Association between birth by caesarian section and anxiety, self-harm: a gene-environment interaction study using UK Biobank data.

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.

Identifying psychiatric disorder-associated gut microbiota using microbiota-related gene set enrichment analysis.

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.

Associations between electronic devices use and common mental traits: A gene-environment interaction model using the UK Biobank data.

BACKGROUND: Electronic devices use has been reported to be associated with depression. However, limited effort has been provided to elucidate the associations between electronic devices use and mental traits in interaction with genetic factors. METHODS: We first conducted an observational study consisting of 138 976-383 742 participants for TV watching, 29 636-38 599 participants for computer using and 118 61-330 985 participants for computer playing in the UK Biobank cohort. A linear regression model was used to evaluate the associations between common mental traits and electronic devices use. Subsequently, a genome-wide gene-environment interaction study (GWEIS) was performed by PLINK2.0 to estimate the interaction effects of genes and electronic devices use on the risks of the four mental traits. RESULTS: In the UK Biobank cohort, significant associations were observed between electronic devices use and mental traits (all P < 1.0 × 10-9 ), including depression score (B = 0.094 for TV watching), anxiety score (B = 0.051 for TV watching), cigarette smoking (B = 0.046 for computer using) and alcohol drinking (B = 0.010 for computer playing). GWEIS identified multiple mental traits associated loci, interacting with electronic devices use, such as DCDC2 (rs115986722, P = 4.10 × 10-10 ) for anxiety score and TV watching, PRKCE (rs56181965, P = 9.64 × 10-10 ) for smoking and computer using and FRMD4A (rs56227933, P = 7.42 × 10-11 ) for depression score and computer playing. CONCLUSIONS: Our findings suggested that electronic devices use was associated with common mental traits and provided new clues for understanding genetic architecture of mental traits.

A Large-Scale Genetic Correlation Scan Between Intelligence and Brain Imaging Phenotypes.

Limited efforts have been paid to evaluate the potential relationships between structural and functional brain imaging and intelligence until now. We performed a two-stage analysis to systematically explore the relationships between 3144 brain image-derived phenotypes (IDPs) and intelligence. First, by integrating genome-wide association studies (GWAS) summaries data of brain IDPs and two GWAS summary datasets of intelligence, we systematically scanned the relationship between each of the 3144 brain IDPs and intelligence through linkage disequilibrium score regression (LDSC) analysis. Second, using the individual-level genotype and intelligence data of 160 124 subjects derived from UK Biobank datasets, polygenetic risk scoring (PRS) analysis was performed to replicate the common significant associations of the first stage. In the first stage, LDSC identified 6 and 2 significant brain IDPs significantly associated with intelligence dataset1 and dataset2, respectively. It is interesting that NET100_0624 showed genetic correlations with intelligence in the two datasets of intelligence. After adjusted for age and sex as the covariates, NET100_0624 (P = 5.26 × 10-20, Pearson correlation coefficients = -0.02) appeared to be associated with intelligence by PRS analysis of UK Biobank samples. Our findings may help to understand the genetic mechanisms of the effects of brain structure and function on the development of intelligence.

Genetic Correlation Analysis and Transcriptome-wide Association Study Suggest the Overlapped Genetic Mechanism between Gout and Attention-deficit Hyperactivity Disorder.

