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The metaverse gradually evolves into a virtual world containing a series of interconnected sub-metaverses. Diverse digital resources, including identities, contents, services, and supporting data, are key components of the sub-metaverse. Therefore, a Domain Name System (DNS)-like system is necessary for efficient management and resolution. However, the legacy DNS was designed with security vulnerabilities and trust risks due to centralized issues. Blockchain is used to mitigate these concerns due to its decentralized features. Additionally, it supports identity management as a default feature, making it a natural fit for the metaverse. While there are several DNS alternatives based on the blockchain, they either manage only a single type of identifiers or isolate identities from other sorts of identifiers, making it difficult for sub-metaverses to coexist and connect with each other. This article proposes a Multi-Identifier management and resolution System (MIS) in the metaverse, supporting the registration, resolution, and inter-translation functions. The basic MIS is portrayed as a four-tier architecture on a consortium blockchain due to its manageability, enhanced security, and efficiency properties. On-chain data is lightweight and compressed to save on storage while accelerating reading and writing operations. The resource data is encrypted based on the attributes of the sub-metaverse in the storage tier for privacy protection and access control. For users with decentralization priorities, a modification named EMIS is built on top of Ethereum. Finally, MIS is implemented on two testbeds and is available online as the open-source system. The first testbed consists of 4 physical servers located in the UK and Malaysia while the second is made up of 200 virtual machines (VMs) spread over 26 countries across all 5 continents on Google Cloud. Experiments indicate that MIS provides efficient reading and writing performance than the legacy DNS and other public blockchain-based workarounds including EMIS and Ethereum Name Service (ENS). [ABSTRACT FROM AUTHOR]

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]

Background: Limited efforts have been paid to explore the underlying genetic mechanisms of birth by caesarian section (CS) affecting the risks of adult anxiety and self-harm. Methods: Using UK Biobank cohort, the logistic regression model was first applied to evaluate the associations of adult anxiety and self-harm with birth by CS. Using birth by CS as exposure variables, genome-wide by environment interaction study (GWEIS) was then applied by PLINK2.0 to identify associated genes interacting with birth by CS for anxiety and self-harm. Results: In the observational study, significant associations were observed between birth by CS and anxiety (odds ratio (OR) = 1.24; 95% confidence interval (CI), 1.12–1.38; P = 4.86 × 10− 5), and self-harm (OR = 1.12; 95% CI, 1.01–1.24; P = 2.90 × 10− 2). GWEIS revealed multiple suggestive genes interacted with birth by CS for anxiety, such as DKK2 (rs13137764, P = 1.24 × 10− 9, adjusted P = 2.68 × 10− 7) and ATXN1 (rs62389045, P = 4.38 × 10− 8, adjusted P = 3.55 × 10− 6). For self-harm, significant gene-environment interactions of birth by CS on self-harm were detected, such as ALDH1A2 (rs77828167, P = 1.62 × 10− 8; rs116899929, P = 1.92 × 10− 8) and DAB1 (rs116124269, P = 3.20 × 10− 8; rs191070006, P = 3.63 × 10− 8). Conclusions: Our results suggested that birth by CS was associated with the risk of adult anxiety and self-harm. We also discovered some genes interacted with birth by CS might influence the risk of anxiety and self-harm, which may provide novel clues for the pathogenesis of those mental disorders. [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]

Despite considerable variation in disease manifestations observed among coronavirus disease 2019 (COVID‐19) patients infected with severe acute respiratory syndrome coronavirus 2, the risk factors predicting disease severity remain elusive. Recent studies suggest that peripheral blood cells play a pivotal role in COVID‐19 pathogenesis. Here, we applied two‐sample Mendelian randomization (MR) analyses to evaluate the potential causal contributions of blood cell indices variation to COVID‐19 severity, using single‐nucleotide polymorphisms (SNPs) as instrumental variables for 17 indices from the UK Biobank and INTERVAL genome‐wide association studies (N = 173 480). Data on the associations between the SNPs and very severe respiratory confirmed COVID‐19 were obtained from the COVID‐19 host genetics initiative (N = 8779/1 001 875). We observed significant negative association between hematocrit (HCT; odds ratio, OR = 0.775, 95% confidence interval, CI = 0.635–0.915, p = 3.48E−04) or red blood cell count (OR = 0.830, 95% CI = 0.728–0.932, p = 2.19E−03) and very severe respiratory confirmed COVID‐19, as well as nominal negative association of hemoglobin concentration (OR = 0.808, 95% CI = 0.673–0.943, p = 3.95E−03) with very severe respiratory confirmed COVID‐19 (no effect survived multiple correction). In conclusion, the MR study supports a protective effect of high HCT and red blood cell count from very severe respiratory confirmed COVID‐19, suggesting potential strategies to ameliorate/treat clinical conditions in very severe respiratory confirmed COVID‐19. [ABSTRACT FROM AUTHOR]

