Novel Insight Into the Etiology of Autism Spectrum Disorder Gained by Integrating Expression Data With Genome-wide Association Statistics

Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders

N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.

Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction

Introduction

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates common genetic association results from the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

Methods

Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival.

Results

SNP-based fine-mapping, TWAS and PWAS identified 118 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified six drugs significantly enriched for interactions with ALS associated genes, though directionality could not be determined. Additionally, drug class enrichment analysis showed gene signatures linked to calcium channel blockers may reduce ALS risk, whereas antiepileptic drugs may increase ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R 2 = 5.1 %; p-value = 3.2 × 10-27) and clinical characteristics.

Conclusions

Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which current treatments are limited and drug development costs are prohibitive. Identifying drug targets for ASD is crucial for the development of targeted therapies. Summary-level data of expression quantitative trait loci obtained from GTEx, protein quantitative trait loci data from the ROSMAP project, and two ASD genome-wide association studies datasets were utilized for discovery and replication. We conducted a combined analysis using Mendelian randomization (MR), transcriptome-wide association studies, Bayesian colocalization, and summary-data-based MR to identify potential therapeutic targets associated with ASD and examine whether there are shared causal variants among them. Furthermore, pathway and drug enrichment analyses were performed to further explore the underlying mechanisms and summarize the current status of pharmacological targets for developing drugs to treat ASD. The protein-protein interaction (PPI) network and mouse knockout models were performed to estimate the effect of therapeutic targets. A total of 17 genes revealed causal associations with ASD and were identified as potential targets for ASD patients. Cathepsin B (CTSB) [odd ratio (OR) = 2.66 95, confidence interval (CI): 1.28-5.52, P = 8.84 × 10-3], gamma-aminobutyric acid type B receptor subunit 1 (GABBR1) (OR = 1.99, 95CI: 1.06-3.75, P = 3.24 × 10-2), and formin like 1 (FMNL1) (OR = 0.15, 95CI: 0.04-0.58, P = 5.59 × 10-3) were replicated in the proteome-wide MR analyses. In Drugbank, two potential therapeutic drugs, Acamprosate (GABBR1 inhibitor) and Bryostatin 1 (CASP8 inhibitor), were inferred as potential influencers of autism. Knockout mouse models suggested the involvement of the CASP8, GABBR1, and PLEKHM1 genes in neurological processes. Our findings suggest 17 candidate therapeutic targets for ASD and provide novel drug targets for therapy development and critical drug repurposing opportunities.

Integrating human endogenous retroviruses into transcriptome-wide association studies highlights novel risk factors for major psychiatric conditions

Human endogenous retroviruses (HERVs) are repetitive elements previously implicated in major psychiatric conditions, but their role in aetiology remains unclear. Here, we perform specialised transcriptome-wide association studies that consider HERV expression quantified to precise genomic locations, using RNA sequencing and genetic data from 792 post-mortem brain samples. In Europeans, we identify 1238 HERVs with expression regulated in cis, of which 26 represent expression signals associated with psychiatric disorders, with ten being conditionally independent from neighbouring expression signals. Of these, five are additionally significant in fine-mapping analyses and thus are considered high confidence risk HERVs. These include two HERV expression signatures specific to schizophrenia risk, one shared between schizophrenia and bipolar disorder, and one specific to major depressive disorder. No robust signatures are identified for autism spectrum conditions or attention deficit hyperactivity disorder in Europeans, or for any psychiatric trait in other ancestries, although this is likely a result of relatively limited statistical power. Ultimately, our study highlights extensive HERV expression and regulation in the adult cortex, including in association with psychiatric disorder risk, therefore providing a rationale for exploring neurological HERV expression in complex neuropsychiatric traits.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Genetic relationship between the immune system and autism