OBJECTIVES: Gout is a common inflammatory arthritis, which is caused by hyperuricemia. Limited efforts have been paid to systematically explore the relationships between gout and common psychiatric disorders. METHODS: Genome-wide association study summary data of gout were obtained from the GeneATLAS, which contained 452,264 participants including 3,528 gout cases. Linkage disequilibrium score regression (LDSC) was first conducted to evaluate the genetic relationships between gout and 5 common psychiatric disorders. Transcriptome-wide association studies (TWAS) was then conducted to explore the potential biological mechanism underlying the observed genetic correlation between gout and attention-deficit hyperactivity disorder (ADHD). The Database for Annotation, Visualization and Integrated Discovery online functional annotation system was applied for pathway enrichment analysis and gene ontology enrichment analysis. RESULTS: LDSC analysis observed significant genetic correlation between gout and ADHD (genetic correlation coefficients = 0.29, standard error = 0.09 and P value = 0.0015). Further TWAS of gout identified 105 genes with P value < 0.05 in muscle skeleton and 228 genes with P value < 0.05 in blood. TWAS of ADHD also detected 300 genes with P value < 0.05 in blood. Further comparing the TWAS results identified 9 common candidate genes shared by gout and ADHD, such as CD300C (Pgout = 0.0040; PADHD = 0.0226), KDM6B (Pgout = 0.0074; PADHD = 0.0460), and BST1 (Pgout = 0.0349; PADHD = 0.03560). CONCLUSION: We observed genetic correlation between gout and ADHD and identified multiple candidate genes for gout and ADHD.

Evaluating the Genetic Effects of Gut Microbiota on the Development of Neuroticism and General Happiness: A Polygenic Score Analysis and Interaction Study Using UK Biobank Data.

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.

Long-term antibiotic use during early life and risks to mental traits: an observational study and gene-environment-wide interaction study in UK Biobank cohort.

The relationships between long-term antibiotic use during early life and mental traits remain elusive now. A total of 158,444 subjects from UK Biobank were used in this study. Linear regression analyses were first conducted to assess the correlations between long-term antibiotic use during early life and mental traits. Gene-environment-wide interaction study (GEWIS) was then performed by PLINK2.0 to detect the interaction effects between long-term antibiotic use during early life and genes on the risks of mental traits. Finally, DAVID tool was used to conduct gene ontology (GO) analysis of the identified genes interacting with long-term antibiotic use during early life. We found negative associations of long-term antibiotic use during early life with remembrance (p value=1.74 × 10-6, b = -0.10) and intelligence (p value=2.64 × 10-26, b = -0.13), and positive associations of long-term antibiotic use during early life with anxiety (p value = 2.75 × 10-47, b = 0.12) and depression (p value=2.01 × 10-195, b = 0.25). GEWIS identified multiple significant genes-long-term antibiotic use during early life interaction effects, such as ANK3 (rs773585997, p value = 1.78 × 10-8) for anxiety and STRN (rs140049205, p value = 1.88 × 10-8) for depression. GO enrichment analysis detected six GO terms enriched in the identified genes interacting with long-term antibiotic use during early life for anxiety, such as GO:0030425~dendrite (p value = 3.41 × 10-2) and GO:0005886~plasma membrane (p value = 3.64 × 10-3). Our study results suggest the impact of long-term antibiotic use during early life on the development of mental traits.

Traumatic events during childhood and its risks to substance use in adulthood: an observational and genome-wide by environment interaction study in UK Biobank.

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.

Evaluate the effects of serum urate level on bone mineral density: a genome-wide gene-environment interaction analysis in UK Biobank cohort.

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.

Insomnia affects the levels of plasma bilirubin and protein metabolism: an observational study and GWGEIS in UK Biobank cohort.

STUDY OBJECTIVES: We aim to explore the mechanism of relationship between insomnia and liver metabolism by examining the gene × insomnia interactions. METHODS: Individual level genotypic and phenotypic data were obtained from the UK Biobank cohort. Regression analysis was first conducted to test the association of insomnia with plasma total bilirubin (TBil; n = 186,793), direct bilirubin (DBil; n = 159,854) and total protein (TP; n = 171,574) in UK Biobank cohort. Second, genome-wide gene-environment interaction study (GWGEIS) was conducted by PLINK 2.0, and FUMA platform was used to identify enriched pathway terms. RESULTS: In UK Biobank cohort, we found that TP (P < 2.00 × 10-16), DBil (P = 1.72 × 10-3) and TBil (P = 3.38 × 10-5) were significantly associated with insomnia. GWGEIS of both DBil and TBil observed significant G × INSOMNIA effects between insomnia and UDP Glucuronosyltransferase Family 1 (rs6431558, P = 6.26 × 10-11) gene. GWGEIS of TP also detected several significant genes interacting with insomnia, such as KLF15, (rs70940816, P = 6.77 × 10-10) and DOK7, (rs2344205, P = 1.37 × 10-9). Multiple gene ontology (GO) terms were identified for bilirubin, such as GO_URONIC_ACID_METABOLIC_PROCESS (adjusted P = 4.15 × 10-26). CONCLUSION: Our study results suggested negative associations between insomnia and DBil and TBil; and a positive association between insomnia and TP.