Limited efforts have been invested in exploring the interaction effects between genetic factors and gut microbiota on neuroticism and general happiness. The polygenic risk scores (PRS) of gut microbiota were calculated from individual-level genotype data of the UK Biobank cohort. Linear regression models were then used to assess the associations between individual PRS of gut microbiota and mental traits and interaction analysis was performed by PLINK2.0. KOBAS-i was used to conduct gene ontology (GO) enrichment analysis of the identified genes. We observed suggestive significant associations between neuroticism and PRS for the genus Bifidobacterium (rank-normal transformation, RNT) (beta = −1.10, P = 4.16 × 10−3) and the genus Desulfovibrio (RNT) (beta = 0.54, P = 7.46 × 10−3). PRS for the genus Bifidobacterium (hurdle binary, HB) (beta = 1.99, P = 5.24 × 10−3) and the genus Clostridium (RNT) (beta = 1.26, P = 9.27 × 10−3) were found to be suggestive positively associated with general happiness. Interaction analysis identified several significant genes that interacted with gut microbiota, such as RORA (rs575949009, beta = −45.00, P = 1.82 × 10−9) for neuroticism and ASTN2 (rs36005728, beta = 19.15, P = 3.37 × 10−8) for general happiness. Our study results support the genetic effects of gut microbiota on the development of neuroticism and general happiness. [ABSTRACT FROM AUTHOR]

Background Depression is a debilitating mental disorder that often coexists with anxiety. The genetic mechanisms of depression and anxiety have considerable overlap, and studying depression in non-anxiety samples could help to discover novel gene. We assess the genetic variation of depression in non-anxiety samples, using genome-wide association studies (GWAS) and linkage disequilibrium score regression (LDSC). Methods The GWAS of depression score and self-reported depression were conducted using the UK Biobank samples, comprising 99,178 non-anxiety participants with anxiety score <5 and 86,503 non-anxiety participants without self-reported anxiety, respectively. Replication analysis was then performed using two large-scale GWAS summary data of depression from Psychiatric Genomics Consortium (PGC). LDSC was finally used to evaluate genetic correlations with 855 health-related traits based on the primary GWAS. Results Two genome-wide significant loci for non-anxiety depression were identified: rs139702470 (p = 1.54 × 10−8, OR = 0.29) locate in PIEZO2, and rs6046722 (p = 2.52 × 10−8, OR = 1.09) locate in CFAP61. These associated genes were replicated in two GWAS of depression from PGC, such as rs1040582 (preplication GWAS1 = 0.02, preplication GWAS2 = 2.71 × 10−3) in CFAP61, and rs11661122 (preplication GWAS1 = 8.16 × 10−3, preplication GWAS2 = 8.08 × 10−3) in PIEZO2. LDSC identified 19 traits genetically associated with non-anxiety depression (p < 0.001), such as marital separation/divorce (rg = 0.45, SE = 0.15). Conclusions Our findings provide novel clues for understanding of the complex genetic architecture of depression. [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]

The relationships between gut microbiota and brain-related diseases/traits remains not fully understood. A two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits, and evaluate the potential sex specific effects of gut microbiota. In discovery stage, we systematically scanned the relationships between 515 brain-related diseases/traits and gut microbiota through two-sample Mendelian randomisation analysis. Using ∼500,000 individuals derived from the UK Biobank, polygenetic risk scoring (PRS) analysis was performed to validate the associations detected in discovery stage. To evaluate the potential sex-specific effect of gut microbiota on brain-related disorders, PRS analysis was conducted in female and male, respectively. After systematically scanning diseases or traits, 41 of the 515 brain-related diseases/traits were identified to be associated with gut microbiota, such as Neuroticism score (P2-MR = 0.0018), worrier/anxious feelings (P2-MR = 0.0013), Suffer from 'nerves' (P2-MR = 0.0062) and Nervous feelings (P2-MR = 0.0158). 5 of 41 brain-related diseases or traits were successfully validated in UK Biobank, such as Neuroticism score (PUK = 0.0024, PUK-female = 0.0063, PUK-male = 0.1142), Nervous feelings (PUK = 0.0043, PUK-female = 0.0115, PUK-male = 0.1670) and Worrier/anxious feelings (PUK = 0.0166, PUK-female = 0.0196, PUK-male = 0.2930). Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males. A two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits. Using the individuals derived from the UK Biobank, polygenetic risk scoring analysis was performed to validate the associations detected in the discovery stage. Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males. [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]

