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.

Osteoporosis (OP) is a multi-factorial bone disease influenced by genetic factors, age, and lifestyles. The aim of this study is to evaluate the genetic correlations between OP and multiple lifestyle-related factors, and explore the genes underlying the detected genetic correlations. Linkage disequilibrium score regression (LDSC) analysis was applied to evaluate the genetic correlations of total body bone mineral density (TB-BMD) of different ages (including 15–30 years, 30–45 years, 45–60 years, and over 60 years) with four common lifestyle/environment-related factors (including serum 25-hydroxyvitamin D, cigarette smoking, alcohol dependence, and caffeine metabolites). Transcriptome-wide association studies (TWAS) of TB-BMD (30–45 years) and smoking were conducted in peripheral blood (PB), whole blood (WB), and adipose tissues. The identified candidate genes were also subjected to gene set enrichment analysis (GSEA). Genetic correlation was only observed between TB-BMD (30–45 years) and cigarette smoking status (P = 0.01, LD score = 0.11 ± 0.04). No significant genetic correlation was detected for other lifestyle/environmental factors, including serum 25-hydroxyvitamin D, alcohol dependence, and caffeine metabolites for TB-BMD within all of the four age groups. TWAS identified 85 genes in PB and 163 genes in WB for TB-BMD, as well as 123 genes in PB and 257 genes in WB for smoking. Multiple common candidate genes shared by both TB-BMD and smoking were detected, such as MAP1LC3B (PTB-BMD-PB = 1.00 × 10–3, Psmoking-PB = 9.62 × 10–3, PTB-BMD-WB = 2.99 × 10–2) and SLC23A3 (PTB-BMD-WB = 1.48 × 10–2, Psmoking-WB = 8.76 × 10–3). GSEA detected one GO terms for TB-BMD (cytosol) in WB, one GO term for smoking (mitochondrion) in PB, and one pathway (oocyte meiosis) for smoking in WB.

Background: Schizophrenia is a complex mental disorder.The genetic mechanism of schizophrenia remains elusive now.Methods: We conducted a large-scale integrative analysis of two genome-wide association studies of schizophrenia with functional annotation datasets of regulatory single-nucleotide polymorphism (rSNP).The significant SNPs identified by the two genome-wide association studies were first annotated to obtain schizophrenia associated rSNPs and their target genes and proteins, respectively.We then compared the integrative analysis results to identify the common rSNPs and their target regulatory genes and proteins, shared by the two genome-wide association studies of schizophrenia.Finally, DAVID tool was used to conduct gene ontology and pathway enrichment analysis of the identified targets genes and proteins.Results: We detected 53 schizophrenia-associated target genes for rSNP, such as FOS (P value = 2.18×10 -20 ), ATXN1 (P value = 5.22×10 -21 ) and HLA-DQA1 (P value = 1.98×10 -10 ).Pathway enrichment analysis identified 24 pathways for transcription factors binding regions, chromatin interacting regions, long non-coding RNAs, topologically associated domains, circular RNAs and post-translational modifications, such as hsa05034:Alcoholism (P value = 2.57×10 -7 ) and hsa04612:Antigen processing and presentation (P value = 6.82×10 -8 ). Conclusion:We detected multiple candidate genes, gene ontology terms and pathways for schizophrenia, supporting the functional importance of rSNPs, and providing novel clues for understanding the genetic architecture of schizophrenia.