Autism spectrum disorder (ASD) is a common and complex neurodevelopmental condition. The pathophysiology of ASD is poorly defined; however, it includes a strong genetic component and there is increasing evidence to support a role of immune dysregulation. Nonetheless, it is unclear which immune phenotypes link to ASD through genetics. Hence, we investigated the genetic correlation between ASD and diverse classes of immune conditions and markers; and if these immune-related genetic factors link to specific autistic-like traits in the population. We estimated global and local genetic correlations between ASD (n = 55,420) and 11 immune phenotypes (n = 14,256-755,406) using genome-wide association study summary statistics. Subsequently, polygenic scores (PGS) for these immune phenotypes were calculated in a population-based sample (n = 2487) and associated to five autistic-like traits (i.e., attention to detail, childhood behaviour, imagination, rigidity, social skills), and a total autistic-like traits score. Sex-stratified PGS analyses were also performed. At the genome-wide level, ASD was positively correlated with allergic diseases (ALG), and negatively correlated with lymphocyte count, rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE) (FDR-p = 0.01-0.02). At the local genetic level, ASD was correlated with RA, C-reactive protein, and granulocytes and lymphocyte counts (p = 5.8 × 10-6-0.002). In the general population sample, increased genetic liability for SLE, RA, ALG, and lymphocyte levels, captured by PGS, was associated with the total autistic score and with rigidity and childhood behaviour (FDR-p = 0.03). In conclusion, we demonstrated a genetic relationship between ASD and immunity that depends on the type of immune phenotype considered; some increase likelihood whereas others may potentially help build resilience. Also, this relationship may be restricted to specific genetic loci and link to specific autistic dimensions (e.g., rigidity).

The genetic relationships between brain structure and schizophrenia

Genetic risks for schizophrenia are theoretically mediated by genetic effects on brain structure but it has been unclear which genes are associated with both schizophrenia and cortical phenotypes. We accessed genome-wide association studies (GWAS) of schizophrenia (N = 69,369 cases; 236,642 controls), and of three magnetic resonance imaging (MRI) metrics (surface area, cortical thickness, neurite density index) measured at 180 cortical areas (N = 36,843, UK Biobank). Using Hi-C-coupled MAGMA, 61 genes were significantly associated with both schizophrenia and one or more MRI metrics. Whole genome analysis with partial least squares demonstrated significant genetic covariation between schizophrenia and area or thickness of most cortical regions. Genetic similarity between cortical areas was strongly coupled to their phenotypic covariance, and genetic covariation between schizophrenia and brain phenotypes was strongest in the hubs of structural covariance networks. Pleiotropically associated genes were enriched for neurodevelopmental processes and positionally concentrated in chromosomes 3p21, 17q21 and 11p11. Mendelian randomization analysis indicated that genetically determined variation in a posterior cingulate cortical area could be causal for schizophrenia. Parallel analyses of GWAS on bipolar disorder, Alzheimer's disease and height showed that pleiotropic association with MRI metrics was stronger for schizophrenia compared to other disorders.

De novo network analysis reveals autism causal genes and developmental links to co-occurring traits

Autism is a complex neurodevelopmental condition that manifests in various ways. Autism is often accompanied by other conditions, such as attention-deficit/hyperactivity disorder and schizophrenia, which can complicate diagnosis and management. Although research has investigated the role of specific genes in autism, their relationship with co-occurring traits is not fully understood. To address this, we conducted a two-sample Mendelian randomisation analysis and identified four genes located at the 17q21.31 locus that are putatively causal for autism in fetal cortical tissue (LINC02210, LRRC37A4P, RP11-259G18.1, and RP11-798G7.6). LINC02210 was also identified as putatively causal for autism in adult cortical tissue. By integrating data from expression quantitative trait loci, genes and protein interactions, we identified that the 17q21.31 locus contributes to the intersection between autism and other neurological traits in fetal cortical tissue. We also identified a distinct cluster of co-occurring traits, including cognition and worry, linked to the genetic loci at 3p21.1. Our findings provide insights into the relationship between autism and co-occurring traits, which could be used to develop predictive models for more accurate diagnosis and better clinical management.

