Inferring relationships among major psychiatric disorders in a resting-state functional connectivity-informed embedding space.

BackgroundThe genetic etiology of schizophrenia (SCZ) overlaps with that of other major psychiatric disorders in samples of European ancestry. On the other hand, these major psychiatric disorders are distinct diagnoses that have disorder-specific genetic factors. Recently, the bipolar disorder (BIP) and SCZ Working Group of the PGC identified two genome-wide significant loci differentiating the two disorders in individuals of European descent. We hypothesized that genetic variants differentiating SCZ from BIP in Europeans as well as genetic variants related to psychiatric disorders in Europeans would overlap with genetic risk variants in Japanese SCZ patients and unaffected first-degree relatives (FRs), i.e., individuals at high risk of developing SCZ. The present study investigated transethnic polygenetic features shared between Japanese SCZ or their unaffected FRs and European patients with major psychiatric disorders by conducting polygenic risk score (PRS) analyses.MethodsTo calculate PRSs for five psychiatric disorders [SCZ, BIP, major depressive disorder (MDD), autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD)] and PRSs differentiating SCZ from BIP, we utilized large-scale European genome-wide association study (GWAS) datasets as discovery samples. PRSs derived from these GWASs were calculated for 335 Japanese target subjects [131 SCZ patients, 57 of their unaffected FRs and 147 healthy controls (HCs)]. We took these PRSs based on GWASs of European psychiatric disorders (SCZ, BIP, SCZ vs BIP, MDD, ASD and ADHD) and investigated their effect on risk in Japanese SCZ patients [(i) SCZ vs FRs vs HCs, (ii) SCZ vs HCs and (iii) SCZ vs FRs] or unaffected FRs [(iv) FRs vs HCs] by PRS analyses.ResultsThe PRSs obtained from European SCZ samples were significantly higher in Japanese patients with SCZ than in HCs [(i) SCZ vs FRs vs HCs, a maximum at PT≤1.0: adjusted R2=0.028, p=1.30×10–3; (ii) SCZ vs HCs, a maximum at PT≤1.0: Nagelkerke’s R2=0.049, p=1.66×10–3]. In addition, the PRSs related to European BIP were nominally higher in Japanese patients with SCZ than in HCs [(i) SCZ vs FRs vs HCs, a maximum at PT≤0.5: adjusted R2=0.016, p=0.012; (ii) SCZ vs HCs, a maximum at PT≤0.5: Nagelkerke’s R2=0.029, p=0.015]. Furthermore, PRSs differentiating SCZ patients from European BIP patients were marginally higher in Japanese SCZ patients than in HCs [(i) SCZ vs FRs vs HCs, a maximum at PT≤0.05: adjusted R2=0.010, p=0.043; (ii) SCZ vs HCs, a maximum at PT≤0.05: Nagelkerke’s R2=0.020, p=0.046]. Interestingly, the PRSs obtained from European ASD were marginally lower in Japanese FRs compared with HCs [(iv) FRs vs HCs, a maximum at PT≤0.01: Nagelkerke’s R2=0.045, p=0.013] and patients with SCZ [(iii) SCZ vs FRs, a maximum at PT≤0.2: Nagelkerke’s R2=0.023, p=0.084]. As childhood-onset patients with SCZ have showed higher PRSs for both SCZ and ASD than their unaffected siblings, we further investigated the correlation between age at onset and PRSs for both SCZ and ASD in our SCZ samples. Lower age at onset of SCZ was significantly associated with higher PRSs for ASD (PT≤0.05: beta=-0.20, p=7.13×10–3) but not PRSs for SCZ (p>0.05).DiscussionThese findings suggest that polygenic factors related to European SCZ and BIP and the polygenic components differentiating SCZ from BIP can transethnically contribute to SCZ risk in Japanese people. Furthermore, we suggest that reduced levels of an ASD-related genetic factor in unaffected FRs may help protect against SCZ development.

