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Smoking has caused great public health burdens in the global. Although socioeconomic factors have been confirmed to substantially influence smoking behaviors, the impact of comprehensive social deprivations on smoking behavior and related biological mechanisms are still unclear. Supported by UK Biobank cohort, we randomly divided 369,915 participants into discovery cohort (60%) and replication cohort (40%). We first tested the associations for index of multiple deprivation (IMD) and 7 IMD subdomains with ever-smoking and daily smoking frequency using regression models in both cohorts. Genome-wide by environment interaction study (GWEIS) was further conducted by using PLINK2.0 to detect the effects of the interactions between SNPs and IMD subdomains on two smoking behaviors. In discovery and replication cohorts, we observed the more deprivation status the individuals were in, the higher daily smoking frequency or risk to be ever smokers. And among the subdomains of IMD, income, education, health, living environment, and crime deprivation were positively associated with the risk of smoking behaviors, while employment deprivation was negatively correlated with smoking behaviors (all P < 0.0125). Totally, GWEIS analysis detected 23 shared significant socioeconomic-deprivation interacted loci for daily smoking frequency in two cohorts, such as rs116680599 at 2p16.3 (NRXN1, Pdiscovery = 7.68 × 10−14, Preplication = 1.96 × 10−2) and rs150451424 at 16p13.3 (RBFOX1, Pdiscovery = 1.89 × 10−10, Preplication = 1.96 × 10−2) interacting with employment deprivation. We illustrated the diverse effects of IMD on smoking behaviors and provided insights for the biological mechanisms of the interaction effects between socioeconomic deprivation with genetic factors on smoking behaviors. [ABSTRACT FROM AUTHOR]

We aim to explore the combined effects of the smoking and breastfeeding on offspring mental health outcomes. We used data from UK biobank (N = 342,846) to evaluate joint effect of breastfeeding and maternal smoke during pregnancy (MSDP) on seven adult offspring mental health outcomes (self-reported depression, depression score, self-reported anxiety, anxiety score, neuroticism score, self-harm, suicide). We stratified individuals to MSDP group and non-MSDP group as well as breastfeeding group and non-breastfeeding group. Multiple linear regression and logistic regressions analysis were performed between independent variables (MSDP or breastfeeding) and dependent variables separately (seven mental health outcomes) in each stratum. Effect estimates were expressed as β values and OR values. Sex, age, 10 principle components of population structure, smoking, alcohol use, and Townsend deprivation index were examined as covariates. At MSDP grouping level, coefficients (odds ratio [OR]) for association of breastfed as a baby with self-reported anxiety (category variable) were 0.87 (95%CI, (0.82–0.93), P = 1.74 × 10–5) in the MSDP group and 0.83 (95%CI, (0.79–0.87), P = 2.76 × 10–17) in the non-MSDP group. At breastfeeding grouping level, OR for association of MSDP and self-reported anxiety were 1.15 (95%CI, (1.10–1.20), P = 5.36 × 10–11) in breastfeeding group and 1.12(95%CI, (1.06–1.20), P = 2.02 × 10–4) in non-breastfeeding group. At MSDP grouping level, negatively associations were found for breastfeeding and anxiety score (continuable variable) in MSDP group (-0.04 SD change per SD change in MSDP, 95% CI, (− 0.06, − 0.02), P = 2.42 × 10–3) and non-MSDP group (-0.06 SD change per SD change in MSDP, 95%CI, (− 0.07, − 0.04), P = 1.70 × 10–11). At breastfeeding grouping level, positive association was found for MSDP and anxiety score in the breastfeeding group (0.07 SD change per SD change in MSDP, 95%CI, (0.06–0.09), P = 1.49 × 10–20) and non-breastfeeding group (0.07 SD change per SD change in MSDP, 95%CI, (0.05–0.09), P = 7.19 × 10–8). Compared with non-MSDP group, the protective effect (reflected by coefficients) of breastfeeding on anxiety in the MSDP decreased. Our preliminary study found MSDP may lower the protective effect of breastfeeding on the adult offspring anxiety, depression and neuroticism, providing useful recommendations for health care service via quitting smoking during pregnancy and encouraging prolonged breastfeeding. [ABSTRACT FROM AUTHOR]

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]