SOX7: Novel Autistic Gene Identified by Analysis of Multi-Omics Data

Background

Despite thousands of variants identified by genome-wide association studies (GWAS) to be associated with autism spectrum disorder (ASD), it is unclear which mutations are causal because most are noncoding. Consequently, reliable diagnostic biomarkers are lacking. RNA-seq analysis captures biomolecular complexity that GWAS cannot by considering transcriptomic patterns. Therefore, integrating DNA and RNA testing may reveal causal genes and useful biomarkers for ASD.

Methods

We performed gene-based association studies using an adaptive test method with GWAS summary statistics from two large Psychiatric Genomics Consortium (PGC) datasets (ASD2019: 18,382 cases and 27,969 controls; ASD2017: 6,197 cases and 7,377 controls). We also investigated differential expression for genes identified with the adaptive test using an RNA-seq dataset (GSE30573: 3 cases and 3 controls) and DESeq2.

Results

We identified 5 genes significantly associated with ASD in ASD2019 (KIZ-AS1, p = 8.67×10- 10; KIZ, p = 1.16×10- 9; XRN2, p = 7.73×10- 9; SOX7, p = 2.22×10- 7; LOC101929229 (also known as PINX1-DT), p = 2.14×10- 6). Two of the five genes were replicated in ASD2017: SOX7 (p = 0.00087) and LOC101929229 (p = 0.009), and KIZ was close to the replication boundary of replication (p = 0.06). We identified significant expression differences for SOX7 (p = 0.0017, adjusted p = 0.0085), LOC101929229 (p = 5.83×10- 7, adjusted p = 1.18×10- 5), and KIZ (p = 0.00099, adjusted p = 0.0055). SOX7 encodes a transcription factor that regulates developmental pathways, alterations in which may contribute to ASD.

Limitations

The limitation of the gene-based analysis is the reliance on a reference population for estimating linkage disequilibrium between variants. The similarity of this reference population to the population of study is crucial to the accuracy of many gene-based analyses, including those performed in this study. As a result, the extent of our findings is limited to European populations, as this was our reference of choice. Future work includes a tighter integration of DNA and RNA information as well as extensions to non-European populations that have been under-researched.

Conclusions

These findings suggest that SOX7 and its related SOX family genes encode transcription factors that are critical to the downregulation of the canonical Wnt/β-catenin signaling pathway, an important developmental signaling pathway, providing credence to the biologic plausibility of the association between gene SOX7 and autism spectrum disorder.

Discovery of eQTL Alleles Associated with Autism Spectrum Disorder: A Case-Control Study

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by challenges in social communication as well as repetitive or restrictive behaviors. Many genetic associations with ASD have been identified, but most associations occur in a fraction of the ASD population. Here, we searched for eQTL-associated DNA variants with significantly different allele distributions between ASD-affected and control. Thirty significant DNA variants associated with 174 tissue-specific eQTLs from ASD individuals in the SPARK project were identified. Several significant variants fell within brain-specific regulatory regions or had been associated with a significant change in gene expression in the brain. These eQTLs are a new class of biomarkers that could control the myriad of brain and non-brain phenotypic traits seen in ASD-affected individuals.

Identifying Novel Drug Targets for Epilepsy Through a Brain Transcriptome-Wide Association Study and Protein-Wide Association Study with Chemical-Gene-Interaction Analysis