Studies using genome-wide association (GWA) single nucleotide polymorphism (SNP) level data have indicated genetic overlap across the five major disorders in the Psychiatric Genomics Consortium (PGC): attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and schizophrenia (SCZ). However, such SNP-level analyses reveal little about the underlying biology and are reliant on correlated SNP effects across disorders. In contrast to SNPs, genes are more closely related to biology and gene-based tests can incorporate allelic heterogeneity. This study aimed to extend genetic overlap analysis across the five disorders from SNP level to gene level using a novel gene-based approach. Gene-based tests for association were performed using PGC GWA summary results for the five disorders in samples including 33,332 cases and 27,888 controls of European ancestry. After accounting for non-independence of gene-based test results, we determined whether the proportion of genes with association across multiple disorders was more than expected by chance. Similar to previous SNP-level analyses, we observed significant pairwise genetic overlap between ASD, BPD, MDD and SCZ. However, our approach also produced evidence for genetic overlap between ADHD and ASD, ADHD and BPD, and ADHD and MDD. Combining gene-based association results across disorders, 36 genes produced genome-wide significant P values (<3.2×10-6). Pathway analysis of genes with P values <1.0×10-3 highlighted magnesium ion binding and transport, as well as signal peptide processing, and provide insight into the biological mechanisms underlying these major psychiatric disorders.

Although large-scale neuroimaging studies have demonstrated similar patterns of structural brain abnormalities across major psychiatric disorders, the underlying genetic etiology behind these similar cross-disorder patterns is not well understood. We quantified the extent of shared genetic components between cortical structures and major psychiatric disorders (CS-MPD) by using genome-wide association study (GWAS) summary statistics of 70 cortical structures (surface area and thickness of the whole cortex and 34 cortical regions) and five major psychiatric disorders, consisting of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ). Cross-disorder analyses were then conducted to estimate the degree of similarity in CS-MPD shared genetic components among these disorders. The CS-MPD shared genetic components have medium-to-strong positive correlations in ADHD, BD, MDD, and SCZ (r = 0.415 to r = 0.806) while ASD was significantly correlated with ADHD, BD, and SCZ (r = 0.388 to r = 0.403). These pairwise correlations of CS-MPD shared genetic components among disorders were significantly associated with corresponding cross-disorder similarities in cortical structural abnormalities (r = 0.668), accounting for 44% variance. In addition, one latent shared factor consisted primarily of BD, MDD, and SCZ, explaining 62.47% of the total variance in CS-MPD shared genetic components of all disorders. The current results bridge the gap between shared cross-disorder heritability and shared structural brain abnormalities in major psychiatric disorders, providing important implications for a shared genetic basis of cortical structures in these disorders.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

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.

Major neuropsychiatric disorders are highly heritable, with mounting evidence suggesting that these disorders share overlapping sets of molecular and cellular underpinnings. In the current article we systematically test the degree of genetic commonality across six major neuropsychiatric disorders—attention deficit hyperactivity disorder (ADHD), anxiety disorders (Anx), autistic spectrum disorders (ASD), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ). We curated a well-vetted list of genes based on large-scale human genetic studies based on the NHGRI catalog of published genome-wide association studies (GWAS). A total of 180 genes were accepted into the analysis on the basis of low but liberal GWAS p-values (<10−5). 22% of genes overlapped two or more disorders. The most widely shared subset of genes—common to five of six disorders–included ANK3, AS3MT, CACNA1C, CACNB2, CNNM2, CSMD1, DPCR1, ITIH3, NT5C2, PPP1R11, SYNE1, TCF4, TENM4, TRIM26, and ZNRD1. Using a suite of neuroinformatic resources, we showed that many of the shared genes are implicated in the postsynaptic density (PSD), expressed in immune tissues and co-expressed in developing human brain. Using a translational cross-species approach, we detected two distinct genetic components that were both shared by each of the six disorders; the 1st component is involved in CNS development, neural projections and synaptic transmission, while the 2nd is implicated in various cytoplasmic organelles and cellular processes. Combined, these genetic components account for 20–30% of the genetic load. The remaining risk is conferred by distinct, disorder-specific variants. Our systematic comparative analysis of shared and unique genetic factors highlights key gene sets and molecular processes that may ultimately translate into improved diagnosis and treatment of these debilitating disorders.