Osteoporosis is a common metabolic bone disease. The impact of global blood plasma proteins on the risk of osteoporosis remains elusive now. We performed a large-scale polygenic risk score (PRS) analysis to evaluate the potential effects of blood plasma proteins on the development of osteoporosis in 2286 Caucasians, including 558 males and 1728 females. Bone mineral density (BMD) and bone areas at ulna & radius, hip, and spine were measured using Hologic 4500W DXA. BMD/bone areas values were adjusted for age, sex, height, and weight as covariates. Genome-wide SNP genotyping of 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The 267 blood plasma proteins-associated SNP loci and their genetic effects were obtained from recently published genome-wide association study (GWAS) using a highly multiplexed aptamer-based affinity proteomics platform. The polygenetic risk score (PRS) of study subjects for each blood plasma protein was calculated from the genotypes data of the 2286 Caucasian subjects by PLINK software. Pearson correlation analysis of individual PRS values and BMD/bone area value was performed using R. Additionally, gender-specific analysis also was performed by Pearson correlation analysis. 267 blood plasma proteins were analyzed in this study. For BMD, we observed association signals between 41 proteins and BMD, mainly including whole body total BMD versus Factor H (p value = 9.00 × 10−3), whole body total BMD versus BGH3 (p value = 1.40 × 10−2), spine total BMD versus IGF-I (p value = 2.15 × 10−2), and spine total BMD versus SAP (p value = 3.90 × 10−2). As for bone areas, association evidence was observed between 45 blood plasma proteins and bone areas, such as ferritin versus spine area (p value = 1.90 × 10−2), C4 versus hip area (p value = 1.25 × 10−2), and hemoglobin versus right ulna and radius area (p value = 2.70 × 10−2). Our study results suggest the modest impact of blood plasma proteins on the variations of BMD/bone areas, and identify several candidate blood plasma proteins for osteoporosis.

Autism spectrum disorders (ASD) are a group of highly heritable psychiatric syndromes with high prevalence. The genetic mechanism of ASD remains elusive now. Here we conducted a transcriptome-wide association study (TWAS) of ASD. The GWAS summary data of ASD was driven from the Psychiatric Genomics Consortium (PGC) portal, totally involving 5,305 ASD cases and 5,305 controls. FUSION software was applied to the GWAS summary data for tissue-related TWAS of ASD considering brain and blood. The ASD associated genes identified by TWAS were further validated by mRNA expression profiling of ASD and the Simons Foundation for Autism Research (SFARI) Gene tool. DAVID 6.8 was used to perform gene ontology (GO) enrichment analysis of ASD associated genes identified by TWAS. TWAS identified 85 genes with TWAS P value <0.05 for ASD. Further comparing the 85 genes with the differentially expressed genes identified by mRNA expression profiling of ASD patients found 5 overlapped genes, including MUTYH (PTWAS = 0.0460, PmRNA = 0.0040), ARHGAP27 (PTWAS = 0.0100, PmRNA = 0.0016), GCA (PTWAS = 0.0480, PmRNA = 0.0063), CCDC14 (PTWAS = 0.0067, PmRNA = 0.0035), and MED15 (PTWAS = 0.0324, PmRNA = 0.0092). Gene Ontology (GO) enrichment analysis of the genes identified by TWAS detected 10 significant GO terms, such as mitochondrion (P = 0.0051), NAD or NADH binding (P = 0.0169), mitochondrial part (P = 0.0386) and 2-oxoglutarate metabolic process (P = 0.0399). In conclusion, this study identified multiple ASD associated genes and gene sets, providing novel clues for revealing the pathogenesis of ASD. Autism Research 2019, 12: 33-38. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Recent genetic studies of autism spectrum disorders (ASD) have found multiple ASD related genes. However, the results of these studies were hardly replicated with each other, providing limited clues for exploring the genetic mechanism of ASD. This study detected a group of candidate genes showing transcriptome-wide associations with ASD. These results may provide novel clues for revealing the pathogenesis of ASD.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia characterized by extensive structural, contractile and electrophysiological remodeling. The genetic basis of AF remained elusive until now. Transcriptome-wide association study (TWAS) was conducted by FUSION tool using gene expression weights of 7 tissues combined with a large-scale genome-wide association study (GWAS) dataset of AF, totally involving 8180 AF cases and 28,612 controls. Significant genes identified by TWAS were then subjected to gene ontology (GO) and pathway enrichment analysis. The genome-wide mRNA gene expression profiling of AF was compared with the results of TWAS to detect common genes shared by TWAS and mRNA expression profiling of AF. TWAS detected a group of candidate genes with PTWAS values < 0.05 across the seven tissues for AF, such as CMAH (PTWAS = 3.15 × 10–25 for whole blood), INCENP (PTWAS = 1.77 × 10–22 for artery aorta), CMAHP (PTWAS = 4.57 × 10–20 for artery aorta). Pathway enrichment analysis identified multiple candidate pathways, such as protein K48-linked ubiquitination (P value = 0.0124), positive regulation of leukocyte chemotaxis (P value = 0.0046) and fatty acid degradation (P value = 0.0295). Further comparing the GO results of TWAS and mRNA expression profiling, 2 common GO terms were identified, including actin binding (PTWAS = 0.0446, PmRNA = 7.00 × 10–4) and extracellular matrix (PTWAS = 0.0037, PmRNA = 3.00 × 10–6). We detected multiple novel candidate genes, GO terms and pathways for AF, providing novel clues for understanding the genetic mechanism of AF.