Maternal smoking during pregnancy and risks to depression and anxiety in offspring: An observational study and genome-wide gene-environment interaction analysis in UK biobank cohort.

BACKGROUND: Maternal smoking during pregnancy (MSDP) has been reported to be associated with increased anxiety and depression behaviors in offspring. However, there is still scant evidence to support the link between MSDP and anxiety/depression. METHODS: Using the subjects from the UK Biobank cohort (n = 371,903-432,881). Logistic regression analyses were first conducted to test the correlation between MSDP and anxiety/depression in offspring. Second, genome-wide gene-environment interaction study (GWGEIS) analyses were conducted by PLINK, using MSDP as environmental factor. Genetic correlation analysis of anxiety/depression and smoking was conducted by the LDSC software using the published genome-wide association study (GWAS) summary data of four smoking traits (n = 337,334-1,232,091), anxiety (n = 31,880) and depression (n = 490,359). Finally, pathway enrichment analysis was carried out to detect the pathway involved in the development of offspring anxiety caused by the interaction of MSDP × SNPs. RESULTS: Observational analyses showed that anxiety and depression status in offspring were significantly associated with MSDP (all p < 0.0001). Further GWEGI analyses observed significant MSDP-gene interaction effects at UNC80 gene for anxiety (p = 9.09 × 10-9). LDSC did not detect significant genetic correlation between anxiety and smoking traits. Pathway analysis identified 19 significant pathways for anxiety, such as MANALO_HYPOXIA_UP (FDR = 5.50 × 10-4), REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS (FDR = 0.0304) and ONDER_CDH1_TARGETS_2_UP (FDR = 0.0371). CONCLUSION: Our study results suggested the important impact of MDSP on the risk of anxiety in offspring, partly attributing to environment-gene interactions effects.

Socioeconomic Deprivation Index Is Associated With Psychiatric Disorders: An Observational and Genome-wide Gene-by-Environment Interaction Analysis in the UK Biobank Cohort.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 (ß = .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. CONCLUSIONS: 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.

Identifying insomnia-related chemicals through integrative analysis of genome-wide association studies and chemical-genes interaction information.

STUDY OBJECTIVES: Insomnia is a common sleep disorder and constitutes a major issue in modern society. We provide new clues for revealing the association between environmental chemicals and insomnia. METHODS: Three genome-wide association studies (GWAS) summary datasets of insomnia (n = 113,006, n = 1,331,010, and n = 453,379, respectively) were driven from the UK Biobank, 23andMe, and deCODE. The chemical-gene interaction dataset was downloaded from the Comparative Toxicogenomics Database. First, we conducted a meta-analysis of the three datasets of insomnia using the METAL software. Using the result of meta-analysis, transcriptome-wide association studies were performed to calculate the expression association testing statistics of insomnia. Then chemical-related gene set enrichment analysis (GSEA) was used to explore the association between chemicals and insomnia. RESULTS: For GWAS meta-analysis dataset of insomnia, we identified 42 chemicals associated with insomnia in brain tissue (p < 0.05) by GSEA. We detected five important chemicals such as pinosylvin (p = 0.0128), bromobenzene (p = 0.0134), clonidine (p = 0.0372), gabapentin (p = 0.0372), and melatonin (p = 0.0404) which are directly associated with insomnia. CONCLUSION: Our study results provide new clues for revealing the roles of environmental chemicals in the development of insomnia.

Identifying 5 Common Psychiatric Disorders Associated Chemicals Through Integrative Analysis of Genome-Wide Association Study and Chemical-Gene Interaction Datasets.