BackgroundThe genetic etiology of schizophrenia (SCZ) overlaps with that of other major psychiatric disorders in samples of European ancestry. The present study investigated transethnic polygenetic features shared between Japanese SCZ or their unaffected first-degree relatives and European patients with major psychiatric disorders by conducting polygenic risk score (PRS) analyses.MethodsTo calculate PRSs for 5 psychiatric disorders (SCZ, bipolar disorder [BIP], major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder) and PRSs differentiating SCZ from BIP, we utilized large-scale European genome-wide association study (GWAS) datasets as discovery samples. PRSs derived from these GWASs were calculated for 335 Japanese target participants [SCZ patients, FRs, and healthy controls (HCs)]. We took these PRSs based on GWASs of European psychiatric disorders and investigated their effect on risk in Japanese SCZ patients and unaffected first-degree relatives.ResultsThe PRSs obtained from European SCZ and BIP patients were higher in Japanese SCZ patients than in HCs. Furthermore, PRSs differentiating SCZ patients from European BIP patients were higher in Japanese SCZ patients than in HCs. Interestingly, PRSs related to European autism spectrum disorder were lower in Japanese first-degree relatives than in HCs or SCZ patients. The PRSs of autism spectrum disorder were positively correlated with a young onset age of SCZ.ConclusionsThese findings suggest that polygenic factors related to European SCZ and BIP and the polygenic components differentiating SCZ from BIP can transethnically contribute to SCZ risk in Japanese people. Furthermore, we suggest that reduced levels of an ASD-related genetic factor in unaffected first-degree relatives may help protect against SCZ development.

Genomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

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. 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. 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). 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.

Systemic inflammatory regulators and 7 major psychiatric disorders: A two-sample Mendelian randomization study

Unraveling the causal pathways of maternal smoking and breastfeeding in the development of neuropsychiatric disorders: A Mendelian randomization perspective.

People with major psychiatric disorders (MPDs) have a 10- to 20-year shorter life span than the rest of the population, and this difference is mainly due to comorbid cardiovascular diseases. Genome-wide association studies have identified common variants involved in schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MD) and body mass index (BMI), a key cardiometabolic risk factor. However, genetic variants jointly influencing MPD and BMI remain largely unknown. To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them. Using a conditional false discovery rate statistical framework, independent genome-wide association study data on individuals with SCZ (n = 82 315), BIP (n = 51 710), MD (n = 480 359), and BMI (n = 795 640) were analyzed. The UK Biobank cohort (n = 29 740) was excluded from the MD data set to avoid sample overlap. Data were collected from August 2017 to May 2018, and analysis began July 2018. The primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways. Genome-wide association study data from 1 380 284 participants were analyzed, and the genetic correlation between BMI and MPDs varied (SCZ: r for genetic = -0.11, P = 2.1 × 10-10; BIP: r for genetic = -0.06, P = .0103; MD: r for genetic = 0.12, P = 6.7 × 10-10). Overall, 63, 17, and 32 loci shared between BMI and SCZ, BIP, and MD, respectively, were analyzed at conjunctional false discovery rate less than 0.01. Of the shared loci, 34% (73 of 213) in SCZ, 52% (36 of 69) in BIP, and 57% (56 of 99) in MD had risk alleles associated with higher BMI (conjunctional false discovery rate <0.05), while the rest had opposite directions of associations. Functional analyses indicated that the overlapping loci are involved in several pathways including neurodevelopment, neurotransmitter signaling, and intracellular processes, and the loci with concordant and opposite association directions pointed mostly to different pathways. In this genome-wide association study, extensive polygenic overlap between BMI and SCZ, BIP, and MD were found, and 111 shared genetic loci were identified, implicating novel functional mechanisms. There was mixture of association directions in SCZ and BMI, albeit with a preponderance of discordant ones.