Epilepsy is a severe neurological condition affecting 50-65 million individuals worldwide that can lead to brain damage. Nevertheless, the etiology of epilepsy remains poorly understood. Meta-analyses of genome-wide association studies involving 15,212 epilepsy cases and 29,677 controls of the ILAE Consortium cohort were used to conduct transcriptome-wide association studies (TWAS) and protein-wide association studies (PWAS). Furthermore, a protein-protein interaction (PPI) network was generated using the STRING database, and significant epilepsy-susceptible genes were verified using chip data. Chemical-related gene set enrichment analysis (CGSEA) was performed to determine novel drug targets for epilepsy. TWAS analysis identified 21,170 genes, of which 58 were significant (TWASfdr < 0.05) in ten brain regions, and 16 differentially expressed genes were verified based on mRNA expression profiles. The PWAS identified 2249 genes, of which 2 were significant (PWASfdr < 0.05). Through chemical-gene set enrichment analysis, 287 environmental chemicals associated with epilepsy were identified. We identified five significant genes (WIPF1, IQSEC1, JAM2, ICAM3, and ZNF143) that had causal relationships with epilepsy. CGSEA identified 159 chemicals that were significantly correlated with epilepsy (Pcgsea < 0.05), such as pentobarbital, ketone bodies, and polychlorinated biphenyl. In summary, we performed TWAS, PWAS (for genetic factors), and CGSEA (for environmental factors) analyses and identified several epilepsy-associated genes and chemicals. The results of this study will contribute to our understanding of genetic and environmental factors for epilepsy and may predict novel drug targets.

Transcriptome-Wide Structural Equation Modeling of 13 Major Psychiatric Disorders for Cross-Disorder Risk and Drug Repurposing

Importance

Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy.

Objective

To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes.

Design, Setting, and Participants

This genomic study applied a multivariate transcriptomic method, transcriptome-wide structural equation modeling (T-SEM), to investigate gene expression patterns associated with 5 genomic factors indexing shared risk across 13 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. The Broad Institute Connectivity Map Drug Repurposing Database and Drug-Gene Interaction Database public databases of drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Data were collected from database inception up to February 20, 2023.

Main Outcomes and Measures

Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes.

Results

In total, T-SEM identified 466 genes whose expression was significantly associated (z ≥ 5.02) with genomic factors and 36 genes with disorder-specific effects. Most associated genes were found for a thought disorders factor, defined by bipolar disorder and schizophrenia. Several existing pharmacological interventions were identified that could be repurposed to target genes whose expression was associated with the thought disorders factor or a transdiagnostic p factor defined by all 13 disorders.

Conclusions and Relevance

The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

SOX7: Novel Autistic Gene Identified by Analysis of Multi-Omics Data

Background

Genome-wide association studies and next generation sequencing data analyses based on DNA information have identified thousands of mutations associated with autism spectrum disorder (ASD). However, more than 99% of identified mutations are non-coding. Thus, it is unclear which of these mutations might be functional and thus potentially causal variants. Transcriptomic profiling using total RNA-sequencing has been one of the most utilized approaches to link protein levels to genetic information at the molecular level. The transcriptome captures molecular genomic complexity that the DNA sequence solely does not. Some mutations alter a gene's DNA sequence but do not necessarily change expression and/or protein function. To date, few common variants reliably associated with the diagnosis status of ASD despite consistently high estimates of heritability. In addition, reliable biomarkers used to diagnose ASD or molecular mechanisms to define the severity of ASD do not exist.

Objectives

It is necessary to integrate DNA and RNA testing together to identify true causal genes and propose useful biomarkers for ASD.

Methods

We performed gene-based association studies with adaptive test using genome-wide association studies (GWAS) summary statistics with two large GWAS datasets (ASD 2019 data: 18,382 ASD cases and 27,969 controls [discovery data]; ASD 2017 data: 6,197 ASD cases and 7,377 controls [replication data]) which were obtained from the Psychiatric Genomics Consortium (PGC). In addition, we investigated differential expression for genes identified in gene-based GWAS with a RNA-seq dataset (GSE30573: 3 cases and 3 controls) using the DESeq2 package.

