Integrated Post-GWAS Analysis Sheds New Light on the Disease Mechanisms of Schizophrenia

Neuronal alterations in AKT isotype expression in schizophrenia

Schizophrenia is characterized by substantial alterations in brain function, and previous studies suggest insulin signaling pathways, particularly involving AKT, are implicated in the pathophysiology of the disorder. This study demonstrates elevated mRNA expression of AKT1-3 in neurons from schizophrenia subjects, contrary to unchanged or diminished total AKT protein expression reported in previous postmortem studies, suggesting a potential decoupling of transcript and protein levels. Sex-specific differential AKT activity was observed, indicating divergent roles in males and females with schizophrenia. Alongside AKT, upregulation of PDPK1, a critical component of the insulin signaling pathway, and several protein phosphatases known to regulate AKT were detected. Moreover, enhanced expression of the transcription factor FOXO1, a regulator of glucose metabolism, hints at possible compensatory mechanisms related to insulin signaling dysregulation. Findings were largely independent of antipsychotic medication use, suggesting inherent alterations in schizophrenia. These results highlight the significance of AKT and related signaling pathways in schizophrenia, proposing that these changes might represent a compensatory response to a primary defect of conical insulin signaling pathways. This research underscores the need for a detailed understanding of these signaling pathways for the development of effective therapeutic strategies.

Toll-Like Receptor (TLR) 1, 2, and 6 Gene Polymorphisms Support Evidence of Innate Immune Factors in Schizophrenia

Introduction

Schizophrenia is a complex psychiatric disorder with an important genetic contribution. Immunological abnormalities have been reported in schizophrenia. Toll-like receptor (TLR) genes play an important role in the activation of the innate immune response, which may help to explain the presence of inflammation in people with this disorder. The aim of this study was to analyze the association of TLR1, TLR2, and TLR6 gene polymorphisms in the etiology of schizophrenia.

Methods

We included 582 patients with schizophrenia and 525 healthy controls. Genetic analysis was performed using allelic discrimination with TaqMan probes.

Results

We observed significant differences between patients and controls in the genotype and allele frequencies of TLR1/rs4833093 (χ2 = 17.3, p = 0.0002; χ2 = 15.9, p = 0.0001, respectively) and TLR2/rs5743709 (χ2 = 29.5, p = 0.00001; χ2 = 7.785, p = 0.0053, respectively), and in the allele frequencies of TLR6/rs3775073 (χ2 = 31.1, p = 0.00001). Finally, we found an interaction between the TLR1/rs4833093 and TLR2/rs5743709 genes, which increased the risk of developing schizophrenia (OR = 2.29, 95% CI [1.75, 3.01]).

Discussion

Our findings add to the evidence suggesting that the activation of innate immune response might play an important role in the development of schizophrenia.

Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome

BACKGROUND

22q11.2 Deletion Syndrome (22q11DS) is a genetic disorder characterized by the deletion of adjacent genes at a location specified as q11.2 of chromosome 22, resulting in an array of clinical phenotypes including autistic spectrum disorder, schizophrenia, congenital heart defects, and immune deficiency. Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood.

RESULTS

Using previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different.

CONCLUSIONS

Using the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.

SMAD genes are up-regulated in brain and blood samples of individuals with schizophrenia

Schizophrenia is a severe psychiatric disorder, with heritability around 80%, but a not fully understood pathophysiology. Signal transduction through the mothers against decapentaplegic (SMADs) are eight different proteins involved in the regulation of inflammatory processes, cell cycle, and tissue patterning. The literature is not consistent regarding the differential expression of SMAD genes among subjects with schizophrenia. In this article, we performed a systematic meta-analysis of the expression of SMAD genes in 423 brain samples (211 schizophrenia vs. 212 healthy controls), integrating 10 datasets from two public repositories, following the PRISMA guidelines. We found a statistically significant up-regulation of SMAD1, SMAD4, SMAD5, and SMAD7, and a tendency for up-regulation of SMAD3 and SMAD9 in brain samples of patients with schizophrenia. Overall, six of the eight genes showed a tendency for up-regulation, and none of them was found to have a tendency for down-regulation. SMAD1 and SMAD4 were up-regulated also in blood samples of 13 individuals with schizophrenia versus eight healthy controls, suggesting the SMAD genes' potential role as biomarkers of schizophrenia. Furthermore, SMAD genes' expression levels were significantly correlated with those of Sphingosine-1-phosphate receptor-1 (S1PR1), which is known to regulate inflammatory processes. Our meta-analysis supports the involvement of SMAD genes in the pathophysiology of schizophrenia through their role in inflammatory processes, as well as demonstrates the importance of gene expression meta-analysis for improving our understanding of psychiatric diseases.