There is great phenotypic heterogeneity within autism spectrum disorders (ASD), which has led to question their classification into a single diagnostic category. The study of the common genetic variation in ASD has suggested a greater contribution of other psychiatric conditions in Asperger syndrome (AS) than in the rest of the DSM-IV ASD subtypes (Non_AS). Here, using available genetic data from previously performed genome-wide association studies (GWAS), we aimed to study the genetic overlap between five of the most related disorders (schizophrenia (SCZ), major depression disorder (MDD), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorders (OCD) and anxiety (ANX)), and AS, comparing it with the overlap in Non_AS subtypes. A Spanish cohort of autism trios (N = 371) was exome sequenced as part of the Autism Sequencing Consortium (ASC) and 241 trios were extensively characterized to be diagnosed with AS following DSM-IV and Gillberg’s criteria (N = 39) or not (N = 202). Following exome imputation, polygenic risk scores (PRS) were calculated for ASD, SCZ, ADHD, MDD, ANX, and OCD (from available summary data from Psychiatric Genomic Consortium (PGC) repository) in the Spanish trios’ cohort. By using polygenic transmission disequilibrium test (pTDT), we reported that risk for SCZ (Pscz = 0.008, corrected-PSCZ = 0.0409), ADHD (PADHD = 0.021, corrected-PADHD = 0.0301), and MDD (PMDD = 0.039, corrected-PMDD = 0.0501) is over-transmitted to children with AS but not to Non_AS. Indeed, agnostic clustering procedure with deviation values from pTDT tests suggested two differentiated clusters of subjects, one of which is significantly enriched in AS (P = 0.025). Subsequent analysis with S-Predixcan, a recently developed software to predict gene expression from genotype data, revealed a clear pattern of correlation between cortical gene expression in ADHD and AS (P < 0.001) and a similar strong correlation pattern between MDD and AS, but also extendable to another non-brain tissue such as lung (P < 0.001). Altogether, these results support the idea of AS being qualitatively distinct from Non_AS autism and consistently evidence the genetic overlap between AS and ADHD, MDD, or SCZ.

Using big data of genetics, health claims, and brain imaging to challenge the categorical classification in mental illness

Psychiatric disorders are usually caused by the dysfunction of various brain regions. Incorporating the genetic information of brain regions into correlation analysis can provide novel clues for pathogenetic and therapeutic studies of psychiatric disorders. The latest genome-wide association study (GWAS) summary data of schizophrenia (SCZ), bipolar disorder (BIP), autism spectrum disorder (AUT), major depression disorder (MDD), and attention-deficit/hyperactivity disorder (ADHD) were obtained from the Psychiatric GWAS Consortium (PGC). The expression quantitative trait loci (eQTLs) datasets of 10 brain regions were driven from the genotype-tissue expression (GTEx) database. The PGC GWAS summaries were first weighted by the GTEx eQTLs summaries for each brain region. Linkage disequilibrium score regression was applied to the weighted GWAS summary data to detect genetic correlation for each pair of 5 disorders. Without considering brain region difference, significant genetic correlations were observed for BIP vs SCZ (P = 1.68 × 10-63), MDD vs SCZ (P = 5.08 × 10-45), ADHD vs MDD (P = 1.93 × 10-44), BIP vs MDD (P = 6.39 × 10-9), AUT vs SCZ (P = .0002), and ADHD vs SCZ (P = .0002). Utilizing brain region related eQTLs weighted LD score regression, different strengths of genetic correlations were observed within various brain regions for BIP vs SCZ, MDD vs SCZ, ADHD vs MDD, and SCZ vs ADHD. For example, the most significant genetic correlations were observed at anterior cingulate cortex (P = 1.13 × 10-34) for BIP vs SCZ. This study provides new clues for elucidating the mechanism of genetic correlations among various psychiatric disorders.