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.

Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.

Inflammatory bowel disease (IBD) is a complex disease, resulting from abnormal immune response to intestinal tract microbiota in genetically susceptible individuals. Crohn's disease and ulcerative colitis (UC) are the two major types of IBD. Transcriptome-wide association study (TWAS) of IBD was first performed using a large-scale genome-wide association study summary data sets of IBD. The FUSION software was applied for TWAS, considering various tissues and cells. The genes identified by TWAS were then validated by the gene expression profiling data sets of IBD. The functional annotation and potential pathways of common differentially expressed genes were further subjected to gene ontology (GO) and pathway enrichment analysis. Integrative analysis of TWAS and messenger RNA (mRNA) expression data detected several tissues related common genes for UC, such as HLA-DRB1 (PTWAS = 0.024; mRNA expression ratio = 1.700) and TAP2 in colon (P TWAS = 0.047; mRNA expression ratio = 2.170). Further comparing the GO enrichment analysis results of TWAS and mRNA expression data, we identified 11 common GO terms for UC, such as plasma membrane (P value = 5.08 × 10-10 ) in intestinal tissues and immune response (P = 0.001) in peripheral blood. We also detected several common pathways for UC, including cell adhesion molecules (P = 0.003) in intestinal tissues, IBD (P = 0.049) in whole blood and phagosome (P = 0.0003) in peripheral blood. Our study results provide novel clues for understanding the genetic mechanism of IBD.

Intelligence is an important quantitative trait associated with human cognitive ability. The genetic basis of intelligence remains unclear now. Utilizing the latest chromosomal enhancer maps of brain regions, we explored brain region related biological pathways associated with intelligence. Summary data was derived from a large scale genome-wide association study (GWAS) of human, involving 78,308 unrelated individuals from 13 cohorts. The chromosomal enhancer maps of 8 brain regions were then aligned with the GWAS summary data to obtain the association testing results of enhancer regions for intelligence. Gene set enrichment analysis was then conducted to identify the biological pathways associated with intelligence for 8 brain regions, respectively. A total of 178 KEGG pathways was analyzed in this study. We detected multiple biological pathways showing cross brain regions or brain region specific association signals for human intelligence. For instance, KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS pathway presented association signals for intelligence across 8 brain regions (all P value < 0.01). KEGG_GLYCOSPHINGOLIPID_BIOSYNTHESIS_GANGLIO_SERIES was detected for 5 brain regions. We also identified several brain region specific pathways, such as AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM for Germinal Matrix (P value = 0.009) and FRUCTOSE_AND_MANNOSE_METABOLISM for Anterior Caudate (P value = 0.005). Our study results provided novel clues for understanding the genetic mechanism of intelligence.

Attention deficit hyperactivity disorder (ADHD), bipolar disorder (BP) and schizophrenia (SCZ) are complex psychiatric disorders. We conducted a large-scale integrative analysis of genome-wide association studies (GWAS) and life course consistent methylation quantitative trait loci (meQTLs) datasets. The GWAS data of ADHD (including 20,183 cases and 35,191 controls), BP (including 7481 cases and 9250 controls) and SCZ (including 36,989 cases and 113,075 controls) were derived from published GWAS. Life course consistent meQTLs dataset was obtained from a longitudinal meQTLs analysis of 1018 mother-child pairs. Gene prioritization, pathway and tissue/cell type enrichment analysis were conducted by DEPICT. We identified multiple genes and pathways with common or disease specific effects, such as NISCH (P = 9.87 x 10(-3) for BP and 2.49 x 10(-6) for SCZ), ST3GAL3 (P = 1.19 x 10(-2) for ADHD), and KEGG_MAPK_SIGNALING_PATHWAY (P = 1.56 x 10(-3) for ADHD, P = 4.71 x 10(-3) for BP, P = 4.60 x 10(-4) for SCZ). Our study provides novel clues for understanding the genetic mechanism of ADHD, BP and SCZ.

