3 months on the FACT-G+H&N was predicted by (r2 = 0.51, r2 adj. = 0.33, p = 0.001) the polygenic risk score for depression (standardized b = -0.28, p = 0.01) and a previous history of suicidal ideation (standardized b = -0.25, p = 0.04). Other variables were non-significant in the analyses: sociodemographic (i.e., age, sex, education, living alone), psychosocial (i.e., SCID current and past diagnoses (trend), past history of abuse), and medical variables (i.e., cancer stage and site, HPV status, functional status/ECOG, treatment). Conclusions: Our results outline the importance of attending to genetic predisposition and past history of suicidal ideation as markers for quality of life compromise immediately post-treatment in patients with head and neck cancers. Strategies are needed to address psychosocial vulnerability early-on as part of pre-habilitation in the treatment of patients with head and neck cancer.

Impact of selection bias on polygenic risk score estimates in healthcare settings.

Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions. PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals. Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8-11.2%) in the unweighted analysis but only 6.2% (5.0-7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7-35.4%) to 28.9% (25.8-31.9%) after IP weighting. Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

108. Distinct Developmental Signatures of Novel Sex-Specific Transcriptome-Based Polygenic Risk Scores for Depression

108. Distinct Developmental Signatures of Novel Sex-Specific Transcriptome-Based Polygenic Risk Scores for Depression

A comparison of taxon-like schizotypal clusters of non-clinical individuals on polygenic scores for schizophrenia and related phenotypes

IntroductionSchizotypy is conceptualized to be on the continuum of the risk for psychosis. However, previous studies that used the dimensional approach to schizotypy failed to confirm the expected relations between schizotypal traits and polygenic risk scores (PRS) for schizophrenia. A taxonic approach is an alternative way of looking at schizotypy, but to the best of our knowledge it has not been used to explore the genetic architecture of this construct.ObjectivesThe present study aimed to fill this gap by comparing groups with different profiles of schizotypal traits on PRS for schizophrenia and related phenotypes.MethodsTo find clusters with different combinations of schizotypal traits, we conducted data mining of an ethnically homogeneous cohort of 1377 healthy individuals completed the Schizotypal Personality Questionnaire. Four clusters emerged, termed low, positive, negative, and high mixed schizotypy. Approximately equal groups from each cluster were selected for genotyping. After quality control, PRS for schizophrenia, depression, neuroticism, and educational attainment were calculated for 320 individuals (mean age = 31.74, SD 12.25 years, 59% women) based on the summary statistics of the largest genome-wide association studies of the respective traits. The groups from different clusters were then compared on PRS.ResultsThe schizotypy groups were similar in age and sex composition but differed in educational attainment and neuroticism (as measured by the Eysenck Personality Inventory), with the high mixed schizotypes having the lowest education and highest neuroticism scores among the schizotypy groups. There were no statistically significant differences between the groups in any PRS. However, the high mixed schizotypy group showed the largest PRS for schizophrenia, neuroticism, and depression. At a nominally significant level, it differed from negative and positive schizotypes in schizophrenia PRS and from low schizotypy in neuroticism PRS. The positive and negative schizotypal groups had non-significantly lower schizophrenia PRS than low schizotypy subjects.ConclusionsOur study showed no reliable difference between groups with different schizotypal profiles in PRS of schizophrenia and related phenotypes. At the same time, a number of trends were observed suggesting that only the high mixed schizotypy might be viewed as a condition with an elevated genetic risk of schizophrenia. This is in line with Meehl’s quasi-dimensional model of schizotypy and warrants further investigation in larger samples. This work was supported by the Russian Foundation for Basic Research under Grant 20-013-00230.Disclosure of InterestNone Declared

More bipolar than bipolar disorder – a polygenic risk score analysis of postpartum psychosis

