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Psychiatric disorders and neuroticism are closely associated with central nervous system, whose proper functioning depends on efficient protein renewal. This study aims to systematically analyze the association between anxiety / depression / neuroticism and each of the 439 proteins. 47,536 pQTLs of 439 proteins in brain, plasma and cerebrospinal fluid (CSF) were collected from recent genome-wide association study. Polygenic risk scores (PRS) of the 439 proteins were then calculated using the UK Biobank cohort, including 120,729 subjects of neuroticism, 255,354 subjects of anxiety and 316,513 subjects of depression. Pearson correlation analyses were performed to evaluate the correlation between each protein and each of the mental traits by using calculated PRSs as the instrumental variables of protein. In general population, six correlations were identified in plasma and CSF such as plasma protease C1 inhibitor (C1-INH) with neuroticism score (r = − 0.011, P = 2.56 × 10− 9) in plasma, C1-INH with neuroticism score (r = -0.010, P = 3.09 × 10− 8) in CSF, and ERBB1 with self-reported depression (r = − 0.012, P = 4.65 × 10− 5) in CSF. C1-INH and ERBB1 may induce neuroticism and depression by affecting brain function and synaptic development. Gender subgroup analyses found that BST1 was correlated with neuroticism score in male CSF (r = − 0.011, P = 1.80 × 10− 5), while CNTN2 was correlated with depression score in female brain (r = − 0.013, P = 6.43 × 10− 4). BST1 and CNTN2 may be involved in nervous system metabolism and brain health. Six common candidate proteins were associated with all three traits (P < 0.05) and were confirmed in relevant proteomic studies, such as C1-INH in plasma, CNTN2 and MSP in the brain. Our results provide novel clues for revealing the roles of proteins in the development of anxiety, depression and neuroticism. [ABSTRACT FROM AUTHOR]

Background 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. Methods 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  = 42 886) and cardiac function (N  = 1791) in the subjects with or without exposing to smoking and alcohol drinking. Results 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). Conclusions 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. [ABSTRACT FROM AUTHOR]

Rare variants are believed to play a substantial role in the genetic architecture of mental disorders, particularly in coding regions. However, limited evidence supports the impact of rare variants on anxiety. Using whole-exome sequencing data from 200,643 participants in the UK Biobank, we investigated the contribution of rare variants to anxiety. Firstly, we computed genetic risk score (GRS) of anxiety utilizing genotype data and summary data from a genome-wide association study (GWAS) on anxiety disorder. Subsequently, we identified individuals within the lowest 50% GRS, a subgroup more likely to carry pathogenic rare variants. Within this subgroup, we classified individuals with the highest 10% 7-item Generalized Anxiety Disorder scale (GAD-7) score as cases (N = 1869), and those with the lowest 10% GAD-7 score were designated as controls (N = 1869). Finally, we conducted gene-based burden tests and single-variant association analyses to assess the relationship between rare variants and anxiety. Totally, 47,800 variants with MAF ≤0.01 were annotated as non-benign coding variants, consisting of 42,698 nonsynonymous SNVs, 489 nonframeshift substitution, 236 frameshift substitution, 617 stop-gain and 40 stop-loss variants. After variation aggregation, 5066 genes were included in gene-based association analysis. Totally, 11 candidate genes were detected in burden test, such as RNF123 (P Bonferroni adjusted = 3.40 × 10−6), MOAP1 (P Bonferroni adjusted = 4.35 × 10−4), CCDC110 (P Bonferroni adjusted = 5.83 × 10−4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(P Bonferroni adjusted = 3.16 × 10−10) and rs16942615(CAMTA2) (P Bonferroni adjusted = 4.04 × 10−4). Notably, RNF123 , CCDC110 , DNAH2 , and CSKMT gene were identified in both tests. Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety. • We investigated the rare variants for anxiety among 3738 European individuals with lower anxiety GWAS-derived polygenic risk. • Gene-based burden test revealed 11 candidate genes for anxiety, such as RNF123 , DNAH2 and CCDC110. • Single variant association analysis detected 9 rare variants for anxiety, such as rs35726701(RNF123) and rs16942615(CAMTA2). [ABSTRACT FROM AUTHOR]

• Healthy sleep score demonstrated inverse associations with MD. • Healthy sleep pattern was associated with a reduced risk of MD when compared to those with poor sleep pattern at each group of physical activity, coffee intake, tea intake and genetic risk. • Encouraging healthy sleep pattern might be a great contributor to reducing the global burden of MD. Sleep behaviors were believed to be associated with mental disorders (MD). However, the underlying mechanism of such association relationship, especially the role of multiple lifestyle factors in it remains unclear. A total of 402,290 participants from UK Biobank who don't have MD at baseline were included. They were divided into poor, intermediate and healthy sleep patterns according to the sleep score, which was calculated based on the data collecting from five sleep behaviors. Cox proportional hazard model was used to estimate the associations between sleep behaviors and MD. The associations were further estimated when taking lifestyle factors such as physical activity, coffee intake, tea intake and genetic susceptibility into account. Healthy sleep pattern was associated with lower risk of overall MD status (HR,0.41, 95%CI,0.39–0.43), depressive disorders (HR,0.34, 95%CI,0.31–0.37) and anxiety disorders (HR,0.46, 95%CI,0.41–0.79), compared with poor sleep pattern. And in each subgroup of physical activity, tea intake, coffee intake, age and genetic risk scores (GRS), healthy sleep pattern could partly offset the risk of diseases. Our study suggested healthy sleep behaviors could diminish the negative effect from genetic predisposition and lifestyle factors on the risk of MD, highlighting the benefit of healthy sleep pattern. [ABSTRACT FROM AUTHOR]