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1.
Sleep Med ; 85: 184-190, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34343768

RESUMO

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.

2.
Transl Psychiatry ; 11(1): 431, 2021 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-34417442

RESUMO

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.


Assuntos
Interação Gene-Ambiente , Transtornos Relacionados ao Uso de Substâncias , Adulto , Consumo de Bebidas Alcoólicas/epidemiologia , Consumo de Bebidas Alcoólicas/genética , Bancos de Espécimes Biológicos , Estudo de Associação Genômica Ampla , Humanos , Reino Unido/epidemiologia
3.
J Psychiatr Res ; 140: 149-158, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34118634

RESUMO

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.


Assuntos
Interação Gene-Ambiente , Efeitos Tardios da Exposição Pré-Natal , Ansiedade/epidemiologia , Ansiedade/genética , Bancos de Espécimes Biológicos , Proteínas de Transporte , Depressão/epidemiologia , Depressão/genética , Feminino , Estudo de Associação Genômica Ampla , Humanos , Proteínas de Membrana , Gravidez , Efeitos Tardios da Exposição Pré-Natal/epidemiologia , Efeitos Tardios da Exposição Pré-Natal/genética , Fumar , Reino Unido/epidemiologia
4.
Endocrine ; 73(3): 702-711, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34046847

RESUMO

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.


Assuntos
Densidade Óssea , Ácido Úrico , Bancos de Espécimes Biológicos , Densidade Óssea/genética , Feminino , Interação Gene-Ambiente , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Reino Unido
5.
Biol Psychiatry ; 89(9): 888-895, 2021 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-33500177

RESUMO

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.


Assuntos
Bancos de Espécimes Biológicos , Esquizofrenia , Interação Gene-Ambiente , Estudo de Associação Genômica Ampla , Humanos , Herança Multifatorial , Fatores Socioeconômicos , Reino Unido/epidemiologia
6.
Neuropsychopharmacology ; 46(6): 1086-1092, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-32801349

RESUMO

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.


Assuntos
Bancos de Espécimes Biológicos , Estudo de Associação Genômica Ampla , Antibacterianos/efeitos adversos , Interação Gene-Ambiente , Humanos , Reino Unido
7.
Can J Psychiatry ; : 706743720970844, 2020 Nov 06.
Artigo em Inglês | MEDLINE | ID: mdl-33155823

RESUMO

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 (P gout = 0.0040; P ADHD = 0.0226), KDM6B (P gout = 0.0074; P ADHD = 0.0460), and BST1 (P gout = 0.0349; P ADHD = 0.03560). CONCLUSION: We observed genetic correlation between gout and ADHD and identified multiple candidate genes for gout and ADHD.

8.
Eur Psychiatry ; 63(1): e73, 2020 07 24.
Artigo em Inglês | MEDLINE | ID: mdl-32706328

RESUMO

BACKGROUND: Birth weight influences not only brain development, but also mental health outcomes, including depression, but the underlying mechanism is unclear. METHODS: The phenotypic data of 12,872-91,009 participants (59.18-63.38% women) from UK Biobank were included to test the associations between the birth weight, depression, and brain volumes through the linear and logistic regression models. As birth weight is highly heritable, the polygenic risk scores (PRSs) of birth weight were calculated from the UK Biobank cohort (154,539 participants, 56.90% women) to estimate the effect of birth weight-related genetic variation on the development of depression and brain volumes. Finally, the mediation analyses of step approach and mediation analysis were used to estimate the role of brain volumes in the association between birth weight and depression. All analyses were conducted sex stratified to assess sex-specific role in the associations. RESULT: We observed associations between birth weight and depression (odds ratio [OR] = 0.968, 95% confidence interval [CI] = 0.957-0.979, p = 2.29 × 10-6). Positive associations were observed between birth weight and brain volumes, such as gray matter (B = 0.131, p = 3.51 × 10-74) and white matter (B = 0.129, p = 1.67 × 10-74). Depression was also associated with brain volume, such as left thalamus (OR = 0.891, 95% CI = 0.850-0.933, p = 4.46 × 10-5) and right thalamus (OR = 0.884, 95% CI = 0.841-0.928, p = 2.67 × 10-5). Additionally, significant mediation effects of brain volume were found for the associations between birth weight and depression through steps approach and mediation analysis, such as gray matter (B = -0.220, p = 0.020) and right thalamus (B = -0.207, p = 0.014). CONCLUSIONS: Our results showed the associations among birth weight, depression, and brain volumes, and the mediation effect of brain volumes also provide evidence for the sex-specific of associations.


