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The objectives of this study were to investigate the interaction of mitochondrial DNA (mtDNA) and lifestyle factors in the development of psychiatric disorders and to gain greater insight into their pathogenesis and comorbidity. We analyzed data from approximately 150,000 individuals from the UK Biobank. Mitochondrial gene-by-environment interaction studies (mtGEIS) were performed to assess the relationships between mtDNA and psychiatric disorders, such as anxiety, depression, and self-harm. These disorders were defined using diagnostic and severity indicators derived from the General Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9). Smoking and drinking behaviors were characterized based on UK Biobank criteria. For the mtGEIS, logistic and linear regression models from PLINK 2.0 were employed, accounting for covariates like age, sex, PC1-10, Townsend Deprivation Index (TDI), and educational attainment. We also conducted sex-stratified analyses to detect any gender-specific effects. Our findings highlighted significant associations between mtDNA and three psychiatric disorders. Moreover, the interplay between mtDNA and lifestyle factors showed significant associations with psychiatric disorders (all P values < 0.05). Specifically, two mutant loci, T5004C ( B Anx _ self = − 0.0026 , B Dep _ self = − 0.0024 , B Self − harm = − 0.0018) and G9123A ( B Anx _ self = − 0.0030 , B Dep _ self = − 0.0024 , B Self − harm = − 0.0017), were found to reduce the risk of three disorders after interacting with alcohol. Sex-specific differences were also observed. In summary, the expression of mitochondrial genes could be modulated by lifestyle factors like smoking and drinking, potentially affecting psychiatric disorders. These habits might influence mitochondrial respiratory chain activity and the replication and transcriptional regulation of mitochondrial genes, culminating in changes in mitochondrial functionality and subsequently psychiatric disorders. [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]

Introduction: Observational studies highlight associations of common diseases with individual schizophrenia symptoms. However, it is unclear whether these diseases are associated with individual treatment-resistant schizophrenia (TRS). We aimed to explore the genetic associations between common immune diseases, metabolic diseases, psychiatric disorders, gut microbiota and TRS. Methods: Genome-wide association study (GWAS) summary data of European participants (n = ∼456,327) included TRS, 11 psychiatric disorders, 23 immune and metabolic diseases, body mass index, height, and 211 gut microbiota. In this genetic correlation and two-sample Mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with common immune diseases, metabolic diseases, psychiatric disorders, and gut microbiota with TRS. Results: LDSC revealed candidate associations between attention deficit/hyperactivity disorder (ADHD), schizophrenia, intestinal infectious diseases, obesity and TRS (genetic correlation range, 0.230–0.702; p < 0.05). Two-sample MR analyses suggested that ADHD was positively associated with TRS (estimate [SE] = 0.204 [0.073], p = 0.005), a finding that remained stable across statistical models. Besides, schizophrenia and genus Barnesiella levels were causally associated with TRS but not consistent across MR approaches. Conclusion: This study reports genetic correlations between ADHD, schizophrenia, intestinal infectious diseases, obesity and TRS. The study also found that genus Barnesiella was associated with TRS. These findings may have clinical implications, highlighting the possible strategy for TRS prevention. [ABSTRACT FROM AUTHOR]

Objectives: The role of neurological proteins in the development of bipolar disorder (BD) and schizophrenia (SCZ) remains elusive now. The current study aims to explore the potential genetic correlations of plasma neurological proteins with BD and SCZ. Methods: By using the latest genome-wide association study (GWAS) summary data of BD and SCZ (including 41,917 BD cases, 11,260 SCZ cases, and 396,091 controls) derived from the Psychiatric GWAS Consortium website (PGC) and a recently released GWAS of neurological proteins (including 750 individuals), we performed a linkage disequilibrium score regression (LDSC) analysis to detect the potential genetic correlations between the two common psychiatric disorders and each of the 92 neurological proteins. Two-sample Mendelian randomisation (MR) analysis was then applied to assess the bidirectional causal relationship between the neurological proteins identified by LDSC, BD and SCZ. Results: LDSC analysis identified one neurological protein, NEP , which shows suggestive genetic correlation signals for both BD (coefficient = −0.165, p value = 0.035) and SCZ (coefficient = −0.235, p value = 0.020). However, those association did not remain significant after strict Bonferroni correction. Two sample MR analysis found that there was an association between genetically predicted level of NEP protein, BD (odd ratio [OR] = 0.87, p value = 1.61 × 10−6) and SCZ (OR = 0.90, p value = 4.04 × 10−6). However, in the opposite direction, there is no genetically predicted association between BD, SCZ, and NEP protein level. Conclusion: This study provided novel clues for understanding the genetic effects of neurological proteins on BD and SCZ. [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]