Results

We identified 5 genes significantly associated with ASD in ASD 2019 data (KIZ-AS1, p=8.67×10-10; KIZ, p=1.16×10-9; XRN2, p=7.73×10-9; SOX7, p=2.22×10-7; PINX1-DT, p=2.14×10-6). Among these 5 genes, gene SOX7 (p=0.00087), LOC101929229 (p=0.009), and KIZ-AS1 (p=0.059) were replicated in ASD 2017 data. KIZ (p=0.06) was close to the boundary of replication in ASD 2017 data. Genes SOX7 (p=0.0017, adjusted p=0.0085), LOC101929229 (also known as PINX1-DT, p=5.83×10-7, adjusted p=1.18×10-5), and KIZ (p=0.00099, adjusted p=0.0055) indicated significant expression differences between cases and controls in the RNA-seq data. SOX7 encodes a member of the SOX (SRY-related HMG-box) family of transcription factors pivotally contributing to determining of the cell fate and identity in many lineages. The encoded protein may act as a transcriptional regulator after forming a protein complex with other proteins leading to autism.

Conclusion

Gene SOX7 in the transcription factor family could be associated with ASD. This finding may provide new diagnostic and therapeutic strategies for ASD.

Multi-omic underpinnings of epigenetic aging and human longevity

Biological aging is accompanied by increasing morbidity, mortality, and healthcare costs; however, its molecular mechanisms are poorly understood. Here, we use multi-omic methods to integrate genomic, transcriptomic, and metabolomic data and identify biological associations with four measures of epigenetic age acceleration and a human longevity phenotype comprising healthspan, lifespan, and exceptional longevity (multivariate longevity). Using transcriptomic imputation, fine-mapping, and conditional analysis, we identify 22 high confidence associations with epigenetic age acceleration and seven with multivariate longevity. FLOT1, KPNA4, and TMX2 are novel, high confidence genes associated with epigenetic age acceleration. In parallel, cis-instrument Mendelian randomization of the druggable genome associates TPMT and NHLRC1 with epigenetic aging, supporting transcriptomic imputation findings. Metabolomics Mendelian randomization identifies a negative effect of non-high-density lipoprotein cholesterol and associated lipoproteins on multivariate longevity, but not epigenetic age acceleration. Finally, cell-type enrichment analysis implicates immune cells and precursors in epigenetic age acceleration and, more modestly, multivariate longevity. Follow-up Mendelian randomization of immune cell traits suggests lymphocyte subpopulations and lymphocytic surface molecules affect multivariate longevity and epigenetic age acceleration. Our results highlight druggable targets and biological pathways involved in aging and facilitate multi-omic comparisons of epigenetic clocks and human longevity.

Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction

Introduction

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

Methods

Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival.

Results

SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 4%; p-value = 2.1×10-21).

Conclusions

Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

Identification of multiple novel susceptibility genes associated with autoimmune thyroid disease

Background

Autoimmune thyroid disease (AITD) is induced by various factors, including inheritability, which regulates gene expression. Multiple loci correlated with AITD have been discovered utilizing genome-wide association studies (GWASs). Nevertheless, demonstrating the biological relevance and function of these genetic loci is difficult.

Methods

The FUSION software was utilized to define genes that were expressed differentially in AITD using a transcriptome-wide association study (TWAS) method in accordance with GWAS summary statistics from the largest genome-wide association study of 755,406 AITD individuals (30,234 cases and 725,172 controls) and levels of gene expression from two tissue datasets (blood and thyroid). Further analyses were performed such as colocalization, conditional, and fine-mapping analyses to extensively characterize the identified associations, using functional mapping and annotation (FUMA) to conduct functional annotation of the summary statistics of 23329 significant risk SNPs (P < 5 × 10^-8) recognized by GWAS, together with summary-data-based mendelian randomization (SMR) for identifying functionally related genes at the loci in GWAS.

Results

There were 330 genes with transcriptome-wide significant differences between cases and controls, and the majority of these genes were new. 9 of the 94 unique significant genes had strong, colocalized, and potentially causal correlations with AITD. Such strong associations included CD247, TPO, KIAA1524, PDE8B, BACH2, FYN, FOXK1, NKX2-3, and SPATA13. Subsequently, applying the FUMA approach, novel putative AITD susceptibility genes and involved gene sets were detected. Furthermore, we detected 95 probes that showed strong pleiotropic association with AITD through SMR analysis, such as CYP21A2, TPO, BRD7, and FCRL3. Lastly, we selected 26 genes by integrating the result of TWAS, FUMA, and SMR analysis. A phenome-wide association study (pheWAS) was then carried out to determine the risk of other related or co-morbid phenotypes for AITD-related genes.

