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Background: Electronic devices use has been reported to be associated with depression. However, limited effort has been provided to elucidate the associations between electronic devices use and mental traits in interaction with genetic factors.Methods: We first conducted an observational study consisting of 138 976-383 742 participants for TV watching, 29 636-38 599 participants for computer using and 118 61-330 985 participants for computer playing in the UK Biobank cohort. A linear regression model was used to evaluate the associations between common mental traits and electronic devices use. Subsequently, a genome-wide gene-environment interaction study (GWEIS) was performed by PLINK2.0 to estimate the interaction effects of genes and electronic devices use on the risks of the four mental traits.Results: In the UK Biobank cohort, significant associations were observed between electronic devices use and mental traits (all P < 1.0 × 10-9 ), including depression score (B = 0.094 for TV watching), anxiety score (B = 0.051 for TV watching), cigarette smoking (B = 0.046 for computer using) and alcohol drinking (B = 0.010 for computer playing). GWEIS identified multiple mental traits associated loci, interacting with electronic devices use, such as DCDC2 (rs115986722, P = 4.10 × 10-10 ) for anxiety score and TV watching, PRKCE (rs56181965, P = 9.64 × 10-10 ) for smoking and computer using and FRMD4A (rs56227933, P = 7.42 × 10-11 ) for depression score and computer playing.Conclusions: Our findings suggested that electronic devices use was associated with common mental traits and provided new clues for understanding genetic architecture of mental traits. [ABSTRACT FROM AUTHOR]

A ground source heat pump system is a highly efficient renewable heating, cooling, and ventilation system that utilizes the ground as a heat source or sink via ground heat exchangers. Energy pile is an energy geotechnical structure that couples a ground heat exchanger with a geotechnical structure, leading to low capital cost. The design of energy piles can be challengeable due to their complicated geometries and the requirement of mechanical load. This study focuses on the heat transfer across the concrete–soil interface of energy piles in urban areas. Case studies from two projects, the Lambeth College and Shell Centre projects, are presented and discussed. The back analysis of two energy pile cases illustrated that the heat transfer coefficient at the pile–soil interface can differ between the cooling mode and the heating mode. It can be concluded that the difference in the heat transfer coefficient is influenced by a number of factors such as soil properties, concrete (grout) properties, and the installation method. [ABSTRACT FROM AUTHOR]

Background: Herpes simplex virus‐1 (HSV‐1) infection is reported to be associated with depression. But limited efforts were made to investigate the relationship between HSV‐1 infection and the risk of depression, especially from the genetic perspective. Methods: In UK Biobank cohort, linear and logistic regression analyses were first performed to test the association of HSV‐1 seropositivity/antibody with depression, including depression status (N = 2951) and Patient Health Questionnaire (PHQ) score (N = 2839). Using individual genotypic and phenotypic data from the UK Biobank, genome‐wide environmental interaction study (GWEIS) was then conducted by PLINK2.0 to evaluate gene × HSV‐1 interacting effect on the risk of depression. Finally, gene set enrichment analysis was conducted to identify the biological pathways involved in the observed gene × HSV‐1 interaction for depression. Result: In UK Biobank cohort, significant associations were observed between depression status and HSV‐1 (odds ratio [OR] = 1.09; 95% confidence interval [CI], 1.02‐1.16; P = 2.40 × 10−2 for HSV‐1 antibody and OR = 1.28; 95% CI, 1.12‐1.47, P = 2.59 × 10−3 for HSV‐1 seropositivity). GWEIS revealed four significant gene × HSV‐1 interaction signals for PHQ score (all P < 5.0 × 10−8) and the leading loci was SULF2 (rs6094791, P = 8.60 × 10−9). Pathway analyses identified 21 pathways for PHQ score and 19 for depression status, including multiple neural development‐ and immune‐related ones, such as KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION (false discovery rate [FDR] = 3.18 × 10−2) for depression and LU_AGING_BRAIN_UP (FDR = 4.21 × 10−2) for PHQ score. Conclusion: Our results suggested that HSV‐1 was associated with the risk of depression, which was modulated by the several genes that were related to the nerve development or immune function. [ABSTRACT FROM AUTHOR]