Inflammation subtypes in psychosis and their relationships with genetic risk for psychiatric and cardiometabolic disorders

Cardiometabolic disorders have known inflammatory implications, and peripheral measures of inflammation and cardiometabolic disorders are common in persons with psychotic disorders. Inflammatory signatures are also related to neurobiological and behavioral changes in psychosis. Relationships between systemic inflammation and cardiometabolic genetic risk in persons with psychosis have not been examined. Thirteen peripheral inflammatory markers and genome-wide genotyping were assessed in 122 participants (n ​= ​86 psychosis, n ​= ​36 healthy controls) of European ancestry. Cluster analyses of inflammatory markers classified higher and lower inflammation subgroups. Single-trait genetic risk scores (GRS) were constructed for each participant using previously reported GWAS summary statistics for the following traits: schizophrenia, bipolar disorder, major depressive disorder, coronary artery disease, type-2 diabetes, low-density lipoprotein, high-density lipoprotein, triglycerides, and waist-to-hip ratio. Genetic correlations across traits were quantified. Principal component (PC) analysis of the cardiometabolic GRSs generated six PC loadings used in regression models to examine associations with inflammation markers. Functional module discovery explored biological mechanisms of the inflammation association of cardiometabolic GRS genes. A subgroup of 38% persons with psychotic disorders was characterized with higher inflammation status. These higher inflammation individuals had lower BACS scores (p ​= ​0.038) compared to those with lower inflammation. The first PC of the cardiometabolic GRS matrix was related to higher inflammation status in persons with psychotic disorders (OR ​= ​2.037, p ​= ​0.001). Two of eight modules within the functional interaction network of cardiometabolic GRS genes were enriched for immune processes. Cardiometabolic genetic risk may predispose some individuals with psychosis to elevated inflammation which adversely impacts cognition associated with illness.

Identification of ultra-rare genetic variants in pediatric acute onset neuropsychiatric syndrome (PANS) by exome and whole genome sequencing

Abrupt onset of severe neuropsychiatric symptoms including obsessive-compulsive disorder, tics, anxiety, mood swings, irritability, and restricted eating is described in children with Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS). Symptom onset is often temporally associated with infections, suggesting an underlying autoimmune/autoinflammatory etiology, although direct evidence is often lacking. The pathological mechanisms are likely heterogeneous, but we hypothesize convergence on one or more biological pathways. Consequently, we conducted whole exome sequencing (WES) on a U.S. cohort of 386 cases, and whole genome sequencing (WGS) on ten cases from the European Union who were selected because of severe PANS. We focused on identifying potentially deleterious genetic variants that were de novo or ultra-rare (MAF) < 0.001. Candidate mutations were found in 11 genes (PPM1D, SGCE, PLCG2, NLRC4, CACNA1B, SHANK3, CHK2, GRIN2A, RAG1, GABRG2, and SYNGAP1) in 21 cases, which included two or more unrelated subjects with ultra-rare variants in four genes. These genes converge into two broad functional categories. One regulates peripheral immune responses and microglia (PPM1D, CHK2, NLRC4, RAG1, PLCG2). The other is expressed primarily at neuronal synapses (SHANK3, SYNGAP1, GRIN2A, GABRG2, CACNA1B, SGCE). Mutations in these neuronal genes are also described in autism spectrum disorder and myoclonus-dystonia. In fact, 12/21 cases developed PANS superimposed on a preexisting neurodevelopmental disorder. Genes in both categories are also highly expressed in the enteric nervous system and the choroid plexus. Thus, genetic variation in PANS candidate genes may function by disrupting peripheral and central immune functions, neurotransmission, and/or the blood-CSF/brain barriers following stressors such as infection.

Treatment Response and GWAS Risk Allele rs2514218 (C) of the Dopamine D2 Receptor Gene in Inpatients with Schizophrenia

INTRODUCTION

The pathophysiological mechanisms of acute schizophrenia are largely unknown, but it is widely accepted that dopamine D2 receptors (DRD2s) are involved in psychosis treatments for schizophrenic patients. We suggest that genetic variation in these receptors may play a role in patients' responses to commonly used antipsychotics, particularly D2-blockers.

METHODS

This study included adult patients with ICD-10 diagnoses of schizophrenia and current acute psychosis who were treated with antipsychotics. All patients underwent genotyping for DRD2 rs2514218 polymorphism. The definition of overall treatment response was based on changes in treatment scheme: no changes indicated a good response, and changes indicated a limited response.