Conclusions

The current work provides further insight into widespread changes in AITD at the transcriptomic level, as well as characterized the genetic component of gene expression in AITD by validating identified genes, establishing new correlations, and uncovering novel susceptibility genes. Our findings indicate that the genetic component of gene expression plays a significant part in AITD.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits

Functional genomic methods are needed that consider multiple genetically correlated traits. Here we develop and validate Transcriptome-wide Structural Equation Modeling (T-SEM), a multivariate method for studying the effects of tissue-specific gene expression across genetically overlapping traits. T-SEM allows for modeling effects on broad dimensions spanning constellations of traits, while safeguarding against false positives that can arise when effects of gene expression are specific to a subset of traits. We apply T-SEM to investigate the biological mechanisms shared across seven distinct cognitive traits (N = 11,263-331,679), as indexed by a general dimension of genetic sharing (g). We identify 184 genes whose tissue-specific expression is associated with g, including 10 genes not identified in univariate analysis for the individual cognitive traits for any tissue type, and three genes whose expression explained a significant portion of the genetic sharing across g and different subclusters of psychiatric disorders. We go on to apply Stratified Genomic SEM to identify enrichment for g within 28 functional categories. This includes categories indexing the intersection of protein-truncating variant intolerant (PI) genes and specific neuronal cell types, which we also find to be enriched for the genetic covariance between g and a psychotic disorders factor.

Attenuated transcriptional response to pro-inflammatory cytokines in schizophrenia hiPSC-derived neural progenitor cells

Maternal immune activation (MIA) during prenatal development is an environmental risk factor for psychiatric disorders including schizophrenia (SZ). Converging lines of evidence from human and animal model studies suggest that elevated cytokine levels in the maternal and fetal compartments are an important indication of the mechanisms driving this association. However, there is variability in susceptibility to the psychiatric risk conferred by MIA, likely influenced by genetic factors. How MIA interacts with a genetic profile susceptible to SZ is challenging to test in animal models. To address this gap, we examined whether differential gene expression responses occur in forebrain-lineage neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (hiPSC) generated from three individuals with a diagnosis of schizophrenia and three healthy controls. Following acute (24 h) treatment with either interferon-gamma (IFNγ; 25 ng/μl) or interleukin (IL)-1β (10 ng/μl), we identified, by RNA sequencing, 3380 differentially expressed genes (DEGs) in the IFNγ-treated control lines (compared to untreated controls), and 1980 DEGs in IFNγ-treated SZ lines (compared to untreated SZ lines). Out of 4137 genes that responded significantly to IFNγ across all lines, 1223 were common to both SZ and control lines. The 2914 genes that appeared to respond differentially to IFNγ treatment in SZ lines were subjected to a further test of significance (multiple testing correction applied to the interaction effect between IFNγ treatment and SZ diagnosis), yielding 359 genes that passed the significance threshold. There were no differentially expressed genes in the IL-1β-treatment conditions after Benjamini-Hochberg correction. Gene set enrichment analysis however showed that IL-1β impacts immune function and neuronal differentiation. Overall, our data suggest that a) SZ NPCs show an attenuated transcriptional response to IFNγ treatment compared to controls; b) Due to low IL-1β receptor expression in NPCs, NPC cultures appear to be less responsive to IL-1β than IFNγ; and c) the genes differentially regulated in SZ lines - in the face of a cytokine challenge - are primarily associated with mitochondrial, "loss-of-function", pre- and post-synaptic gene sets. Our findings particularly highlight the role of early synaptic development in the association between maternal immune activation and schizophrenia risk.