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.

A genome-wide multiphenotypic association analysis identified common candidate genes for subjective well-being, depressive symptoms and neuroticism.

Subjective well-being (SWB), depressive symptoms, and neuroticism are common and vital traits of mental disorders. Genetic mechanisms of SWB, depressive symptoms and neuroticism remain elusive now. The large-scale GWAS summary datasets of SWB (n = 229,883), depressive symptoms (n = 180,866), and neuroticism (n = 170,911) were obtained from published studies. MASH tool was applied to the GWAS datasets for identifying candidate SNPs shared by SWB, depressive symptoms and neuroticism. SNPs detected by MASH, were then mapped to target genes considering regulatory SNP (rSNP), methylated quantitative trait locus (MeQTL) and the SNPs near to known genes. Gene set enrichment analysis (GSEA) was conducted by the FUMA platform. A total of 122 candidate SNPs were detected by MASH analysis, mapping to 29 target genes, such as CLDN23, MSRA and XKR6. GO enrichment analysis identified multiple immune related gene sets for SWB, depressive symptoms and neuroticism, such as GSE2770_UNTREATED_VS_IL4_TREATED_ACT_CD4_TCELL_48H_DN (P = 7.32 × 10-3), GSE6259_FLT3L_INDUCED_DEC205_POS_DC_VS_CD4_TCELL_DN (P = 2.52 × 10-2). We also found some mental disorders related gene sets were associated with three phenotypes, such as mood instability (P = 1.15 × 10-6) and neuroticism (P = 1.72 × 10-6). We identified multiple candidate genes and GO terms shared by SWB, depressive symptoms and neuroticism. Our results support the overlapping genetic mechanisms, and suggest a functional correlation between immunity and SWB, depressive symptoms and neuroticism.

A large-scale genetic correlation scan identified the plasma proteins associated with brain function related traits.

AIMS: Insomnia, intelligence and neuroticism are three typical traits and dysfunctions mainly regulated by human brain. Our research aimed to explore the potential genetic relationships between brain function related traits and more than 3000 human plasma proteins. MATERIALS AND METHODS: We conducted a large-scale genetic correlation scan of human plasma proteins and three brain function related traits, including insomnia, intelligence and neuroticism. Linkage disequilibrium score regression (LDSC) analysis was performed to estimate the genetic correlations between each of the blood proteins and insomnia, intelligence and neuroticism via utilizing the genome-wide association study summary statistics of plasma proteins and those three traits. RESULTS: LDSC analysis identified 18 specific plasma proteins shown suggestive genetic correlations with insomnia such as Periostin (coefficient=-0.3910, P value = 0.0070). Twenty-one plasma proteins exhibited genetic correlations with intelligence such as Ecto-ADP-ribosyltransferase 3 (coefficient = 0.3066, P value = 0.0013). Six specific plasma proteins shown suggestive genetic correlations with neuroticism, such as CD70 antigen (coefficient = 0.2979, P value = 0.0134). After further comparing the suggestive proteins between insomnia, intelligence and neuroticism, we detected 3 common plasma proteins shared by insomnia and intelligence such as Periostin (coefficient insomnia =-0.3910, Pinsomnia value = 0.0070; coefficient intelligence =0.2673, Pintelligence value = 0.0159) and Neurexin-1 (coefficient insomnia =-0.2913, Pinsomnia value = 0.0197; coefficient intelligence = 0.2399, Pintelligence value = 0.0035). We also detected 2 common plasma proteins shared by intelligence and neuroticism, including CD70 antigen (coefficient intelligence =-0.2092, Pintelligence value = 0.0337; coefficient neuroticism = 0.2979, Pneuroticism value = 0.0134). CONCLUSION: Our results provide novel clues for unveiling the functional relevance of plasma proteins and brain function related traits.

CGSEA: A Flexible Tool for Evaluating the Associations of Chemicals with Complex Diseases.