Limited studies have been conducted to explore the interaction effects of social environmental and genetic factors on the risks of common psychiatric disorders. 56,613–106,695 individuals were collected from the UK Biobank cohort. Logistic or liner regression models were first used to evaluate the associations of index of multiple deprivation (IMD) with bipolar disorder (BD), depression and anxiety in UK Biobank cohort. Then, for the significant IMD associated with BD, depression and anxiety, genome-wide gene-environment interaction study (GWEIS) was performed by PLINK 2.0. Totally, the higher levels of IMD were significantly associated with higher risks of BD, depression and anxiety. For BD, GWEIS identified multiple significant SNPs interacting with IMD, such as rs75182167 for income and rs111841503 for education. For depression and anxiety, GWEIS found significant SNPs interacting with income and education, such as rs147013419 for income and rs142366753 for education. Social environmental deprivations contributed to the risks of psychiatric disorders. Besides, we reported multiple candidate genetic loci interacting with IMD, providing novel insights into the biological mechanism. • The higher levels of IMD were significantly associated with higher risks of BD, depression and anxiety. • GWEIS identified multiple significant SNPs interacting with IMD, such as rs75182167, rs111841503 and rs12257534. • For depression and anxiety, GWEIS found significant SNPs interacting with education, such as rs142366753 and rs35610560. [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]

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.)

Previous studies showed that diet and gut microbiota had a correlation with sleep. However, the potential interaction effects of diet and gut microbiota on sleep are still unclear. The phenotypic data of insomnia (including 374,505 subjects) and sleep duration (including 372,805 subjects) were obtained from the UK Biobank cohort. The Single Nucleotide Polymorphisms (SNPs) associated with 114 gut microbiota, 84 dietary habits, and 4 dietary compositions were derived from the published Genome-wide Association Study (GWAS). We used Linkage Disequilibrium Score Regression (LDSC) to estimate the genetic correlation and colocalization analysis to assess whether dietary habits and insomnia/sleep duration shared a causal variant in a region of the genome. Using UK Biobank genotype data, the polygenetic risk score of gut microbiota, dietary habits, and dietary compositions were calculated for each subject. Logistic regression and linear regression models were used to assess the potential effects of diet-gut microbiota interactions on sleep phenotypes, including insomnia and sleep duration. Insomnia and sleep duration were used as dependent variables, and sex, age, the Townsend Deprivation Index scores, and smoking and drinking habits were selected as covariates in the regression analysis. All statistical analyses were conducted using R-3.5.1 software. Significant genetic correlations were discovered between insomnia/sleep duration and dietary habits. Further, we found several significant dietary compositions-gut microbiota interactions associated with sleep, such as fat × G&#95;Collinsella&#95;RNT ( p = 1.843 × 10 - 2 ) and protein × G&#95;Collinsella&#95;HB ( p = 7.11 × 10 - 3 ). Besides, multiple dietary habits-gut microbiota interactions were identified for sleep, such as overall beef intake × G&#95;Desulfovibrio&#95;RNT ( p = 3.26 × 10 -4 ), cups of coffee per day × G&#95;Escherichia&#95;Shigella&#95;RNT ( p = 1.14 × 10 -3 ), and pieces of dried fruit per day × G&#95;Bifidobacterium&#95;RNT ( p = 5.80 × 10 -3 ). This study reported multiple diet-gut microbiota interactions associated with sleep, which may provide insights into the biological mechanisms of diet and gut microbiota affecting sleep.

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.)

We aimed to explore the underlying genetic mechanisms of traumatic events during childhood affecting the risks of adult substance use in present study. Using UK Biobank cohort, linear regression model was first applied to assess the relationships between cigarette smoking and alcohol drinking in adults with traumatic events during childhood, including felt hated by family member (41,648-111,465), felt loved (46,394-124,481) and sexually molested (47,598-127,766). Using traumatic events as exposure variables, genome-wide by environment interaction study was then performed by PLINK 2.0 to identify cigarette smoking and alcohol drinking associated genes interacting with traumatic events during childhood. We found that the frequency of cigarette smoking was significantly associated with felt hated by family member (coefficient = 0.42, P < 1.0 × 10 -9 ), felt loved (coefficient = -0.31, P < 1.0 × 10 -9 ) and sexually molested (coefficient = 0.46, P < 1.0 × 10 -9 ). We also observed weaker associations of alcohol drinking with felt hated by family member (coefficient = 0.08, P = 3.10 × 10 -6 ) and felt loved (coefficient = -0.06, P = 3.15 × 10 -7 ). GWEIS identified multiple candidate loci interacting with traumatic events, such as CTNNA3 (rs189142060, P = 4.23 × 10 -8 ) between felt hated by family member and the frequency of cigarette smoking, GABRG3 (rs117020886, P = 2.77 × 10 -8 ) between felt hated by family member and the frequency of alcohol drinking. Our results suggested the significant impact of traumatic events during childhood on the risk of cigarette smoking and alcohol drinking., (© 2021. The Author(s).)

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.