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Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74–96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development. acceptedVersion

Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR

A new rapid ultra-high performance liquid chromatography coupled with linear trap quadrupole orbitrap mass spectrometry method was established for the qualitative analysis of absorbed ingredients and metabolites of Zhimu-Huangbai herb pair, which is used to treat type 2 diabetes mellitus. A total of 16 absorbed ingredients and 11 metabolites were identified in normal and type 2 diabetes mellitus rats, respectively. Such findings indicated that the diabetic model had no effect on the type of components in plasma. Seven absorbed ingredients and 11 metabolites were first identified after the oral administration of Zhimu-Huangbai herb pair. Thereafter, ultra-high performance liquid chromatography coupled with linear trap quadrupole orbitrap mass spectrometry and liquid chromatography-API4000+ triple quadrupole mass spectrometer methods were established and validated for pharmacokinetic comparative studies of seven major bioactive components in normal and type 2 diabetes mellitus rats. Partial pharmacokinetic parameters in the plasma of type 2 diabetes mellitus rats were significantly different from those in normal rats. To our knowledge, this is the first comparison of absorbed ingredients and metabolites of Zhimu-Huangbai herb pair, and its used in pharmacokinetic studies between normal and type 2 diabetes mellitus rats. Ultimately, our findings provide insights into the clinical usage of Zhimu-Huangbai herb pair. This article is protected by copyright. All rights reserved.

Background and aim Schizophrenia (SCZ) and bipolar disorder (BD) have a high comorbidity of alcohol use disorder (AUD), and both comorbid AUD and excessive alcohol consumption (AC) have been linked to greater illness severity. We aimed to identify genomic loci jointly associated with SCZ, BD, AUD and AC to gain further insights into their shared genetic architecture. Design We analysed summary data (P values and Z scores) from genome-wide association studies (GWAS) using conjunctional false discovery rate (conjFDR) analysis, which increases power to discover shared genomic loci. We functionally characterized the identified loci using publicly available biological resources. Setting AUD and AC data provided by the Million Veteran Program, derived from the United States Department of Veterans Affairs Healthcare System. SCZ and BD data provided by the Psychiatric Genomics Consortium, based on cohorts from countries in Europe, North America and Australia. Participants AUD (34 658 cases, 167 346 controls), AC (n = 200 680), SCZ (31 013 cases and 38 918 controls), BD (20 352 cases and 31 358 controls). All participants were of European ancestry. Measurements Genomic loci shared between alcohol traits, SCZ and BD at conjFDR

MotivationGenome-wide association studies (GWAS) have been successful in identifying genetic variants associated with a particular phenotype. However, many complex phenotypes are influenced by multiple genetic variants with small effects. Detecting the genetic pleiotropy can provide insights into biological mechanisms influencing complex human phenotypes. The recently developed Multivariate Omnibus Statistical Test (MOSTest) has proven to be efficient and powerful, suited for complex large-scale data. The method substantially increased discovery of genetic variants associated with brain MRI phenotypes in the UK Biobank compared to conventionally use multivariate approach. Here we extend the MOSTest to meta-analysis (Meta-MOSTest), facilitating data analysis of multiple phenotypes across multiple cohorts. We evaluated our updated approach in the UK Biobank using brain MRI phenotypes, by comparing the discovery yield of the single-cohort MOSTest versus Meta-MOSTest through simulating sub-cohorts of different sample sizes from 265 to 26501 subjects.ResultsOur method works efficiently on large-scale cohorts with a large number of MRI phenotypes. We found that lower per-cohort sample sizes resulted in a reduced discovery yield indicating a loss of statistical power. However, with a minimum sample size of 250 subjects across cohorts, Meta-MOSTest was equivalent to MOSTest on discovery yield while maintaining a well-calibrated type I error and equivalent statistical power. We conclude that Meta-MOSTest is a useful tool for multivariate analysis across separate brain imaging genetics cohorts.Availability and implementationAll codes are freely available on GitHub: MOSTest and Meta-MOSTest.