RESULTS

There were 275 inpatients (38.1% of whom were female; mean age = 32.7 years, SD = 11.1 years) who met the inclusion criteria. Of the participants, 99 were good responders (34% of whom were female), and 176 were limited responders (40% of whom were female). No differences in demographic, premorbid, or disease characteristics were found. The number of patients that were homozygous for the risk allele was significantly greater in the limited response group than in the good response group.

CONCLUSION

Our findings suggest that the risk variant at the DRD2 locus can be used as an indicator for patients' responses to antipsychotics without direct DRD2-blocking, thereby shortening the time needed for drug selection.

Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology

Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.

Inflammation Subtypes and Translating Inflammation-Related Genetic Findings in Schizophrenia and Related Psychoses: A Perspective on Pathways for Treatment Stratification and Novel Therapies

Dysregulation of immunological and inflammatory processes is frequently observed in psychotic disorders. Numerous studies have examined the complex components of innate and adaptive immune processes in schizophrenia and related psychoses. Elevated inflammation in these conditions is related to neurobiological phenotypes and associated with both genetics and environmental exposures. Recent studies have utilized multivariate cytokine approaches to identify what appears to be a subset of individuals with elevated inflammation. The degree to which these findings represent a general process of dysregulated inflammation or whether there are more refined subtypes remains unclear. Brain-imaging studies have attempted to establish the link between peripheral inflammation and gray matter disruption, white matter abnormalities, and neuropsychological phenotypes. However, the interplay between peripheral inflammation and neuroinflammation, as well as the consequences of this interplay, in the context of psychosis remains unclear and requires further investigation. This Perspectives article reviews the following elements of immune dysregulation and its clinical and therapeutic implications: (1) evidence supporting inflammation and immune dysregulation in schizophrenia and related psychoses; (2) recent advances in approaches to characterizing subgroups of patients with elevated inflammation; (3) relationships between peripheral inflammation and brain-imaging indicators of neuroinflammation; (4) convergence of large-scale genetic findings and peripheral inflammation findings; and (5) therapeutic implications: anti-inflammation interventions leveraging genetic findings for drug discovery and repurposing. We offer perspectives and examples of how multiomics technologies may be useful for constructing and studying immunogenetic signatures. Advancing research in this area will facilitate biomarker discovery, disease subtyping, and the development of etiological treatments for immune dysregulation in psychosis.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

Deep post-GWAS analysis identifies potential risk genes and risk variants for Alzheimer's disease, providing new insights into its disease mechanisms

Alzheimer’s disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the risk variants underlying GWAS AD-association signals remain unknown. Here, we describe a deep post-GWAS analysis of AD-associated variants, using an integrated computational framework for predicting both disease genes and their risk variants. We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS collected in GWAS catalogs, and 115 of 342 AD risk genes are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 risk variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%) of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms—e.g., the complement and coagulation cascade and phosphorylation and activation of immune response—and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes—e.g., IL1RAP, PMAIP1, LAMTOR4—as predictors for the prognosis of AD survival and genes such as ARL6IP5 with altered network connectivity between AD patients and normal individuals involved in AD progression. Our findings open new strategies for developing therapeutics targeting AD risk genes or risk variants to influence AD pathogenesis.

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways

MOTIVATION

Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases.

RESULTS

We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes.

AVAILABILITY

DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.

Chronic methamphetamine interacts with BDNF Val66Met to remodel psychosis pathways in the mesocorticolimbic proteome

Methamphetamine (Meth) abuse has reached epidemic proportions in many countries and can induce psychotic episodes mimicking the clinical profile of schizophrenia. Brain-derived neurotrophic factor (BDNF) is implicated in both Meth effects and schizophrenia. We therefore studied the long-term effects of chronic Meth exposure in transgenic mice engineered to harbor the human BDNF^Val66Met polymorphism expressed via endogenous mouse promoters. These mice were chronically treated with an escalating Meth regime during late adolescence. At least 4 weeks later, all hBDNF^Val66Met Meth-treated mice exhibited sensitization confirming persistent behavioral effects of Meth. We used high-resolution quantitative mass spectrometry-based proteomics to biochemically map the long-term effects of Meth within the brain, resulting in the unbiased detection of 4808 proteins across the mesocorticolimbic circuitry. Meth differentially altered dopamine signaling markers (e.g., Dat, Comt, and Th) between hBDNF^Val/Val and hBDNF^Met/Met mice, implicating involvement of BDNF in Meth-induced reprogramming of the mesolimbic proteome. Targeted analysis of 336 schizophrenia-risk genes, as well as 82 growth factor cascade markers, similarly revealed that hBDNF^Val66Met genotype gated the recruitment of these factors by Meth in a region-specific manner. Cumulatively, these data represent the first comprehensive analysis of the long-term effects of chronic Meth exposure within the mesocorticolimbic circuitry. In addition, these data reveal that long-term Meth-induced brain changes are strongly dependent upon BDNF genetic variation, illustrating how drug-induced psychosis may be modulated at the molecular level by a single genetic locus.

