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Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 ( p = 4.11 × 10 -8 , LRRTM4) for self-reported depression status and rs149760119 ( p = 3.88 × 10 -8 , GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

Disrupted brain structures and several life environmental factors have been shown to influence depression and anxiety, but their interactions with anxiety and depression remain elusive. Genome‐wide association study datasets of 15 brain structure longitudinal changes (N = 15,640) were obtained from the published study. Genotype and phenotype‐related data of depression, anxiety, and life environmental factors (including smoking, alcohol drinking, coffee intake, maternal smoking, physical activity, vitamin D, insomnia, sleep duration, and family satisfaction) were collected from UK Biobank. We calculated the polygenic risk scores (PRS) of 15 brain structure changes and then conducted linear regression analyses to explore the interactions of brain structure changes and life environmental factors on depression and anxiety using 15 brain structure change‐related PRS, life environmental factors and interactions of them as instrumental variables, and depression score or anxiety score as outcomes. Sex stratification in all analyses was performed to reveal sex‐specific differences in the interactions. We found 14 shared interactions related to both depression and anxiety in total sample, such as alcohol drinking × cerebellum white matter 3 (WM; beta = −.003, p =.018 for depression; beta = −003, p =.008 for anxiety) and maternal smoking × nucleus accumbens 2 (beta =.088, p =.002 for depression; beta =.070, p =.008 for anxiety). We also observed sex‐specific differences in the interactions, for instance, alcohol drinking × cerebellum WM 3 was negatively associated with depression and anxiety in males (beta = −.004, p =.020 for depression; beta = −.005, p =.002 for anxiety). Our study results reveal the important interactions between brain structure changes and several life environmental factors on depression and anxiety, which may help to explore the pathogenesis of depression and anxiety. [ABSTRACT FROM AUTHOR]

To facilitate investigation of diverse rodent behaviours in rodents' home cages, we have developed an integrated modular platform, the Smart Cage™ system ( Afa Sci, Inc. Burlingame, CA, USA), which enables automated neurobehavioural phenotypic analysis and in vivo drug screening in a relatively higher-throughput and more objective manner., The individual platform consists of an infrared array, a vibration floor sensor and a variety of modular devices. One computer can simultaneously operate up to 16 platforms via USB cables., The Smart Cage™ detects drug-induced increases and decreases in activity levels, as well as changes in movement patterns. Wake and sleep states of mice can be detected using the vibration floor sensor. The arousal state classification achieved up to 98% accuracy compared with results obtained by electroencephalography and electromyography. More complex behaviours, including motor coordination, anxiety-related behaviours and social approach behaviour, can be assessed using appropriate modular devices and the results obtained are comparable with results obtained using conventional methods., In conclusion, the Smart Cage™ system provides an automated and accurate tool to quantify various rodent behaviours in a 'stress-free' environment. This system, combined with the validated testing protocols, offers powerful a tool kit for transgenic phenotyping and in vivo drug screening. [ABSTRACT FROM AUTHOR]

To facilitate investigation of diverse rodent behaviours in rodents' home cages, we have developed an integrated modular platform, the Smart Cage™ system ( Afa Sci, Inc. Burlingame, CA, USA), which enables automated neurobehavioural phenotypic analysis and in vivo drug screening in a relatively higher-throughput and more objective manner., The individual platform consists of an infrared array, a vibration floor sensor and a variety of modular devices. One computer can simultaneously operate up to 16 platforms via USB cables., The Smart Cage™ detects drug-induced increases and decreases in activity levels, as well as changes in movement patterns. Wake and sleep states of mice can be detected using the vibration floor sensor. The arousal state classification achieved up to 98% accuracy compared with results obtained by electroencephalography and electromyography. More complex behaviours, including motor coordination, anxiety-related behaviours and social approach behaviour, can be assessed using appropriate modular devices and the results obtained are comparable with results obtained using conventional methods., In conclusion, the Smart Cage™ system provides an automated and accurate tool to quantify various rodent behaviours in a 'stress-free' environment. This system, combined with the validated testing protocols, offers powerful a tool kit for transgenic phenotyping and in vivo drug screening. [ABSTRACT FROM AUTHOR]

