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Psychiatric disorders have great health hazards and the exact pathogeny remains elusive now. We aim to explore the potential interaction effects of mitochondrial function and human behavior on the risks of anxiety and depression.The genome-wide association study (GWAS) data of mitochondrial function (N = 383,476-982,072) were obtained from published studies. Individual level genotype and phenotype data of anxiety, depression and behavioral factors (including drinking, smoking and physical activity) were all from the UK Biobank (N = 84,805-85,164). We first calculated the polygenic risk scores (PRS) of mitochondrial function as the instrumental variables, and then constructed linear regression analyses to systematically explore the potential interaction effects of mitochondrial function and human behavior on anxiety and depression.In total samples, we observed mitochondrial heteroplasmy (MtHz) vs. Drinking (PConsidering all subjects were from UK Biobank, it should be careful to extrapolate our findings to other populations with different genetic background.Our results suggest the significant impacts of mitochondrial function and human behavior interactions on the development of anxiety and depression, providing new clues for clarifying the pathogenesis of anxiety and depression.

To explore the roles of rare and high-impact variants in sleep duration.Based on the recently released UK Biobank 200k exome dataset, an exome-wide association study was conducted to detect rare variants (minor allele frequency0.01) contributing to sleep duration. Variant annotations were performed by the software tool ANNOVAR. Gene-based burden tests of sleep duration were conducted by the SKAT R-package. After quality control, 137,047 subjects were included in this study. CAUSALdb database was used to explore the related mental traits of identified genes.We detected 730,572 variants with MAF1%, including 3873 frameshift variants, 3977 nonframeshift variants, 449,632 nonsynonymous variants, 1293 startloss variants, 10,254 stopgain variants, 413 stoploss variants, 261,130 synonymous variants, and 3102 variants are annotated as unknown. The burden test of exonic variants detected two exome-wide significant associations for sleep duration including TMIE at 3p21.31 (PThis study reported 2 novel candidate genes for a sleep duration, supporting the roles of rare genetic variants in the regulation of sleep duration.

Despite thousands of common genetic loci of major depression disorders (MDD) have been identified by GWAS to date, a large proportion of genetic variation predisposing to MDD remains unaccounted for. By utilizing the newly released UK Biobank 200,643 exome dataset, we conducted an exome-wide association study to identify rare risk variants contributing to MDD. After quality control, 120,033 participants with MDD polygenic risk scores (PRS) values were included. The individuals with lower 30% quantile of the PRS value were filtered for case and control selecting. Then the cases were set as the individuals with upper 10% quantile of the PHQ depression score and lower 10% quantile were set as controls. Finally, 1612 cases and 1612 controls were included in this study. The variants were annotated by ANNOVRA software. After exclusions, 34,761 qualifying variants, including 148 frameshift variant, 335 non-frameshift variant, 33,758 nonsynonymous, 91 start-loss, 393 stop-gain, 36 stop-loss variants were imported into the SKAT R-package to perform single variants, gene-based burden and robust burden tests with minor allele frequency (MAF) 0.01. Single variant association testing identified one variant, rs4057749 (P = 5.39 × 10

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.

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

We aim to explore the potential interaction effects of brain aging and gut microbiota on the risks of sleep, anxiety and depression disorders. The genome-wide association study (GWAS) datasets of brain aging (N = 21,407) and gut microbiota (N = 3,890) were obtained from published studies. Individual level genotype and phenotype data of psychiatric traits (including sleep, anxiety and depression) were all from the UK Biobank (N = 107,947-374,505). We first calculated the polygenic risk scores (PRS) of 62 brain aging modes and 114 gut microbiota taxa as the instrumental variables, and then constructed linear and logistic regression analyses to systematically explore the potential interaction effects of brain aging and gut microbiota on psychiatric disorders. We observed the interaction effects of brain aging and gut microbiota on sleep, anxiety and depression disorders, such as Putamen/caudate T2* vs. Rhodospirillales (β = -0.012, P = 8.4 × 10

Habitual coffee consumption is an addictive behavior with unknown genetic variations and has raised public health issues about its potential health-related outcomes. We performed exome-wide association studies to identify rare risk variants contributing to habitual coffee consumption utilizing the newly released UK Biobank exome dataset (n = 200,643). A total of 34,761 qualifying variants were imported into SKAT to conduct gene-based burden and robust tests with minor allele frequency

Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (pPWAS-ROS/MAP = 3.00 × 10−5) and C3orf18 (pPWAS-Banner = 7.07 × 10−31). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (pTWAS-Splicing = 3.84 × 10−2), CHMP1A (pTWAS-Rnaseq = 5.09 × 10−4), and SIRT5 (pTWAS-Splicing = 4.81 × 10−3). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (pPWAS-Banner = 4.90 × 10−2, pTWAS-Splicing = 1.23 × 10−2) in the total population and GLO1(pPWAS-Banner = 4.89 × 10−3, pTWAS-Rnaseq = 1.41 × 10−3) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD.

Psychiatric disorders and neuroticism are closely associated with central nervous system, whose proper functioning depends on efficient protein renewal. This study aims to systematically analyze the association between anxiety / depression / neuroticism and each of the 439 proteins. 47,536 pQTLs of 439 proteins in brain, plasma and cerebrospinal fluid (CSF) were collected from recent genome-wide association study. Polygenic risk scores (PRS) of the 439 proteins were then calculated using the UK Biobank cohort, including 120,729 subjects of neuroticism, 255,354 subjects of anxiety and 316,513 subjects of depression. Pearson correlation analyses were performed to evaluate the correlation between each protein and each of the mental traits by using calculated PRSs as the instrumental variables of protein. In general population, six correlations were identified in plasma and CSF such as plasma protease C1 inhibitor (C1-INH) with neuroticism score (r = − 0.011, P = 2.56 × 10− 9) in plasma, C1-INH with neuroticism score (r = -0.010, P = 3.09 × 10− 8) in CSF, and ERBB1 with self-reported depression (r = − 0.012, P = 4.65 × 10− 5) in CSF. C1-INH and ERBB1 may induce neuroticism and depression by affecting brain function and synaptic development. Gender subgroup analyses found that BST1 was correlated with neuroticism score in male CSF (r = − 0.011, P = 1.80 × 10− 5), while CNTN2 was correlated with depression score in female brain (r = − 0.013, P = 6.43 × 10− 4). BST1 and CNTN2 may be involved in nervous system metabolism and brain health. Six common candidate proteins were associated with all three traits (P

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

We aim to systematically explore the potential genetic correlations between five major psychiatric disorders and gestational ages. Genome-wide association study (GWAS) summary datasets of attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) in discovery were downloaded from the Psychiatric GWAS Consortium (PGC) website. Suggestive (Raw p 0.05) genetic associations in the discovery phrase were further replicated in independent GWASs which downloaded from PGC, the FinnGen study or Integrative Psychiatric Research (iPSYCH) website. GWASs of gestational duration, preterm and post-term birth were derived from previous studies of infants from the Early Growth Genetics (EGG) Consortium, the iPSYCH study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN). We calculated genetic correlations using linkage disequilibrium score (LDSC) regression. Mendelian randomization (MR) analyses were performed to investigate the causal effects. We identified four suggestive genetic correlations between psychiatric disorders and gestational age factors in discovery LDSC and two replicated in a confirmation LDSC: gestational duration and ADHD (r

Multiple previous studies explored the associations between early life factors and the age at onset of the depression. However, they only focused on the influence of environmental or genetic factors, without considering the interactions between them. Based on previous genome-wide association study (GWAS) data, we first calculated polygenic risk score (PRS) for depression. Regression analyses were conducted to assess the interacting effects of depression PRS and 5 early life factors, including felt hated by family member (N = 40,112), physically abused by family (N = 40,464), felt loved (N = 35633), and sexually molested (N = 41,595) in childhood and maternal smoking during pregnancy (N = 38,309), on the age at onset of the depression. Genome-wide environment interaction studies (GWEIS) were then performed to identify the genes interacting with early life factors for the age at onset of the depression. In regression analyses, we observed significant interacting effects of felt loved as a child and depression PRS on the age at onset of depression in total sample (β = 0.708, P = 5.03 × 10−3) and males (β = 1.421, P = 7.64 × 10−4). GWEIS identified a novel candidate loci interacting with felt loved as a child at GSAP (rs2068031, P = 4.24 × 10–8) and detected several genes with suggestive significance association, such as CMYA5 (rs7343, P = 2.03 × 10–6) and KIRREL3 (rs535603, P = 4.84 × 10–6) in males. Our results indicate emotional care in childhood may affect the age at onset of depression, especially in males, and GSAP plays an important role in their interaction.