Strategies to solve the reverse inference fallacy in future MRI studies of schizophrenia: a review

Few advances in schizophrenia research have been translated into clinical practice, despite 60 years of serum biomarkers studies and 50 years of genetic studies. During the last 30 years, neuroimaging studies on schizophrenia have gradually increased, partly due to the beautiful prospect that the pathophysiology of schizophrenia could be explained entirely by the Human Connectome Project (HCP). However, the fallacy of reverse inference has been a critical problem of the HCP. For this reason, there is a dire need for new strategies or research "bridges" to further schizophrenia at the biological level. To understand the importance of research "bridges," it is vital to examine the strengths and weaknesses of the recent literature. Hence, in this review, our team has summarized the recent literature (1995-2018) about magnetic resonance imaging (MRI) of schizophrenia in terms of regional and global structural and functional alterations. We have also provided a new proposal that may supplement the HCP for studying schizophrenia. As postulated, despite the vast number of MRI studies in schizophrenia, the lack of homogeneity between the studies, along with the relatedness of schizophrenia with other neurological disorders, has hindered the study of schizophrenia. In addition, the reverse inference cannot be used to diagnose schizophrenia, further limiting the clinical impact of findings from medical imaging studies. We believe that multidisciplinary technologies may be used to develop research "bridges" to further investigate schizophrenia at the single neuron or neuron cluster levels. We have postulated about future strategies for overcoming the current limitations and establishing the research "bridges," with an emphasis on multimodality imaging, molecular imaging, neuron cluster signals, single transmitter biomarkers, and nanotechnology. These research "bridges" may help solve the reverse inference fallacy and improve our understanding of schizophrenia for future studies.

Impaired regulatory T cell control of astroglial overdrive and microglial pruning in schizophrenia

It is widely held that schizophrenia involves an active process of peripheral inflammation that induces or reflects brain inflammation with activation of microglia, the brain's resident immune cells. However, recent in vivo radioligand binding studies and large-scale transcriptomics in post-mortem brain report reduced markers of microglial inflammation. The findings suggest a contrary hypothesis; that microglia are diverted into their non-inflammatory synaptic remodelling phenotype that interferes with neurodevelopment and perhaps contributes to the relapsing nature of schizophrenia. Recent discoveries on the regulatory interactions between micro- and astroglial cells and immune regulatory T cells (Tregs) cohere with clinical omics data to suggest that: i) disinhibited astrocytes mediate the shift in microglial phenotype via the production of transforming growth factor-beta, which also contributes to the disturbances of dopamine and GABA function in schizophrenia, and ii) systemically impaired functioning of Treg cells contributes to the dysregulation of glial function, the low-grade peripheral inflammation, and the hitherto unexplained predisposition to auto-immunity and reduced life-expectancy in schizophrenia, including greater COVID-19 mortality.

Core transcriptional networks in Williams syndrome: IGF1-PI3K-AKT-mTOR, MAPK and actin signaling at the synapse echo autism

Gene networks for disorders of social behavior provide the mechanisms critical for identifying therapeutic targets and biomarkers. Large behavioral phenotypic effects of small human deletions make the positive sociality of Williams syndrome (WS) ideal for determining transcriptional networks for social dysfunction currently based on DNA variations for disorders such as autistic spectrum disorder (ASD) and schizophrenia (SCHZ). Consensus on WS networks has been elusive due to the need for larger cohort size, sensitive genome-wide detection and analytic tools. We report a core set of WS network perturbations in a cohort of 58 individuals (34 with typical, 6 atypical deletions and 18 controls). Genome-wide exon-level expression arrays robustly detected changes in differentially expressed gene (DEG) transcripts from WS deleted genes that ranked in the top 11 of 12 122 transcripts, validated by quantitative reverse transcription PCR, RNASeq and western blots. WS DEG's were strictly dosed in the full but not the atypical deletions that revealed a breakpoint position effect on non-deleted CLIP2, a caveat for current phenotypic mapping based on copy number variants. Network analyses tested the top WS DEG's role in the dendritic spine, employing GeneMANIA to harmonize WS DEGs with comparable query gene-sets. The results indicate perturbed actin cytoskeletal signaling analogous to the excitatory dendritic spines. Independent protein-protein interaction analyses of top WS DEGs generated a 100-node graph annotated topologically revealing three interacting pathways, MAPK, IGF1-PI3K-AKT-mTOR/insulin and actin signaling at the synapse. The results indicate striking similarity of WS transcriptional networks to genome-wide association study-based ASD and SCHZ risk suggesting common network dysfunction for these disorders of divergent sociality.