Background: Psychiatric disorders have great health hazards and the exact pathogeny remains elusive now. We aim to explore the potential interaction effects of mitochondrial function and human behavior on the risks of anxiety and depression.Methods: The genome-wide association study (GWAS) data of mitochondrial function (N = 383,476-982,072) were obtained from published studies. Individual level genotype and phenotype data of anxiety, depression and behavioral factors (including drinking, smoking and physical activity) were all from the UK Biobank (N = 84,805-85,164). We first calculated the polygenic risk scores (PRS) of mitochondrial function as the instrumental variables, and then constructed linear regression analyses to systematically explore the potential interaction effects of mitochondrial function and human behavior on anxiety and depression.Results: In total samples, we observed mitochondrial heteroplasmy (MtHz) vs. Drinking (PGAD-7 = 6.49 × 10-3; PPHQ-9 = 1.89 × 10-3) was positively associated with both anxiety and depression. In males, MtHz vs. Drinking (PMale = 3.46 × 10-5) was positively correlated with depression. In females, blood mitochondrial DNA copy number (mtDNA-CN) vs. Drinking (PFemale = 8.63 × 10-3) was negatively related to anxiety. Furthermore, we identified additional 6 suggestive interaction effects (P < 0.05) for anxiety and depression.Limitations: Considering all subjects were from UK Biobank, it should be careful to extrapolate our findings to other populations with different genetic background.Conclusions: Our results suggest the significant impacts of mitochondrial function and human behavior interactions on the development of anxiety and depression, providing new clues for clarifying the pathogenesis of anxiety and depression. [ABSTRACT FROM AUTHOR]

Smoking and alcohol consumption were associated with the development of depression and anxiety. 3′UTR APA quantitative trait loci (3'aQTLs) have been associated with multiple health states and conditions. Our aim is to evaluate the interactive effects of 3'aQTLs-alcohol consumption/tobacco smoking on the risk of anxiety and depression. The 3'aQTL data of 13 brain regions were extracted from the large-scale 3'aQTL atlas. The phenotype data (frequency of cigarette smoking and alcohol drinking, anxiety score, self-reported anxiety, depression score and self-reported depression) of 90,399–103,011 adults aged 40–69 years living in the UK and contributing to the UK Biobank during 2006–2010, were obtained from the UK Biobank cohort. The frequency of cigarette smoking and alcohol drinking of each subject were defined by the amount of smoking and alcohol drinking of self-reported, respectively. The continuous alcohol consumption/smoking terms were further categorized in tertiles. 3'aQTL-by-environmental interaction analysis was then performed to evaluate the associations of gene-smoking/alcohol consumption interactions with anxiety and depression using generalized linear model (GLM) of PLINK 2.0 with an additive mode of inheritance. Furthermore, GLM was also used to explore the relationship between alcohol consumption/smoking with hazard of anxiety/depression stratified by allele for the significant genotyped SNPs that modified the alcohol consumption/smoking-anxiety/depression association. The interaction analysis identified several candidate 3'aQTLs-alcohol consumption interactions, such as rs7602638 located in PPP3R1 (β = 0.08, P = 6.50 × 10−6) for anxiety score; rs10925518 located in RYR2 (OR = 0.95, P = 3.06 × 10−5) for self-reported depression. Interestingly, we also observed that the interactions between TMOD1 (β = 0.18, P = 3.30 × 10−8 for anxiety score; β = 0.17, P = 1.42 × 10−6 for depression score), ZNF407 (β = 0.17, P = 2.11 × 10−6 for anxiety score; β = 0.15, P = 4.26 × 10−5 for depression score) and alcohol consumption was not only associated with anxiety, but related to depression. Besides, we found that relationship between alcohol consumption and hazard of anxiety/depression was significantly different for different SNPs genotypes, such as rs34505550 in TMOD1 (AA: OR = 1.03, P = 1.79 × 10−6; AG: OR = 1.00, P = 0.94; GG: OR = 1.00, P = 0.21) for self-reported anxiety. The identified 3'aQTLs-alcohol consumption/smoking interactions were associated with depression and anxiety, and its potential biological mechanisms need to be further revealed. Our study identified important interactions between candidate 3'aQTL and alcohol consumption/smoking on depression and anxiety, and found that the 3'aQTL may modify the associations between consumption/smoking with depression and anxiety. These findings may help to further explore the pathogenesis of depression and anxiety. • Interaction between rs7602638 located in PPP3R1 and alcohol consumption was positively associated with anxiety score. • Interaction between rs10925518 in RYR2 and alcohol consumption was negatively correlated with self-reported depression. • Interactions between TMOD1, NLRP1, ZNF407 and alcohol consumption were positively associated with anxiety and depression. [ABSTRACT FROM AUTHOR]