Cumulative evidence shows that gut microbiome can influence brain function and behavior via the inflammatory processes. However, the role of interaction between gut dysbiosis and C-reactive protein (CRP) in the development of anxiety and depression remains to be elucidated. In this study, a total of 3321 independent single nucleotide polymorphism (SNP) loci associated with gut microbiome were driven from genome-wide association study (GWAS). Using individual level genotype data from UK Biobank, we then calculated the polygenetic risk scoring (PRS) of 114 gut microbiome related traits. Moreover, regression analysis was conducted to evaluate the possible effect of interaction between gut microbiome and CRP on the risks of Patient Health Questionnaire-9 (PHQ-9) (N = 113,693) and Generalized Anxiety Disorder-7 (GAD-7) (N = 114,219). At last, 11 candidate CRP × gut microbiome interaction with suggestive significance was detected for PHQ-9 score, such as F_Ruminococcaceae (β = − 0.009, P = 2.2 × 10–3), G_Akkermansia (β = − 0.008, P = 7.60 × 10–3), F_Acidaminococcaceae (β = 0.008, P = 1.22 × 10–2), G_Holdemanella (β = − 0.007, P = 1.39 × 10–2) and O_Lactobacillales (β = 0.006, P = 1.79× 10–2). 16 candidate CRP × gut microbiome interaction with suggestive significance was detected for GAD-7 score, such as O_Bacteroidales (β = 0.010, P = 4.00× 10–4), O_Selenomonadales (β = − 0.010, P = 1.20 × 10–3), O_Clostridiales (β = 0.009, P = 2.70 × 10–3) and G_Holdemanella (β = − 0.008, P = 4.20 × 10–3). Our results support the significant effect of interaction between CRP and gut microbiome on the risks of anxiety and depression, and identified several candidate gut microbiomes for them. [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]

Comparing with the general population, the pain in depression patients has more complex biological mechanism. We aim to explore the etiological mechanism of pain in depression patients from the perspective of genetics. Utilizing the UK Biobank samples with self-reported depression status or PHQ score ≥10, we conducted genome-wide association studies (GWAS) of seven pain traits (N = 1,133–58,349). Additionally, we used FUSION pipeline to perform proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) by integrating GWAS summary data with two different proteome reference weights (ROS/MAP and Banner) and Rnaseq gene expression reference weights, respectively. GWAS identified 3 significant genes associated with different pain traits in depression patients, including TRIOBP (P GWAS = 4.48 × 10−8) for stomach or abdominal pain, SLC9A9(P GWAS = 2.77 × 10−8) for multisite chronic pain (MCP) and ADGRF1 (P GWAS = 1.51 × 10−8) for neck or shoulder pain. In addition, PWAS and TWAS analysis also identified multiple candidate genes associated with different pain traits in depression patients, such as TPRG1L (P PWAS-Banner = 3.38 × 10−2) and SIRPA (P PWAS-Banner = 3.65 × 10−2) for MCP, etc. Notably, when comparing the results of PWAS and TWAS analysis, we found overlapping candidate genes in these pain traits, such as GSTM3 (P PWAS - Banner = 3.38 × 10−2, P TWAS = 6.92 × 10−3) in the stomach or abdominal pain phenotype, ATG7 (P PWAS - Rosmap = 3.15 × 10−2, P TWAS = 2.98 × 10−2) in the MCP, etc. We identified multiple novel candidate genes for pain traits in depression patients from different perspectives of genetics, which provided novel clues for understanding the genetic mechanisms underlying the pain in depression patients. [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]

This study was aimed to evaluate the associations of adult heel bone mineral density (BMD) with birth weight and growth parameters at the age of ten years. The analysis data (97178–178,494 subjects) was derived from the UK Biobank cohort. Birth weight, comparative body size and height size at the age of ten years were determined by self-report. The heel BMD was estimated by the Quantitative Ultrasound Index through the calcaneus. Linear regression analysis was applied to test the associations of adult heel BMD with birth weight and growth parameters at the age of ten years, respectively. Age, sex, body mass index and 10 principle components (PC) of population structure were used as covariates in the regression analysis of total samples. In sex-specific analysis, age, body mass index and 10 PC were used as covariates. We observed significant associations of heel BMD with birth weight (b = −0.020, P = 1.974 × 10−13), comparative body size (b = 0.020, P = 2.539 × 10−6) and comparative height size (b = −0.020, P = 5.892 × 10−11) at the age of ten years in total samples. In females, birth weight (b = −0.040, P = 2.870 × 10−24) and comparative height size (b = −0.040, P = 2.034 × 10−20) were statistically associated with adult heel BMD. In males, comparative body size appeared to be associated with adult heel BMD (b = 0.030, P = 1.590 × 10−7). Our study results support the predictive effects of birth weight and growth parameters at the age of ten years on adult heel BMD. We also observed sex-specific association between adult heel BMD and growth parameters at the age of ten years. • Birth weight was negatively associated with adult heel BMD in total sample. • Growth parameters at age 10 were correlated with adult heel BMD in total sample. • Birth weight, comparative height size at age 10 was related to heel BMD of women. • Comparative body size at age 10 was significantly associated with heel BMD of men. [ABSTRACT FROM AUTHOR]