Inherited L1 Retrotransposon Insertions Associated With Risk for Schizophrenia and Bipolar Disorder

Studies of the genetic heritability of schizophrenia and bipolar disorder examining single nucleotide polymorphisms (SNPs) and copy number variations have failed to explain a large portion of the genetic liability, resulting in substantial missing heritability. Long interspersed element 1 (L1) retrotransposons are a type of inherited polymorphic variant that may be associated with risk for schizophrenia and bipolar disorder. We performed REBELseq, a genome wide assay for L1 sequences, on DNA from male and female persons with schizophrenia and controls (n = 63 each) to identify inherited L1 insertions and validated priority insertions. L1 insertions of interest were genotyped in DNA from a replication cohort of persons with schizophrenia, bipolar disorder, and controls (n = 2268 each) to examine differences in carrier frequencies. We identified an inherited L1 insertion in ARHGAP24 and a quadallelic SNP (rs74169643) inside an L1 insertion in SNTG2 that are associated with risk for developing schizophrenia and bipolar disorder (all odds ratios ~1.2). Pathway analysis identified 15 gene ontologies that were differentially affected by L1 burden, including multiple ontologies related to glutamatergic signaling and immune function, which have been previously associated with schizophrenia. These findings provide further evidence supporting the role of inherited repetitive genetic elements in the heritability of psychiatric disorders.

Common variants in signaling transcription-factor-binding sites drive phenotypic variability in red blood cell traits

Genome-wide association studies identify genomic variants associated with human traits and diseases. Most trait-associated variants are located within cell-type-specific enhancers, but the molecular mechanisms governing phenotypic variation are less well understood. Here, we show that many enhancer variants associated with red blood cell (RBC) traits map to enhancers that are co-bound by lineage-specific master transcription factors (MTFs) and signaling transcription factors (STFs) responsive to extracellular signals. The majority of enhancer variants reside on STF and not MTF motifs, perturbing DNA binding by various STFs (BMP/TGF-β-directed SMADs or WNT-induced TCFs) and affecting target gene expression. Analyses of engineered human blood cells and expression quantitative trait loci verify that disrupted STF binding leads to altered gene expression. Our results propose that the majority of the RBC-trait-associated variants that reside on transcription-factor-binding sequences fall in STF target sequences, suggesting that the phenotypic variation of RBC traits could stem from altered responsiveness to extracellular stimuli.

Genetics of extreme human longevity to guide drug discovery for healthy ageing

Ageing is the greatest risk factor for most common chronic human diseases, and it therefore is a logical target for developing interventions to prevent, mitigate or reverse multiple age-related morbidities. Over the past two decades, genetic and pharmacologic interventions targeting conserved pathways of growth and metabolism have consistently led to substantial extension of the lifespan and healthspan in model organisms as diverse as nematodes, flies and mice. Recent genetic analysis of long-lived individuals is revealing common and rare variants enriched in these same conserved pathways that significantly correlate with longevity. In this Perspective, we summarize recent insights into the genetics of extreme human longevity and propose the use of this rare phenotype to identify genetic variants as molecular targets for gaining insight into the physiology of healthy ageing and the development of new therapies to extend the human healthspan.

An integration analysis based on genomic, transcriptomic and QTX information reveals credible candidate genes for fat-related traits in pigs

Meat quality improvement is of great interest to researchers in pig breeding and many researchers have identified abundant associated quantitative trait loci, genes and polymorphisms (QTXs) for fat-related traits. However, it is challenging to determine credible candidate genes from a mass of associations. The efficiency of identification of credible candidate genes in these QTXs is restricted by limited integration analyses of data from multiple omics. In this study, we constructed a 'candidate gene map' of fat-related traits in pigs based on published literature and the latest genome. In total, 6,861 QTXs, which covered 9,323 genes on the pig genome, were used. Combining the QTX hotspots and pathway analysis, we identified 180 candidate genes that may regulate the fat-related traits, and choose PNPLA2, PPARG, SREBF1, ACACA, PPARD and PPARA as credible candidate genes. In addition, we discussed the importance of incorporating transcriptome data and genomic data in causal gene identification, and the multi-omics information can effectively improve the credibility of identified candidate genes.