The effects of gene-by-environment (G×E) interactions on complex diseases are significant, especially the superimposed effects of multiple environmental factors. However, research on the multi-environments-gene interactions of anxiety, depression, and self-harm is still limited. This study included white individuals (N = 66,041–74,482) from the UK Biobank. We fitted all environmental factors to a single environmental score (ES), and the estimated ES was used to calculate the multiplicative interaction effects between ES and genome-wide SNPs. Heritability was stratified by minor allele frequency (MAF) and linkage disequilibrium (LD). Our research found 10 loci with significant interaction effects, such as rs114830993 (PRICKLE2, P = 2.30 × 10−8), rs151323364 (ASTN2, P = 2.71 × 10−10) and rs536631793 (SYN3, P = 4.09 × 10−8). In addition, we found that G×E heritability has a significant contribution to the depression of Patient Health Questionnaire-9 (PHQ-9) scores (h2 G×E (female) = 6.1%, h2 G×E (male) = 8.7%). Our research supported the important influence of multi-environments-gene interactions on anxiety, depression, and self-harm and provided clues for the prevention and etiological research of them. [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]

Previous genetic studies of anxiety and depression were mostly based on independent phenotypes. This study aims to investigate the shared and specific genetic structure between anxiety and depression. To identify the underlying factors of Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and their combined scale (joint scale), we employed exploratory factor analysis (EFA) using the eigenvalue of parallel analysis. Subsequently, we conducted a genome-wide association study (GWAS) for these factors. In addition, we utilized LD Score Regression (LDSC) to determine the genetic correlations between the identified factors and four common mental disorders, three sleep phenotypes, and other traits that have been previously linked to anxiety and depression. The EFA uncovered two factors for the GAD-7 scale, namely nervousness and disturbance, two factors for the PHQ-9 scale, namely negative affect and sleep/appetite disturbance, and four factors for the joint scale, specifically nervousness, anhedonia, sleep/appetite disturbance, and fidget. We identified two genome-wide significant genomic loci, with overlap across GAD-7 factor 1 and joint scale factor 1: rs148579586 (P GAD-7 = 1.365 × 10−09, P Joint scale = 1.434 × 10–09) and rs201074060 (P GAD-7 = 3.672 × 10−09, P Joint scale = 3.824 × 10−09). Genetic correlations in factors ranged from 0.722 to 1.000 (all p < 1.786 × 10−03) with 27 of 28 correlations being significantly smaller than one. The genetic correlations with external phenotypes showed small variation across the eight factors. Unidimensional structures can provide more precise scores, which can aid in identifying the shared and specific genetic associations between anxiety and depression. This is a crucial step in characterizing the genetic structure of these conditions and their co-occurrence. • We used EFA to identify potential factors for GAD-7, PHQ-9 and the joint scale. • we found common and specific genetic structures between anxiety and depression. • Single-dimensional structures can build more precise fractions. [ABSTRACT FROM AUTHOR]