IntroductionPostpartum psychosis is a rare psychiatric emergency, occurring days to weeks after 1-2 per 1000 deliveries. Its low prevalence makes it difficult to recruit enough participants to investigate the underlying pathophysiology. It is epidemiologically linked to bipolar disorder, which one study also found it to resemble in genetic susceptibility for psychiatric disorders (Di Florio et al. Lancet Psych 2021; 8: 1045–52).ObjectivesIn this study we aim to investigate polygenic liability for psychiatric disorders in two new Swedish postpartum psychosis cohorts.MethodsCases with postpartum psychosis, defined as a psychiatric hospitalization within 6 weeks after delivery, and/or receiving a diagnosis of F53.1 (ICD 10) or 294.40 (ICD 8.), parous women with severe mental illness without postpartum psychosis, and healthy parous controls were identified in two Swedish genetic studies: the Swedish bipolar collection (SWEBIC) and Predictors for ECT (PREFECT). Polygenic risk scores (PRS) were calculated from summary statistics from genome wide studies on bipolar disorder (Mullins et al. Nat Genet 2021; 53 817-829), schizophrenia (Trubetskoy et al. Nature 2022; 604 502-508) and major depression (Wray et al. Nat Genet. 2018; 50 668-681). The p-value thresholds best predicting their respective phenotype were used in logistic regression analyses with the first six principal components and genotyping platform as confounders.ResultsWe identified 176 patients with postpartum psychosis and genetic information (N(SWEBIC)=126, N(PREFECT)=50). Compared with healthy parous women, patients with postpartum psychosis had significantly higher PRS for bipolar disorder (SWEBIC: odds ratio [OR] 2.6 (95% confidence interval [CI] 1.9-3.5), PREFECT: OR 2.4 (95% CI 1.8-3.2), Figure 1.) and schizophrenia (SWEBIC: OR 1.6 (95% CI 1.2-2.2), PREFECT: OR 1.8 (95%; CI 1.3-2.5)). Patients with postpartum psychosis had significantly higher PRS for bipolar disorder (SWEBIC: OR 1.4 (95% CI 1.2-1.8), PREFECT: OR 1.5 (95% CI 1.1-2)) compared with parous women with severe mental illness without postpartum psychosis. We found no associations with major depression PRS in either cohort.Image:ConclusionsWe replicated previous findings of significantly higher PRS for bipolar disorder and schizophrenia in postpartum psychosis compared with healthy controls. In contrast to previous research, we find postpartum psychosis cases to have higher PRS for bipolar disorder than bipolar disorder cases. Our findings highlight the genetic influence in postpartum psychosis and support previous genetic and epidemiological evidence that postpartum psychosis lies on the bipolar spectrum.Disclosure of InterestNone Declared

Passive and active suicidal ideation in a population-based sample of older adults: Associations with polygenic risk scores of relevance for suicidal behavior.

There are few studies investigating genetic factors related to suicidal ideation or behavior in older adult populations. Our aim was to test associations between passive and active suicidal ideation and polygenic risk scores (PRSs) for suicidality and other traits of relevance for suicidality in old age (i.e. depression, neuroticism, loneliness, Alzheimer's disease, cognitive performance, educational attainment, and several specified vascular diseases) in a population-based sample aged 70 years and older. Participants in the prospective H70 study in Gothenburg, Sweden, took part in a psychiatric examination that included the Paykel questions on active and passive suicidal ideation. Genotyping was performed with the Neurochip (Illumina). After quality control of the genetic data the sample included 3467 participants. PRSs for suicidality and other related traits were calculated based on summary statistics from recent GWASs of relevance. Exclusion of persons with dementia or incomplete data on suicidal ideation yielded 3019 participants, age range 70-101 years. Associations between past year suicidal ideation (any level) and selected PRSs were analysed using general estimation equation (GEE) models, adjusted for sex and age. We observed associations between passive/active suicidal ideation and PRSs for depression (three versions), neuroticism, and general cognitive performance. After excluding individuals with current major depressive disorder (MDD), similar associations were seen with PRS for neuroticism, general cognitive performance and two PRSs for depression. No associations were found between suicidal ideation and PRSs for suicidality, loneliness, Alzheimer's disease, educational attainment, or vascular disease. Our results could indicate which types of genetic susceptibility that are of importance for suicidality in old age, and these findings can help to shed light on potential mechanisms that may be involved in passive and active suicidal ideation in late-life, also in those with no current MDD. However, due to the limited sample size, the results need to be interpreted with caution until replicated in larger samples.

The association between trauma exposure, polygenic risk and individual depression symptoms

BackgroundTraumatic experiences are associated with increased risk for major depressive disorder (MDD). This study sought to determine the extent that trauma exposure, depression polygenic risk scores (PRS), and their interaction are associated with MDD and individual depression symptoms. MethodsData from 102,182 individuals from the large-scale UK Biobank population cohort was analysed. A series of regression analyses were conducted to estimate the association between trauma, depression PRS and 1) current depression, 2) lifetime MDD case-control status, 3) nine individual current depressive symptoms, and 4) thirteen individual symptoms experienced during a major depressive episode. Additive and multiplicative PRS-by-trauma interactions were also assessed. ResultsTrauma and depression PRS were significantly associated with both current depression and lifetime MDD. A positive, additive interaction effect was observed on depression, but multiplicative interactions were not significant. Trauma exposure and depression PRS were associated with specific patterns of depression symptoms; Trauma was associated with low self-esteem, suicidal ideation, and atypical (but not typical) neurovegetative symptoms. Additive interaction effects were observed on six out of nine current depressive symptoms. ConclusionsTrauma exposure and genetic predisposition to depression may lead to particular symptomatology, which may contribute to the extreme clinical heterogeneity observed in individuals with major depression.