Shared Genetic Loci Between Body Mass Index and Major Psychiatric Disorders: A Genome-wide Association Study

Importance

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.

Objective

To assess the extent of the overlap between the genetic architectures of MPDs and BMI and identify genetic loci shared between them.

Design, Setting, and Participants

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.

Main Outcomes and Measures

The primary outcomes were a list of genetic loci shared between BMI and MPDs and their functional pathways.

Results

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.

Conclusions and Relevance

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.

The association between schizophrenia and the immune system: Review of the evidence from unbiased 'omic-studies'

A role for immune processes in the pathogenesis of schizophrenia has been suggested by genetic and epidemiological studies, as well as cross-sectional studies on blood and brain samples. However, results are heterogeneous, which is likely caused by low samples sizes, insufficient control of confounders that influence immune processes, and potentially publication bias. Large hypothesis-free 'omic' studies partially circumvent these problems and could provide further evidence for a role of immune pathways in schizophrenia. In this review we assessed whether the largest genome, transcriptome and methylome studies in schizophrenia to date support a link with the immune system. We constructed an overview of the schizophrenia-associated genes and transcripts that were identified in these large 'omic' studies. We then performed a hypothesis-driven analysis to examine the association and enrichment of immune system-related genes and transcripts in these datasets. Additionally, we reviewed secondary analyses that were previously performed on these 'omic' studies. Except for the link between complement factor 4 (C4), we found limited evidence for a role of microglia and immune processes among genetic risk variants. Transcriptome and methylome studies point towards alterations in immune system related genes, pathways and cells. This includes changes in microglia, as well as complement, nuclear factor-κB, toll-like receptor and interferon signaling pathways. Many of these associated immune-related genes and pathways have been shown to be involved in neurodevelopment and neuronal functioning. Additional replication of these findings is needed, but once further conformation is provided, these findings could be a potentially interesting target for future therapies.

Why is Disease Penetration So Variable? Role of Genetic Modifiers of Lung Function in Alpha-1 Antitrypsin Deficiency

Individuals with alpha-1 antitrypsin deficiency (AATD) have marked heterogeneity in lung function, suspected to be related to a combination of both environmental (e.g., cigarette smoking) and genetic factors. Lung function is heritable in the general population and in persons with severe AATD. Several genetic modifiers of lung function in persons with AATD have been described; however, replication is lacking. A genome-wide association study (GWAS) of lung function in persons with AATD has yet to be performed and may inform whether genetic determinants of lung function are overlapping in persons with AATD and in the general population. As GWASs require large sample sizes for adequate power, genetic risk scores offer an alternate approach to assess the overlap of genetic determinants of lung function in the general population in persons with AATD. Where GWASs are limited to common genetic variant discovery, whole genome sequencing (for rare variant discovery) and integrative genomic studies (examining the influence of genetic variants on gene, protein, and metabolite levels) offer potential for an expanded discovery of genetic modifiers of lung function in AATD. In the following review we examine past descriptions of genetic modifiers of lung function in AATD and describe a path forward to further investigate and define the likely genetic modifiers of lung function in AATD.

Comparative analysis of cellular expression pattern of schizophrenia risk genes in human versus mouse cortex

Background

Schizophrenia is a common psychiatric disease with high hereditary. The identification of schizophrenia risk genes (SRG) has shed light on its pathophysiological mechanisms. Mouse genetic models have been widely used to study the function of SRG in the brain with a cell type specific fashion. However, whether the cellular expression pattern of SRG is conserved between human and mouse brain is not thoroughly studied.

Results

We analyzed the single-cell transcription of 180 SRG from human and mouse primary visual cortex (V1). We compared the percentage of glutamatergic, GABAergic and non-neuronal cells that express each SRG between mouse and human V1 cortex. Thirty percent (54/180) of SRG had significantly different expression rate in glutamatergic neurons between mouse and human V1 cortex. By contrast, only 5.6% (10/180) of SRG showed significantly different expression in GABAergic neurons, which is similar with the ratio of SRG (15/180) with species difference in total cell populations. Strikingly, the percentage of non-neuronal cells expressing all SRG are indistinguishable between human and mouse V1 cortex. We further analyzed the biological significance of differentially expressed SRG by gene ontology. The species-different SRG in glutamatergic neurons are highly expressed in dendrite and axon. They are enriched in the biological process of response to stimulus. However, the differentially expressed SRG in GABAergic neurons are enriched in the regulation of organelle organization.