Objective: To explore the associations between chemical elements and attention deficit hyperactivity disorder (ADHD)/intelligence quotient (IQ). Methods: We applied elements related gene set enrichment analysis (ERGSEA) to explore the relationships between elements and ADHD/IQ. The GWAS dataset of ADHD was derived from the Psychiatric Genomics Consortium, involving 55,374 individuals. The GWAS dataset of IQ was derived from UK Biobank web-based measure (n = 17,862), UK Biobank touchscreen measure (n = 36,257), CHIC consortium (n = 12,441) and five additional cohorts (n = 11,748). Enhancer-gene datasets of eight brain tissues consist of 935 individuals. Utilizing the published GWAS summary and eight brain region-related chromosomal enhancer maps to obtain the SNP association testing signals. The element-gene interaction datasets of 21 elements were downloaded from the comparative toxicogenomics database (CTD). Results: ERGSEA observed significant associations between 4 elements and ADHD, such as Al at Hippocampus Middle (P value = 0.040), As at Angular Gyms (P value = 0.007) and Na at Hippocampus Middle (P value = 0.026). Additionally, ERGSEA identified that 5 elements were associated with IQ, mainly including Al at Dorsolateral Prefrontal Cortex (P value = 0.017), As at Dorsolateral Prefrontal Cortex (P value = 0.004) and Pb at Germinal Matrix (P value = 0.045). Conclusion: Our study results provide novel clues for understanding the associations between elements and ADHD/IQ. This study also illustrated the good performance of ERGSEA approach for complex diseases.

Attention deficit/hyperactivity disorder (ADHD) is among the most common childhood onset psychiatric behavioral disorders, and the pathogenesis of ADHD is still unclear. Utilizing the latest genome wide association studies (GWAS) data and enhancer map, we explored the brain region related biological pathways associated with ADHD. The GWAS summary data of ADHD was driven from a published study, involving 20,183 ADHD cases and 35,191 healthy controls. The brain-related enhancer map was collected from ENCODE and Roadmap Epigenomics (ENCODE + Roadmap) including 489,581 enhancers. Firstly, the chromosomal enhancer maps of four brain regions were aligned with the ADHD GWAS summary data in order to obtain enhancer SNPs. Then the significant enhancers SNPs were subjected to the gene set enrichment analysis (GSEA) for identifying ADHD associated gene sets. A total of 866 pathways and 4 brain tissues were analyzed in this study. We detected several candidate genes for ADHD, such as AHI1, ALG2 and DNM1. We also detected several candidate biological pathways associated with ADHD, such as Reactome SEMA4D in semaphorin signaling and Reactome NCAM1 interactions. Our findings may provide a novel insight into the complex genetic mechanism of ADHD.

OBJECTIVE:The relationships between early growth parameters and bone mineral density (BMD) remain elusive now. In this study, we performed a large scale polygenic risk score (PRS) analysis to evaluate the potential impact of early growth parameters on the variations of BMD. METHODS:We used 2286 Caucasian subjects as cohort 1 and 3404 Framingham Heart Study (FHS) subjects as cohort 2 in this study. BMD at ulna & radius, hip and spine were measured using dual energy X-ray absorptiometry. BMD values were adjusted for age, sex, height and weight as covariates. Genome-wide single-nucleotide polymorphism (SNP) genotyping of the 2286 Caucasian subjects was performed using Affymetrix Human SNP Array 6.0. The GWAS datasets of early growth parameters were driven from the Early Growth Genetics Consortium, including birth weight (BW), birth head circumference (BHC), childhood body mass index (CBMI), pubertal height growth related indexes and tanner stage. Polygenic Risk Score (PRSice) and linkage disequilibrium (LD) score regression analysis were conducted to assess the genetic correlation between early growth parameters and BMD. RESULTS:We detected significant genetic correlations in cohort 1, such as total spine BMD vs. CBMI (p value = 1.51 × 10-4, rg = 0.4525), right ulna and radius BMD vs. CBMI (p value = 1.51 × 10-4, rg = 0.4399) and total body BMD vs. tanner stage (p value = 7.00 × 10-4, rg = -0.0721). For cohort 2, significant correlations were observed for total spine BMD vs. height change standard deviation score (SDS) between 8 years and adult (denoted as PGF + PGM) (p value = 3.97 × 10-4, rg = -0.1425), femoral neck BMD vs. the timing of peak height velocity by looking at the height change SDS between age 14 years and adult (denoted as PTF + PTM) (p value = 7.04 × 10-4, rg = -0.2185), and total spine BMD vs. PTF + PTM (p value = 6.86 × 10-4, rg = -0.2180). CONCLUSION:Our study results suggest that some early growth parameters could affect the variations of BMD.