BackgroundThe Norwegian Mother, Father, and Child Cohort Study (MoBa) is a population-based pregnancy cohort, which includes approximately 114,500 children, 95,200 mothers, and 75,200 fathers.Genotyping of MoBa has been conducted through multiple research projects, spanning several years; using varying selection criteria, genotyping arrays, and genotyping centres. MoBa contains numerous interrelated families, which necessitated the implementation of a family-based quality control (QC) pipeline that verifies and accounts for diverse types of relatedness.MethodsThe MoBaPsychGen pipeline, comprising pre-imputation QC, phasing, imputation, and post-imputation QC, was developed based on current best-practice protocols and implemented to account for the complex structure of the MoBa genotype data. The pipeline includes QC on both single nucleotide polymorphism (SNP) and individual level. Phasing and imputation were performed using the publicly available Haplotype Reference Consortium release 1.1 panel as a reference. Information from the Medical Birth Registry of Norway and MoBa questionnaires were used to identify biological sex, year of birth, reported parent-offspring (PO) relationships, and multiple births (only available in the offspring generation).ResultsIn total, 207,569 unique individuals (90% of the unique individuals included in the study) and 6,981,748 SNPs passed the MoBaPsychGen pipeline. The relatedness checks performed throughout the pipeline allowed identification of within-generation and across-generation first-degree, second-degree, and third-degree relatives. The individuals passing post-imputation QC comprised 64,471 families ranging in size from singletons to 84 unique individuals (singletons are included as families as other family members may not have been genotyped, imputed, or passed post-imputation QC). The relationships identified include 287 monozygotic twin pairs, 22,884 full siblings, 117,004 PO pairs, 23,299 second-degree relative pairs, and 10,828 third-degree relative pairs.DiscussionMoBa contains a highly complex relatedness structure, with a variety of family structures including singletons, PO duos, full (mother, father, child) PO trios, nuclear families, blended families, and extended families. The availability of robustly quality-controlled genetic data for such a large cohort with a unique extended family structure will allow many novel research questions to be addressed. Furthermore, the MoBaPsychGen pipeline has potential utility in similar cohorts.

Objective Autism Spectrum Disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. Methods We included data from genome-wide association studies (GWAS) of INT (n = 269,867), EDU (n = 766,345) and ASD (cases n = 18,381, controls n = 27,969). We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, and conditional and conjunctional false discovery rate (cond/conjFDR) to identify specific overlapping loci. Results The MiXeR indicated 12.7k genetic variants associated with ASD, with 12.0k shared with EDU and 11.1k shared with INT (Dice: 0.90–0.91), with both positive and negative relationships within overlapping variants. The majority (59%-68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU (rg=0.21, p = 2e-13) and INT (rg=0.22, p = 4e-12). We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR

Alcohol use disorder (AUD) is a pervasive and devastating mental illness with high comorbidity rates with other mental disorders. We aimed to characterize the sharedvs. unique polygenicity of AUD, alcohol consumption (AC) and mood instability (MOOD), a relevant transdiagnostic factor, using large genome-wide association studies (GWASs) data. We hypothesize that cross-analyzing these phenotypes would shed light on their unique and shared polygenicity, increase our knowledge regarding the genetic basis of the comorbidity between AUD and mood disorders, and boost discovery for jointly-associated loci. Summary statistics for MOOD, AC and AUD GWASs (Ns =363,705; 200,680 and 200,004; respectively) were analysed to characterize the cross-phenotype associations between MOOD and AC, MOOD and AUD and AC and AUD, respectively. To do so, we used a newly-established pipeline that combines (i) the bivariate causal mixture model (MiXeR) to quantify the cross-phenotype polygenic overlap and (ii) the conjunctional false discovery rate (conjFDR) to discover specific jointly-associated genomic loci. These loci were functionally characterized and mapped to genes and biological functions. We also performed validation in independent samples and phenotypic analyses. MOOD was highly polygenic (10,400 single nucleotide polymorphisms, SNPs) compared to AC and AUD (4,900 SNPs, SD =600 and 4,300 SNPs, SD =2,000; respectively). The polygenic overlap of MOOD and AC was much larger than that of MOOD and AUD (98%vs. 49%) and genetic correlation was opposite (−0.2vs. 0.23), which was confirmed in independent samples. MOOD&AUD causal SNPs were significantly enriched for brain genes, conversely to MOOD&AC. Among 38 loci identified in the joint analysis, sixteen were novel for MOOD, AC and AUD. Similarly distinct patterns were evidenced for SNP localization, function and previous GWAS associations outside of the phenotypes that were currently studied. MOOD, AC and AUD were also strongly associated at the phenotypic level. Overall, using multilevel polygenic quantification, joint loci discovery and functional annotation methods, we evidenced that the polygenic overlap between MOOD and AC/AUD implicated shared biological underpinnings but clearly distinct functional patterns between MOOD&AC and MOOD&AUD. Using the MOOD endophenotype, the current study suggests new mechanisms for the comorbidity of AUD with mood disorders.