Conclusion

GABAergic neurons are more conserved in the expression of SRG than glutamatergic neurons while the non-neuronal cells show the species conservation for the expression of all SRG. It should be cautious to use mouse models to study those SRG which show different cellular expression pattern between human and mouse cortex.

Effects of a GWAS-Supported Schizophrenia Variant in the DRD2 Locus on Disease Risk, Anhedonia, and Prefrontal Cortical Thickness

The study aimed to confirm the association of the schizophrenia genome-wide association study (GWAS) hit rs2514218 located near the DRD2 gene with the risk of the disease and to investigate the relationships between rs2514218 and schizophrenia-related clinical and neuroimaging phenotypes. Genotypes at the rs2514218 site were determined for 2148 schizophrenia spectrum patients and 1273 control subjects from the Russian population. In subsets of subjects, we assessed symptomatic dimensions using the Positive and Negative Syndrome Scale (n = 1651) and Temporal Experience of Pleasure Scale (n = 471). At the brain level, gray matter volumes in striatal structures and cortical thickness in the lateral prefrontal cortical regions were investigated (n = 97). Genotype frequencies did not differ between patients and controls. The allelic association analysis yielded a near-threshold p value (p = 0.054), the magnitude (OR = 0.90), and direction of the minor allele (T) effect being in accord with those in the schizophrenia GWAS. Also, patients homozygous for the risk allele C had more severe consummatory anhedonia and a thinner cortex than controls and patients carrying the T allele. The largest effect size of the genotype with diagnosis interaction was seen in the right pars opercularis area. The findings support the role of rs2514218 in schizophrenia risk and presentation and suggest rs2514218 has an influence on brain morphology and negative symptoms.

Host and Microbiome Genome-Wide Association Studies: Current State and Challenges

The involvement of the microbiome in health and disease is well established. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome. The emergence of this relatively new field has been facilitated by the advent of next generation sequencing technologies that enable the investigation of the complex interaction between host genetics and microbial communities. In this paper, we review recent studies investigating host-microbiome interactions using mGWAS. Additionally, we highlight the marked disparity in the sampling population of mGWAS carried out to date and draw attention to the critical need for inclusion of diverse populations.

A large-scale integrative analysis of GWAS and common meQTLs across whole life course identifies genes, pathways and tissue/cell types for three major psychiatric disorders

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 × 10^-3 for BP and 2.49 × 10^-6 for SCZ), ST3GAL3 (P = 1.19 × 10^-2 for ADHD), and KEGG_MAPK_SIGNALING_PATHWAY (P = 1.56 × 10^-3 for ADHD, P = 4.71 × 10^-2 for BP, P = 4.60 × 10^-4 for SCZ). Our study provides novel clues for understanding the genetic mechanism of ADHD, BP and SCZ.

Genetics of Alcohol Use Disorder: A Role for Induced Pluripotent Stem Cells?

Alcohol use disorder (AUD) affects millions of people and costs nearly 250 billion dollars annually. Few effective FDA-approved treatments exist, and more are needed. AUDs have a strong heritability, but only a few genes have been identified with a large effect size on disease phenotype. Genomewide association studies (GWASs) have identified common variants with low effect sizes, most of which are in noncoding regions of the genome. Animal models frequently fail to recapitulate key molecular features of neuropsychiatric disease due to the polygenic nature of the disease, partial conservation of coding regions, and significant disparity in noncoding regions. By contrast, human induced pluripotent stem cells (hiPSCs) derived from patients provide a powerful platform for evaluating genes identified by GWAS and modeling complex interactions in the human genome. hiPSCs can be differentiated into a wide variety of human cells, including neurons, glia, and hepatic cells, which are compatible with numerous functional assays and genome editing techniques. In this review, we focus on current applications and future directions of patient hiPSC-derived central nervous system cells for modeling AUDs in addition to highlighting successful applications of hiPSCs in polygenic neuropsychiatric diseases.