Autism spectrum disorder (ASD) is a complex developmental disorder with strong genetic components involved. Recent studies have demonstrated the importance of non‐coding regulatory variants for complex diseases. To explore the roles of chromosomal enhancer regions in the pathogenesis of ASD, we conducted an integrative analysis of genome‐wide association study (GWAS) and brain region related enhancer‐gene networks for ASD. The GWAS data of ASD were driven from a published study, involving 7,387 ASD cases and 8,567 controls. The enhancer‐gene networks of eight brain regions were used here. The GWAS of ASD was first merged respectively with the enhancer datasets of eight brain regions. Pathway enrichment analysis was then performed to detect ASD associated pathways based on the enhancer‐related single nucleotide polymorphism (SNPs) of each brain region. We detected multiple genes with brain region specific or common association signals, such as PGM3 ( P value = 1.93 × 10 −5 ) and RWDD2A ( P value = 1.93 × 10 −5 ) for hippocampus middle, and ENPP4 (all P values <0.05), and ENPP5 (all P values <0.05) for seven brain regions. By comparing the pathway enrichment analysis results of various brain regions, several cross brain regions pathways were detected for ASD, such as REACTOME_POTASSIUM_CHANNELS (all P values <0.05) for six brain regions and KEGG_CELL_ADHESION_MOLECULES_CAMS (all P values <0.05) for seven brain regions. In addition, several pathways were also identified for specific brain regions, such as REACTOME_CD28_DEPENDENT_PI3K_AKT_SIGNALING ( P value = 4.00 × 10 −3 ) for angular gyrus, REACTOME_SIGNALING_BY_CONSTITUTIVELY_ACTIVE_EGFR ( P value = 2.22 × 10 −3 ) for anterior caudate, and KEGG_PRION_DISEASES ( P value = 1.00 × 10 −4 ) for germinal matrix. Our results provide novel clues for understanding the genetic basis of ASD. Autism Research 2019, 12: 26–32 . © 2018 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary ASD is a complex developmental disorder with strong genetic components, but the pathogenesis of ASD is still unclear. Using the latest GWAS data and enhancer map, we explored the brain region related biological pathways associated with ASD. Our results provide novel clues for revealing the functional relevance of enhancer variants with ASD and understanding the genetic basis of ASD.

Schizophrenia is a serious mental disease with high heritability. To better understand the genetic basis of schizophrenia, we conducted a large scale integrative analysis of genome-wide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data was derived from a published GWAS of schizophrenia, containing 9394 schizophrenia patients and 12,462 healthy controls. The eQTLs dataset was obtained from an eQTLs meta-analysis of 5311 subjects, containing 923,021 cis-eQTLs for 14,329 genes and 4732 trans-eQTLs for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. The SMR analysis results were further subjected to gene set enrichment analysis (GSEA) to identify schizophrenia associated gene sets. SMR detected 49 genes significantly associated with schizophrenia. The top five significant genes were CRELD2 (p value = 1.65 × 10− 11), DIP2B (p value = 3.94 × 10− 11), ZDHHC18 (p value = 1.52 × 10− 10) and ZDHHC5 (p value = 7.45 × 10− 10), C11ORF75 (p value = 3.70 × 10− 9). GSEA identified 80 gene sets with p values < 0.01. The top five significant gene sets were COWLING_MYCN_TARGETS (p value < 0.001) and CHR16P11 (p value < 0.001), ACTACCT_MIR196A_MIR196B (p value = 0.002), CELLULAR_COMPONENT_DISASSEMBLY (p value = 0.002) and GRAESSMANN_RESPONSE_TO_MC_AND_DOXORUBICIN_DN (p value = 0.002). Our results provide useful information for revealing the genetic basis of schizophrenia.