We aim to explore the mechanism of relationship between insomnia and liver metabolism by examining the gene × insomnia interactions. Individual level genotypic and phenotypic data were obtained from the UK Biobank cohort. Regression analysis was first conducted to test the association of insomnia with plasma total bilirubin (TBil; n = 186,793), direct bilirubin (DBil; n = 159,854) and total protein (TP; n = 171,574) in UK Biobank cohort. Second, genome-wide gene-environment interaction study (GWGEIS) was conducted by PLINK 2.0, and FUMA platform was used to identify enriched pathway terms. In UK Biobank cohort, we found that TP (P < 2.00 × 10−16), DBil (P = 1.72 × 10−3) and TBil (P = 3.38 × 10−5) were significantly associated with insomnia. GWGEIS of both DBil and TBil observed significant G × INSOMNIA effects between insomnia and UDP Glucuronosyltransferase Family 1 (rs6431558, P = 6.26 × 10−11) gene. GWGEIS of TP also detected several significant genes interacting with insomnia, such as KLF15, (rs70940816, P = 6.77 × 10−10) and DOK7, (rs2344205, P = 1.37 × 10−9). Multiple gene ontology (GO) terms were identified for bilirubin, such as GO_URONIC_ACID_METABOLIC_PROCESS (adjusted P = 4.15 × 10−26). Our study results suggested negative associations between insomnia and DBil and TBil; and a positive association between insomnia and TP. • Plasma TP, DBil and TBil associated with insomnia. • Insomnia could affect metabolism by Gene × insomnia interactions. • UGT1A might play a role in relationship between insomnia and bilirubin metabolism. • Gene participated in lipid and bile acid metabolism showed G × E effects on TP. [ABSTRACT FROM AUTHOR]

Maternal smoking during pregnancy (MSDP) has been reported to be associated with increased anxiety and depression behaviors in offspring. However, there is still scant evidence to support the link between MSDP and anxiety/depression. Using the subjects from the UK Biobank cohort (n = 371,903–432,881). Logistic regression analyses were first conducted to test the correlation between MSDP and anxiety/depression in offspring. Second, genome-wide gene-environment interaction study (GWGEIS) analyses were conducted by PLINK, using MSDP as environmental factor. Genetic correlation analysis of anxiety/depression and smoking was conducted by the LDSC software using the published genome-wide association study (GWAS) summary data of four smoking traits (n = 337,334–1,232,091), anxiety (n = 31,880) and depression (n = 490,359). Finally, pathway enrichment analysis was carried out to detect the pathway involved in the development of offspring anxiety caused by the interaction of MSDP × SNPs. Observational analyses showed that anxiety and depression status in offspring were significantly associated with MSDP (all p < 0.0001). Further GWEGI analyses observed significant MSDP-gene interaction effects at UNC80 gene for anxiety (p = 9.09 × 10−9). LDSC did not detect significant genetic correlation between anxiety and smoking traits. Pathway analysis identified 19 significant pathways for anxiety, such as MANALO_HYPOXIA_UP (FDR = 5.50 × 10−4), REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS (FDR = 0.0304) and ONDER_CDH1_TARGETS_2_UP (FDR = 0.0371). Our study results suggested the important impact of MDSP on the risk of anxiety in offspring, partly attributing to environment-gene interactions effects. [ABSTRACT FROM AUTHOR]

Psychiatric disorders are among the largest and fastest-growing categories of the global disease burden. However, limited effort has been made to further elucidate associations between socioeconomic factors and psychiatric disorders from a genetic perspective. We randomly divided 501,882 participants in the UK Biobank cohort with socioeconomic Townsend deprivation index (TDI) data into a discovery cohort and a replication cohort. For both cohorts, we first conducted regression analyses to evaluate the associations between the TDI and common psychiatric disorders or traits, including anxiety, bipolar disorder, self-harm, and depression (based on self-reported depression and Patient Health Questionnaire scores). We then performed a genome-wide gene-by-environment interaction study using PLINK 2.0 with the TDI as an environmental factor to explore interaction effects. In the discovery cohort, significant associations were observed between the TDI and psychiatric disorders (p < 4.00 × 10−16), including anxiety (odds ratio [OR] = 1.08, 95% confidence interval [CI] = 1.07–1.10), bipolar disorder (OR = 1.42, 95% CI = 1.36–1.48), self-harm (OR = 1.21, 95% CI = 1.19–1.23), self-reported depression (OR = 1.22, 95% CI = 1.20–1.24), and Patient Health Questionnaire scores (β =.07, SE = 0.004). We observed similar significant associations in the replication cohort. In addition, multiple candidate loci were identified by the genome-wide gene-by-environment interaction study, including rs10886438 at 10q26.11 (GRK5) (p = 5.72 × 10−11) for Patient Health Questionnaire scores and rs162553 at 2p22.2 (CYP1B1) (p = 2.25 × 10−9) for self-harm. Our findings suggest the relevance of the TDI to psychiatric disorders. The genome-wide gene-by-environment interaction study identified several candidate genes interacting with the TDI, providing novel clues for understanding the biological mechanism of associations between the TDI and psychiatric disorders. [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]