Assuntos
Bancos de Espécimes Biológicos , Peso ao Nascer/fisiologia , Encéfalo/anatomia & histologia , Encéfalo/fisiopatologia , Depressão/genética , Depressão/fisiopatologia , Tamanho do Órgão/fisiologia , Adulto , Idoso , Estudos de Coortes , Depressão/etiologia , Feminino , Substância Cinzenta/anatomia & histologia , Substância Cinzenta/fisiopatologia , Humanos , Masculino , Pessoa de Meia-Idade , Razão de Chances , Análise de Regressão , Fatores de Risco , Tálamo/anatomia & histologia , Tálamo/fisiopatologia , Reino Unido/epidemiologia , Substância Branca/anatomia & histologia , Substância Branca/fisiopatologia
9.
Clin Transl Med ; 10(2): e108, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32564518

RESUMO

BACKGROUND: Herpes simplex virus-1 (HSV-1) infection is reported to be associated with depression. But limited efforts were made to investigate the relationship between HSV-1 infection and the risk of depression, especially from the genetic perspective. METHODS: In UK Biobank cohort, linear and logistic regression analyses were first performed to test the association of HSV-1 seropositivity/antibody with depression, including depression status (N = 2951) and Patient Health Questionnaire (PHQ) score (N = 2839). Using individual genotypic and phenotypic data from the UK Biobank, genome-wide environmental interaction study (GWEIS) was then conducted by PLINK2.0 to evaluate gene × HSV-1 interacting effect on the risk of depression. Finally, gene set enrichment analysis was conducted to identify the biological pathways involved in the observed gene × HSV-1 interaction for depression. RESULT: In UK Biobank cohort, significant associations were observed between depression status and HSV-1 (odds ratio [OR] = 1.09; 95% confidence interval [CI], 1.02-1.16; P = 2.40 × 10-2 for HSV-1 antibody and OR = 1.28; 95% CI, 1.12-1.47, P = 2.59 × 10-3 for HSV-1 seropositivity). GWEIS revealed four significant gene × HSV-1 interaction signals for PHQ score (all P < 5.0 × 10-8 ) and the leading loci was SULF2 (rs6094791, P = 8.60 × 10-9 ). Pathway analyses identified 21 pathways for PHQ score and 19 for depression status, including multiple neural development- and immune-related ones, such as KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION (false discovery rate [FDR] = 3.18 × 10-2 ) for depression and LU_AGING_BRAIN_UP (FDR = 4.21 × 10-2 ) for PHQ score. CONCLUSION: Our results suggested that HSV-1 was associated with the risk of depression, which was modulated by the several genes that were related to the nerve development or immune function.

10.
Schizophr Bull ; 2020 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-32291453

RESUMO

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.

11.
Brain Res Bull ; 158: 84-89, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32119964

RESUMO

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.

12.
Sleep ; 43(9)2020 09 14.
Artigo em Inglês | MEDLINE | ID: mdl-32170308

RESUMO

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.


Assuntos
Estudo de Associação Genômica Ampla , Distúrbios do Início e da Manutenção do Sono , Encéfalo , Predisposição Genética para Doença/genética , Humanos , Polimorfismo de Nucleotídeo Único , Distúrbios do Início e da Manutenção do Sono/induzido quimicamente , Distúrbios do Início e da Manutenção do Sono/genética , Software , Transcriptoma
13.
Cereb Cortex ; 30(7): 4197-4203, 2020 06 01.
Artigo em Inglês | MEDLINE | ID: mdl-32108233

RESUMO

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.