Psychiatric disorders are a group of complex psychological syndromes whose etiology remains unknown. Previous study suggested that various chemicals contributed to the development of psychiatric diseases through affecting gene expression. This study aims to systematically explore the potential relationships between 5 major psychiatric disorders and more than 11 000 chemicals. The genome-wide association studies (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depression disorder (MDD), and schizophrenia (SCZ) were driven from the Psychiatric GWAS Consortium and iPSYCH website. The chemicals related gene sets were obtained from the comparative toxicogenomics database (CTD). First, transcriptome-wide association studies (TWAS) were performed by FUSION to calculate the expression association testing statistics utilizing GWAS summary statistics of the 5 common psychiatric disorders. Chemical-related gene set enrichment analysis (GSEA) was then conducted to explore the relationships between chemicals and each of the psychiatric diseases. We observed several significant correlations between chemicals and each of the psychiatric disorders. We also detected common chemicals between every 4 of the 5 major psychiatric disorders, such as androgen antagonists for ADHD (P value =.0098), ASD (P value =.0330), BD (P value =.0238), and SCZ (P value =.0062), and imipramine for ADHD (P value =.0054), ASD (P value =.0386), MDD (P value =.0438), and SCZ (P value =.0008). Our study results provide new clues for revealing the roles of environmental chemicals in the development of psychiatric disorders. [ABSTRACT FROM AUTHOR]

Psychiatric disorders are a group of complex psychological syndromes with high prevalence. It has been reported that gut microbiota has a dominant influence on the risks of psychiatric disorders through gut microbiota–brain axis. We extended the classic gene set enrichment analysis (GSEA) approach to detect the association between gut microbiota and complex diseases using published genome-wide association study (GWAS) and GWAS of gut microbiota summary data. We applied our approach to real GWAS data sets of five psychiatric disorders, including attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD). To evaluate the performance of our approach, we also tested the genetic correlations of obesity and type 2 diabetes with gut microbiota. We identified several significant associations between psychiatric disorders and gut microbiota, such as ADHD and genus Desulfovibrio (P  = 0.031), order Clostridiales (P  = 0.034). For AUT, association signals were observed for genera Bacteroides (P  = 0.012) and Desulfovibrio (P  = 0.033). Genus Desulfovibrio (P  = 0.005) appeared to be associated with BD. For MDD, association signals were observed for genus Desulfovibrio (P  = 0.003), order Clostridiales (P  = 0.004), family Lachnospiraceae (P  = 0.007) and genus Bacteroides (P  = 0.007). Genus Desulfovibrio (P  = 0.012) and genus Bacteroides (P  = 0.038) appeared to be associated with SCZ. Our study results provide novel clues for revealing the roles of gut microbiota in psychiatric disorders. This study also illustrated the good performance of GSEA approach for exploring the relationships between gut microbiota and complex diseases. [ABSTRACT FROM AUTHOR]

Handedness is an elusive human behavioral phenotypes and the genetic basis of it remains unclear until now. The aim of this study is to evaluate the genetic correlations between left-handedness and multiple mental disorders, and explored the genes detected by genetic correlations. In this study, linkage disequilibrium score regression (LDSC) analysis was conducted to evaluate the genetic correlations between left-handedness and multiple mental disorders. The significant genetic correlation was only observed between left-handedness and schizophrenia (SCZ). For the observed genetic correlation, transcriptome-wide association study (TWAS) was performed to identify the genes associated with left-handedness and SCZ, including brain RNA-seq (CBR) and brain RNA-seq splicing (CBRS). We detected several common genes associated with both left-handedness and SCZ, such as YWHAH, MAPT and ANO10. The common genes shared by left-handedness and SCZ were subjected to gene set enrichment analysis. Our study provides a novel clue for understanding the genetic correlation between left-handedness and SCZ. [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]

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]

• 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]