Background Despite cumulative evidence showing obesity is associated with changes in sleep quality and quantity, the study about the relationships between sleep and body composition is scarce, and whether the relationship is causal remains unknown. In this study, we examined whether there are causal associations between sleep and body composition. Methods First, we estimated genetic correlations between sleep-related phenotypes and body composition using the linkage disequilibrium score regression (LDSC). Mendelian randomization (MR) analysis was then conducted to test 2-way causal relationships on phenotypes with significant genetic associations. Finally, Bayesian colocalization (COLOC) analysis was performed to calculate the posterior probability of causal variation and identify the common genes to verify the results of MR. Results For the LDSC analysis, we observed some significant genetic correlations (rG), such as snoring and right leg fat mass (rG = 0.376, P = 7.21 × 10−80). For the MR analysis, we identified some significant causal relationships, such as snoring is the causal risk factor for whole-body fat-free mass (Pweighted median = 1.28 × 10−6, PMR-PRESSO = 1.35 × 10−7), dozing is the causal risk factor for right leg fat mass (Pweighted median = 9.22 × 10−4, PMR-PRESSO = 9.55 × 10−4), and right arm fat mass (Pweighted median = 1.11 × 10−40, PMR-PRESSO = 4.93 × 10−55) is the causal risk factor for snoring. For the COLOC analysis, we identified rs143384 mapping on GDF5 and 6 overlapped single nucleotide polymorphisms (eg, rs1421085, rs11642015) mapping on FTO. Conclusion Our study identified the causal relationships between sleep-related phenotypes and body composition. These findings may give insights into the mechanism of sleep disturbances and provide novel therapeutic targets.

Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 (p = 4.11 × 10−8, LRRTM4) for self-reported depression status and rs149760119 (p = 3.88 × 10−8, GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

Yao Yao,&ast; Yumeng Jia,&ast; Yan Wen, Bolun Cheng, Shiqiang Cheng, Li Liu, Xuena Yang, Peilin Meng, Yujing Chen, Chun’e Li, Jingxi Zhang, Zhen Zhang, Chuyu Pan, Huijie Zhang, Cuiyan Wu, Xi Wang, Yujie Ning, Sen Wang, Feng Zhang Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Feng ZhangKey Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of ChinaEmail fzhxjtu@mail.xjtu.edu.cnPurpose: Most previous genetic studies of sleep behaviors were conducted individually, without comprehensive consideration of the complexity of various sleep behaviors. Our aim is to identify the genetic architecture and potential biomarker of the sleep health score, which more powerfully represents overall sleep traits.Patients and Methods: We conducted a genome-wide association study (GWAS) of sleep health score (overall assessment of sleep duration, snoring, insomnia, chronotype, and daytime dozing) using 336,463 participants from the UK Biobank. Proteome-wide association study (PWAS) and transcriptome-wide association study (TWAS) were then performed to identify candidate genes at the protein and mRNA level, respectively. We finally used linkage disequilibrium score regression (LDSC) to estimate the genetic correlations between sleep health score and other functionally relevance traits.Results: GWAS identified multiple variants near known candidate genes associated with sleep health score, such as MEIS1, FBXL13, MED20 and SMAD5. HDHD2 (PPWAS = 0.0146) and GFAP (PPWAS = 0.0236) were identified associated with sleep health score by PWAS. TWAS identified ORC4 (PTWAS = 0.0212) and ZNF732 (PTWAS = 0.0349) considering mRNA expression level. LDSC found significant genetic correlations of sleep health score with 3 sleep behaviors (including insomnia, snoring, dozing), 4 psychiatry disorders (major depressive disorder, attention deficit/hyperactivity disorder, schizophrenia, autism spectrum disorder), and 9 plasma protein (such as Stabilin-1, Stromelysin-2, Cytochrome c) (all LDSC PLDSC < 0.05).Conclusion: Our results advance the comprehensive understanding of the aetiology and genetic architecture of the sleep health score, refine the understanding of the relationship of sleep health score with other traits and diseases, and may serve as potential targets for future mechanistic studies of sleep phenotype.Keywords: sleep, sleep health score, genome-wide association study, genetics, complex-traits

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.