Stress-related exposures amplify the effects of genetic susceptibility on depression and anxiety

It is unclear whether and to what extent stress-related exposures moderate the effects of polygenic risk scores (PRSs) on depression and anxiety. We aimed to examine such moderation effects for a variety of stress-related exposures on depression and anxiety. We included 41,810 participants with both genome-wide genetic data and measurements of depression and anxiety in the Lifelines Cohort Study. Current depression and anxiety were measured by the MINI International Neuropsychiatric Interview. Stress-related exposures included long-term difficulties, stressful life events, reduced social support, childhood trauma, and loneliness, which were measured by self-report questionnaires. PRSs were calculated based on recent large genome-wide association studies for depression and anxiety. We used linear mixed models adjusting for family relationships to estimate the interactions between PRSs and stress-related exposures. Nine of the ten investigated interactions between the five stress-related exposures and the two PRSs for depression and anxiety were significant (Ps < 0.001). Reduced social support, and higher exposure to long-term difficulties, stressful life events, and loneliness amplified the genetic effects on both depression and anxiety. As for childhood trauma exposure, its interaction with the PRS was significant for depression (P = 1.78 × 10–05) but not for anxiety (P = 0.32). Higher levels of stress-related exposures significantly amplify the effects of genetic susceptibility on depression and anxiety. With a large sample size and a comprehensive set of stress-related exposures, our study provides powerful evidence on the presence of polygenic risk-by-environment interactions in relation to depression and anxiety.

Polygenic Risk and Episode Polarity Among Individuals With Bipolar Disorder.

The authors investigated associations between polygenic liabilities for bipolar disorder, major depression, and schizophrenia and episode polarity among individuals with bipolar disorder. The sample consisted of 2,705 individuals diagnosed with bipolar disorder at Danish psychiatric hospitals between January 1995 and March 2017. DNA was obtained from dried blood spots collected at birth as part of routine screening. Polygenic risk scores (PRSs) for bipolar disorder, major depression, and schizophrenia were generated using a meta-PRS method combining internally and externally trained components. Associations between PRS and polarity at first episode, polarity at any episode, and number of episodes with a given polarity were evaluated for each disorder-specific PRS using logistic and negative binominal regressions adjusted for the other two PRSs, age, sex, genotype platform, and five ancestral principal components. PRS for bipolar disorder was positively associated with any manic episodes (odds ratio=1.23, 95% CI=1.09-1.38). PRS for depression was positively associated with any depressive (odds ratio=1.11, 95% CI=1.01-1.23) and mixed (odds ratio=1.15, 95% CI=1.03-1.28) episodes and negatively associated with any manic episodes (odds ratio=0.76, 95% CI=0.69-0.84). PRS for schizophrenia was positively associated with any manic episodes (odds ratio=1.13, 95% CI=1.01-1.27), but only when psychotic symptoms were present (odds ratio for psychotic mania: 1.27, 95% CI=1.05-1.54; odds ratio for nonpsychotic mania: 1.06, 95% CI=0.93-1.20). These patterns were similar for first-episode polarity and for the number of episodes within each pole. PRSs for bipolar disorder, major depression, and schizophrenia are associated with episode polarity and psychotic symptoms in a congruent manner among individuals with bipolar disorder.

Polygenic Risk and Social Support in Predicting Depression Under Stress.

Despite substantial progress in identifying genomic variation associated with major depression, the mechanisms by which genomic and environmental factors jointly influence depression risk remain unclear. Genomically conferred sensitivity to the social environment may be one mechanism linking genomic variation and depressive symptoms. The authors assessed whether social support affects the likelihood of depression development differently across the spectrum of genomic risk in two samples that experienced substantial life stress: 1,011 first-year training physicians (interns) in the Intern Health Study (IHS) and 435 recently widowed Health and Retirement Study (HRS) participants. Participants' depressive symptoms and social support were assessed with questionnaires that were administered before and after the life stressor. Polygenic risk scores (PRSs) for major depressive disorder were calculated for both samples. Depressive symptom scores increased by 126% after the start of internship in the IHS sample and by 34% after widowing in the HRS sample. There was an interaction between depression PRS and change in social support in the prediction of depressive symptoms in both the IHS sample (incidence rate ratio [IRR]=0.96, 95% CI=0.93, 0.98) and the HRS sample (IRR=0.78, 95% CI=0.66, 0.92), with higher depression PRS associated with greater sensitivity to changes in social support. Johnson-Neyman intervals indicated a crossover effect, with losses and gains in social support moderating the effect of PRS on depressive symptoms. (Johnson-Neyman interval in the IHS sample, -0.02, 0.71; in the HRS sample, -0.49, 1.92). The study findings suggest that individuals with high genomic risk for developing increased depressive symptoms under adverse social conditions also benefit more from nurturing social environments.