14.
J Psychiatr Res ; 124: 22-28, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32109668

RESUMO

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.


Assuntos
Depressão , Estudo de Associação Genômica Ampla , Depressão/genética , Predisposição Genética para Doença/genética , Humanos , Neuroticismo , Polimorfismo de Nucleotídeo Único/genética
15.
Eur Psychiatry ; 63(1): e17, 2020 02 20.
Artigo em Inglês | MEDLINE | ID: mdl-32093803

RESUMO

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.


Assuntos
Transtorno do Espectro Autista/metabolismo , Transtorno Bipolar/metabolismo , Transtorno Depressivo Maior/metabolismo , Proteoma/metabolismo , Esquizofrenia/metabolismo , Adulto , Transtorno do Espectro Autista/genética , Transtorno Bipolar/genética , Transtorno Depressivo Maior/genética , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Desequilíbrio de Ligação , Masculino , Plasma/metabolismo , Proteoma/genética , Esquizofrenia/genética
16.
Front Genet ; 11: 6, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32082367

RESUMO

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.

17.
Aging (Albany NY) ; 12(4): 3287-3297, 2020 02 22.
Artigo em Inglês | MEDLINE | ID: mdl-32090979

RESUMO

BACKGROUND: Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors. RESULT: MASH analysis identified a group of target genes for risky behaviors, such as APBB2, MAPT and DCC. For GO enrichment analysis, FUMA detected multiple risky behaviors related GO terms and brain related diseases, such as regulation of neuron differentiation (adjusted P value = 2.84×10-5), autism spectrum disorder (adjusted P value =1.81×10-27) and intelligence (adjusted P value =5.89×10-15). CONCLUSION: We reported multiple candidate genes and GO terms shared by the four risky behaviors, providing novel clues for understanding the genetic mechanism of risky behaviors. METHODS: Multivariate Adaptive Shrinkage (MASH) analysis was first applied to the GWAS data of four specific risky behaviors (automobile speeding, drinks per week, ever-smoker, number of sexual partners) to detect the common genetic variants shared by the four risky behaviors. Utilizing genomic functional annotation data of SNPs, the SNPs detected by MASH were then mapped to target genes. Finally, gene set enrichment analysis of the identified candidate genes were conducted by the FUMA platform to obtain risky behaviors related gene ontology (GO) terms as well as diseases and traits, respectively.


Assuntos
Transtorno do Espectro Autista/genética , Ontologia Genética , Inteligência/genética , Neurogênese/genética , Polimorfismo de Nucleotídeo Único , Assunção de Riscos , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Fenótipo , Transcriptoma
18.
Int J Bipolar Disord ; 8(1): 6, 2020 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-32009227

RESUMO

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.

19.
G3 (Bethesda) ; 10(3): 945-949, 2020 03 05.
Artigo em Inglês | MEDLINE | ID: mdl-31937547

RESUMO

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.


Assuntos
Transtorno do Deficit de Atenção com Hiperatividade/epidemiologia , Transtorno do Espectro Autista/epidemiologia , Software , Neoplasias do Colo do Útero/epidemiologia , Transtorno do Deficit de Atenção com Hiperatividade/genética , Transtorno do Espectro Autista/genética , Crizotinibe , Etoxiquina , Feminino , Expressão Gênica , Estudo de Associação Genômica Ampla , Humanos , Indanos , Indóis , Cetoconazol , Acetato de Metilazoximetanol , Sesquiterpenos , Toluidinas , Urânio , Neoplasias do Colo do Útero/genética , Vitamina E
20.
Brief Bioinform ; 21(3): 1016-1022, 2020 05 21.
Artigo em Inglês | MEDLINE | ID: mdl-30953055

RESUMO

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.

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