Personality and cognition are heritable mental traits, and their genetic determinants may be distributed across interconnected brain functions. However, previous studies have employed univariate approaches which reduce complex traits to summary measures. We applied the “pleiotropy-informed” multivariate omnibus statistical test (MOSTest) to genome-wide association studies (GWAS) of 35 item and task-level measures of neuroticism and cognition from the UK Biobank (n=336,993). We identified 431 significant genetic loci and found evidence of abundant pleiotropy across personality and cognitive domains. Functional characterisation implicated genes with significant tissue-specific expression in all tested brain tissues and enriched in brain-specific gene-sets. We conditioned independent GWAS of the Big 5 personality traits and cognition on our multivariate findings, which boosted genetic discovery in other personality traits and improved polygenic prediction. These findings advance our understanding of the polygenic architecture of complex mental traits, indicating a prominence of pleiotropic genetic effects across higher-order domains of mental function.Graphical abstract

Frontline power grid workers are always facing plenty of stressors such as aerial work and high job demands, which may lead them to be less satisfied with their job. Therefore, this study aims to investigate frontline power grid workers’ job satisfaction (JS) and explore how it can be improved by its relationship with personality traits and work–family support (WFS). Data from 535 frontline power grid workers were collected from two power supply bureaus in Guangdong Province, China. Structural equation modeling (SEM) was adopted to examine the structural relationship between personality traits taken as independent variables, JS as dependent variable, and WFS as mediator. The bootstrap method was used to test the significance of indirect effects. Results suggested the overall job satisfaction of our sample is 3.34 ± 0.55 on a scale ranging from 1 to 5, and significantly correlated with personality traits and WFS. Moreover, the results of SEM and bootstrap indicated that WFS partially mediates the effect of neuroticism on JS and fully mediates the effect of conscientiousness and extraversion on JS. These findings shed light on how personality traits and environmental factors jointly impact JS and highlight the important role of WFS among frontline power grid workers.

BACKGROUND Emerging studies have shown the effectiveness of mobile health (mHealth) interventions in reducing depressive symptoms among people living with HIV. Most of these studies included only short-term follow-up, with limited data on long-term effects. OBJECTIVE The purpose of this study is to assess the long-term effects of a randomized controlled trial called Run4Love on depressive symptoms among people living with HIV at 1-year and 3-year follow-ups. METHODS A total of 300 people living with HIV with depressive symptoms were recruited and randomized to an intervention or a control group in Guangzhou, China, from September 2017 to January 2018. The intervention group received a 3-month Run4Love program, including adapted evidence-based cognitive behavioral stress management courses and exercise promotion via WeChat (Tencent), a popular social media app. The control group received usual care and a brochure on nutrition. The primary outcome was reduction in depressive symptoms, measured using the Center for Epidemiological Studies–Depression (CES-D) scale. Data used in this study were collected at baseline and at the 1-year and 3-year follow-ups. Generalized estimating equations were used to examine the group differences at 1-year and 3-year follow-ups. RESULTS Approximately half of the participants completed the assessment at 1-year (149/300, 49.7%) and 3-year (177/300, 59%) follow-ups. At 1-year follow-up, participants in the intervention group reported significant reduction in depressive symptoms compared with the control group (CES-D: from 23.9 to 18.1 in the intervention group vs from 24.3 to 23.3 in the control group; mean −4.79, SD 13.56; 95% CI −7.78 to −1.81; P=.002). At 3-year follow-up, between-group difference in CES-D remained statistically significant (from 23.9 to 20.5 in the intervention group vs from 24.3 to 24.4 in the control group; mean −3.63, SD 13.35; 95% CI −6.71 to −0.54; P=.02). No adverse events were reported during the 3-year follow-up period. CONCLUSIONS The mHealth intervention, Run4Love, significantly reduced depressive symptoms among people living with HIV, and the intervention effects were sustained at 1-year and 3-year follow-ups. Further research is needed to explore the mechanisms of the long-term effects of mHealth interventions such as Run4Love and to implement these effective interventions among people living with HIV. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://trialsearch.who.int/Trial2.aspx?TrialID=ChiCTR-IPR-17012606 INTERNATIONAL REGISTERED REPORT RR2-10.2196/10274

Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg = 0.10–0.62). Of 10.4 K genomic variants influencing MOOD, 4 K–9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, “synapse organization.” The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder. publishedVersion

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N = 35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study (R). This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.