The etiology of many human complex diseases or traits involves interactions between chemicals and genes that regulate important physiological processes. It has been well documented that chemicals can contribute to disease development through affecting gene expression in vivo In this study, we developed a flexible tool CGSEA for scanning the candidate chemicals associated with complex diseases or traits. CGSEA only need genome-wide summary level data, such as transcriptome-wide association studies (TWAS) and mRNA expression profiles. CGSEA was applied to the GWAS summaries of attention deficiency/hyperactive disorder, (ADHD), autism spectrum disorder (ASD) and cervical cancer. CGSEA identified several significant chemicals, which have been demonstrated to be involved in the development or treatment of ADHD, ASD and cervical cancer. The CGSEA program and user manual are available at https://github.com/ChengSQXJTU/CGSEA.

An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a complex mood disorder. The genetic mechanism of BD remains largely unknown. METHODS: We conducted an integrative analysis of genome-wide association study (GWAS) and regulatory SNP (rSNP) annotation datasets, including transcription factor binding regions (TFBRs), chromatin interactive regions (CIRs), mature microRNA regions (miRNAs), long non-coding RNA regions (lncRNAs), topologically associated domains (TADs) and circular RNAs (circRNAs). Firstly, GWAS dataset 1 of BD (including 20,352 cases and 31,358 controls) and GWAS dataset 2 of BD (including 7481 BD patients and 9250 controls) were integrated with rSNP annotation database to obtain BD associated SNP regulatory elements and SNP regulatory element-target gene (E-G) pairs, respectively. Secondly, a comparative analysis of the two datasets results was conducted to identify the common rSNPs and also their target genes. Then, gene sets enrichment analysis (FUMA GWAS) and HumanNet-XC analysis were conducted to explore the functional relevance of identified target genes with BD. RESULTS: After the integrative analysis, we identified 52 TFBRs target genes, 44 TADs target genes, 55 CIRs target genes and 21 lncRNAs target genes for BD, such as ITIH4 (Pdataset1 = 6.68 × 10-8, Pdataset2 = 6.64 × 10-7), ITIH3 (Pdataset1 = 1.09 × 10-8, Pdataset2 = 2.00 × 10-7), SYNE1 (Pdataset1 = 1.80 × 10-6, Pdataset2 = 4.33 × 10-9) and OPRM1 (Pdataset1 = 1.80 × 10-6, Pdataset2 = 4.33 × 10-9). CONCLUSION: We conducted a large-scale integrative analysis of GWAS and 6 common rSNP information datasets to explore the potential roles of rSNPs in the genetic mechanism of BD. We identified multiple candidate genes for BD, supporting the importance of rSNP in the development of BD.

Assessing the Relationship Between Gut Microbiota and Bone Mineral Density.

BACKGROUND: Recent study demonstrates the comprehensive effects of gut microbiota on complex diseases or traits. However, limited effort has been conducted to explore the potential relationships between gut microbiota and BMD. METHODS: We performed a polygenetic risk scoring (PRS) analysis to systematically explore the relationships between gut microbiota and body BMD. Significant SNP sets associated with gut microbiota were derived from previous genome-wide association study (GWAS). In total, 2,294 to 5,065 individuals with BMD values of different sites and their genotype data were obtained from UK Biobank cohort. The gut microbiota PRS of each individual was computed from the SNP genotype data for each study subject of UK Biobank by PLINK software. Using computed PRS as the instrumental variables of gut microbiota, Pearson correlation analysis of individual PRS values and BMD values was finally conducted to test the potential association between gut microbiota and target trait. RESULTS: In total, 31 BMD traits were selected as outcome to assess their relationships with gut microbiota. After adjusted for age, sex, body mass index, and the first 5 principal components (PCs) as the covariates using linear regression model, pelvis BMD (P = 0.0437) showed suggestive association signal with gut microbiota after multiple testing correction. CONCLUSION: Our study findings support the weak relevance of gut microbiota with the development of BMD.

An atlas of genetic correlations between psychiatric disorders and human blood plasma proteome.

BACKGROUND: Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins. METHODS: The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins. RESULTS: LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ). CONCLUSIONS: This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.