Objectives: In this study designed to investigate the effect of diet and gut microbiome on neuropsychiatric disorders, we explored the mechanisms of the interaction between diet and gut microbiome on the risk of neuroticism., Methods: First, using the individual genotype data from the UK Biobank cohort (N = 306 165), we calculated the polygenic risk score (PRS) based on 814 dietary habits single nucleotide polymorphisms (SNPs), 21 diet compositions SNPs and 1001 gut microbiome SNPs, respectively. Gut microbiome and diet-associated SNPs were collected from three genome-wide association studies (GWAS), including the gut microbiome (N = 3890), diet compositions (over 235 000 subjects) and dietary habits (N = 449 210). The neuroticism score was calculated by 12 questions from the Eysenck Personality Inventory Neuroticism scale. Then, regression analysis was performed to evaluate the interaction effects between diet and the gut microbiome on the risk of neuroticism., Results: Our studies demonstrated multiple candidate interactions between diet and gut microbiome, such as protein vs. Bifidobacterium (β = 4.59 × 10-3; P = 9.45 × 10-3) and fat vs. Clostridia (β = 3.67 × 10-3; P = 3.90 × 10-2). In addition, pieces of fresh fruit per day vs. Ruminococcus (β = -5.79 × 10-3, P = 1.10 × 10-3) and pieces of dried fruit per day vs. Clostridiales (β = -5.63 × 10-3, P = 1.49 × 10-3) were found to be negatively associated with neuroticism in fruit types. We also identified several positive interactions, such as tablespoons of raw vegetables per day vs. Veillonella (β = 5.92 × 10-3, P = 9.21 × 10-4) and cooked vegetables per day vs. Acidaminococcaceae (β = 5.69 × 10-3, P = 1.24 × 10-3)., Conclusions: Our results provide novel clues for understanding the roles of diet and gut microbiome in the development of neuroticism., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Serum urate is associated with BMD and may be a protective factor. However, the exact association and mechanism are still unclear. We performed a genome-wide gene-environmental interaction study (GWGEIS) to explore the interaction effects between gene and urate on BMD, using data from the UK Biobank cohort., Methods: A total of 4575 participants for femur total BMD, 4561 participants for L1-L4 BMD, and 237799 participants for heel BMD were included in the present study. Linear regression models were used to test for associations between urate and BMD (femur total BMD, L1-L4 BMD, heel BMD) by R software. GWGEIS was conducted by PLINK 2.0 using a generalize linear model, adjusted for age, sex, weight, smoking behavior, drinking behavior, physical activity and 10 principle components for population structure., Results: Results showed that urate was positively associated with femur total BMD, L1-L4 BMD and heel BMD and similar findings were observed in both the male and female subgroups. GWGEIS identified 261 genome-wide significant (P < 5.00 × 10 -8 ) SNP × urate interaction effects for femur total BMD (rs8192585 in NOTCH4, rs116080577 in PBX1, rs9409991 in COL5A1), 17 genome-wide significant SNP × urate interaction effects for heel BMD (rs145344540 in PDE11A and rs78485379 in DKK2), 17 suggestive genome-wide SNP × urate interaction effects (P < 1.00 × 10 -5 ) for L1-L4 BMD (rs10977015 in PTPRD). We also detected genome-wide significant and suggestive SNP × urate interaction effects for BMD in both the male and female subgroups., Conclusions: This study reported several novel candidate genes, and strengthen the evidence of the interactive effects between gene and urate on the variations of BMD., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Birth weight influences not only brain development, but also mental health outcomes, including depression, but the underlying mechanism is unclear., Methods: The phenotypic data of 12,872-91,009 participants (59.18-63.38% women) from UK Biobank were included to test the associations between the birth weight, depression, and brain volumes through the linear and logistic regression models. As birth weight is highly heritable, the polygenic risk scores (PRSs) of birth weight were calculated from the UK Biobank cohort (154,539 participants, 56.90% women) to estimate the effect of birth weight-related genetic variation on the development of depression and brain volumes. Finally, the mediation analyses of step approach and mediation analysis were used to estimate the role of brain volumes in the association between birth weight and depression. All analyses were conducted sex stratified to assess sex-specific role in the associations., Result: We observed associations between birth weight and depression (odds ratio [OR] = 0.968, 95% confidence interval [CI] = 0.957-0.979, p = 2.29 × 10-6). Positive associations were observed between birth weight and brain volumes, such as gray matter (B = 0.131, p = 3.51 × 10-74) and white matter (B = 0.129, p = 1.67 × 10-74). Depression was also associated with brain volume, such as left thalamus (OR = 0.891, 95% CI = 0.850-0.933, p = 4.46 × 10-5) and right thalamus (OR = 0.884, 95% CI = 0.841-0.928, p = 2.67 × 10-5). Additionally, significant mediation effects of brain volume were found for the associations between birth weight and depression through steps approach and mediation analysis, such as gray matter (B = -0.220, p = 0.020) and right thalamus (B = -0.207, p = 0.014)., Conclusions: Our results showed the associations among birth weight, depression, and brain volumes, and the mediation effect of brain volumes also provide evidence for the sex-specific of associations.