COMT and GAD1 gene polymorphisms are associated with impaired antisaccade task performance in schizophrenic patients

Genetic influences modulating executive functions engaging prefrontal cortical brain systems were investigated in 141 male subjects. The effects of variations in two genes implicated in dopamine and GABA activities in the prefrontal cortex: rs4680 (Val158/Met polymorphism of the catechol-o-methyltransferase gene-COMT) and rs3749034 (C/T) substitution in the promoter region of the glutamic acid decarboxylase gene (GAD1) were studied on antisaccade (AS) performance in healthy subjects and schizophrenic patients. Genotyping revealed a trend towards a reduced proportion of COMT Val/Met heterozygotes and a significantly increased frequency of the GAD1 rs3749034 C allele in schizophrenic patients relative to controls. Patients had elevated error rates, increased AS latencies and increased latency variability (coefficient of variation) compared to controls. The influence of polymorphisms was observed only in patients but not in controls. A substantial effect of the COMT genotype was noted on the coefficient of variation in latency, and this measure was higher in Val homozygotes compared to Met allele carriers (p < 0.05) in the patient group. The outcome from rs3749034 was also disclosed on the error rate (higher in T carriers relative to C homozygotes, p < 0.01) and latency (increased in C homozygotes relative to T carriers, p < 0.01). Binary logistic regression showed that inclusion of the genotype factor (i.e., selective estimation of antisaccade measures in CC carriers) considerably increased the validity of the diagnostic model based on the AS measures. These findings may well be derived from specific genetic associations with prefrontal cortex functioning in schizophrenia.

The Emerging Immunogenetic Architecture of Schizophrenia

Schizophrenia is a severe psychiatric disorder of complex etiology. Immune processes have long been proposed to contribute to the development of schizophrenia, and accumulating evidence supports immune involvement in at least a subset of cases. In recent years, large-scale genetic studies have provided new insights into the role of the immune system in this disease. Here, we provide an overview of the immunogenetic architecture of schizophrenia based on findings from genome-wide association studies (GWAS). First, we review individual immune loci identified in secondary analyses of GWAS, which implicate over 30 genes expressed in both immune and brain cells. The function of the proteins encoded by these immune candidates highlight the role of the complement system, along with regulation of apoptosis in both immune and neuronal cells. Next, we review hypothesis-free pathway analyses which have so far been inconclusive with respect to identifying immune pathways involved in schizophrenia. Finally, we explore the genetic overlap between schizophrenia and immune-mediated diseases. Although there have been some inconsistencies across studies, genome-wide pleiotropy has been reported between schizophrenia and Crohn's disease, multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, type 1 diabetes, and ulcerative colitis. Overall, there are multiple lines of evidence supporting the role of immune genes in schizophrenia. Current evidence suggests that specific immune pathways are involved-likely those with dual functions in the central nervous system. Future studies focused on further elucidating the relevant pathways hold the potential to identify novel biomarkers and therapeutic targets for schizophrenia.

PGA: post-GWAS analysis for disease gene identification

Summary

Although the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question.

Availability and implementation

http://zdzlab.einstein.yu.edu/1/pga.html.

Contact

zhengdong.zhang@einstein.yu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies

Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies. Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases. Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies. Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times. In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2 deletion syndrome patients. These variants were missed by a conventional approach that relied on the rare variant association test alone.

Case-control sequencing studies are a promising design to uncover risk genes of human complex diseases implicated by rare variants. The recent development of different types of rare variant association tests has improved the statistical power to identify disease genes that harbor risk rare variants. However, none of the recent sequencing-based genome-wide association studies identified robust disease association of rare variants or genes based on them. Due to limited sample sizes that can be feasibly achieved in real applications, current rare variant association tests can only generate marginal association signals for most risk genes. Here we proposed an integrated method that combined association signals with orthogonal biological evidence to uncover risk genes in sequencing studies. Designed to address the lack-of-power issue, our method was shown to effectively uncover risk genes with marginal association signals in data simulation. Indeed, in a real application demonstrated in our case study our method disclosed important risk genes of congenital heart disease in 22q11.2 deletion syndrome that were missed by the previous study.

A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium

Rare copy number variants contribute significantly to the risk for schizophrenia, with the 22q11.2 locus consistently implicated. Individuals with the 22q11.2 deletion syndrome (22q11DS) have an estimated 25-fold increased risk for schizophrenia spectrum disorders, compared to individuals in the general population. The International 22q11DS Brain Behavior Consortium is examining this highly informative neurogenetic syndrome phenotypically and genomically. Here we detail the procedures of the effort to characterize the neuropsychiatric and neurobehavioral phenotypes associated with 22q11DS, focusing on schizophrenia and subthreshold expression of psychosis. The genomic approach includes a combination of whole-genome sequencing and genome-wide microarray technologies, allowing the investigation of all possible DNA variation and gene pathways influencing the schizophrenia-relevant phenotypic expression. A phenotypically rich data set provides a psychiatrically well-characterized sample of unprecedented size (n=1616) that informs the neurobehavioral developmental course of 22q11DS. This combined set of phenotypic and genomic data will enable hypothesis testing to elucidate the mechanisms underlying the pathogenesis of schizophrenia spectrum disorders.