A transcriptome-wide association study provides new insights into the etiology of osteoarthritis

Background

Osteoarthritis (OA) is a common clinical disease caused by a variety of factors, including genetic variants. Although genome-wide association studies (GWAS) have been performed to elucidate the genetic basis of OA, some loci of risk located in noncoding regions of the genome have been neglected. Therefore, we integrated multiple data types to detect the genetic component of gene expression in OA patients through transcriptome-wide association studies (TWAS) and summary-data-based Mendelian randomization (SMR) analysis.

Methods

TWAS was performed by integrating the larger GWAS summary-data for OA (n=30,727 cases, n=297,191 controls) and 2 expression weight sets (muscle-skeletal tissue and whole blood). Colocalization analysis, conditional analysis, and fine-mapping analysis were also conducted. A broad description of the identified associations was obtained. In addition, a causal relationship between certain risk genes and OA was identified with SMR.

Results

New significant genome-wide associations were found, including on chromosome 1q36.12 (rs1555024, P=4.24E-07) near the ASAP3 and TCEA3 genes, on chromosome 17q24.2 (rs2521348, P=1.01E-06) near the ABCA9 gene, on chromosome 20q11.22 (rs224331, P=8.17E-09) near the UQCC1 and MYH7B genes, and on chromosome 21q21.3 (rs2832155, P=5.39E-08) near the RWDD2B gene. In addition, SMR results exhibited that upregulated UQCC1 and downregulated ASAP3 were associated with OA development and both had a significant causal relationship with OA.

Conclusions

We revealed some novel OA-associated genes and risk loci by integrating multiple data types and analysis methods, thus providing new clues for the study of genetic mechanisms of OA.

Identifying celiac disease-related chemicals by transcriptome-wide association study and chemical-gene interaction analyses

Celiac disease (CeD) is one of the most common intestinal inflammatory diseases, and its incidence and prevalence have increased over time. CeD affects multiple organs and systems in the body, and environmental factors play a key role in its complex pathogenesis. Although gluten exposure is known to be the causative agent, many unknown environmental factors can trigger or exacerbate CeD. In this study, we investigated the influence of genetic and environmental factors on CeD. Data from a CeD genome-wide association study that included 12,041 CeD cases and 12,228 controls were used to conduct a transcriptome-wide association study (TWAS) using FUSION software. Gene expression reference data were obtained for the small intestine, whole blood, peripheral blood, and lymphocytes. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the significant genes identified by the TWAS and conducted a protein–protein interaction network analysis based on the STRING database to detect the function of TWAS-identified genes for CeD. We also performed a chemical-related gene set enrichment analysis (CGSEA) using the TWAS-identified genes to test the relationships between chemicals and CeD. The TWAS identified 8,692 genes, including 101 significant genes (p_adjusted < 0.05). The CGSEA identified 2,559 chemicals, including 178 chemicals that were significantly correlated with CeD. This study performed a TWAS (for genetic factors) and CGSEA (for environmental factors) and identified several CeD-associated genes and chemicals. The findings expand our understanding of the genetic and environmental factors related to immune-mediated diseases.

Genetics-informed precision treatment formulation in schizophrenia and bipolar disorder

Genetically informed drug development and repurposing is an attractive prospect for improving patient outcomes in psychiatry; however, the effectiveness of these endeavors is confounded by heterogeneity. We propose an approach that links interventions implicated by disorder-associated genetic risk, at the population level, to a framework that can target these compounds to individuals. Specifically, results from genome-wide association studies are integrated with expression data to prioritize individual "directional anchor" genes for which the predicted risk-increasing direction of expression could be counteracted by an existing drug. While these compounds represent plausible therapeutic candidates, they are not likely to be equally efficacious for all individuals. To account for this heterogeneity, we constructed polygenic scores restricted to variants annotated to the network of genes that interact with each directional anchor gene. These metrics, which we call a pharmagenic enrichment score (PES), identify individuals with a higher burden of genetic risk, localized in biological processes related to the candidate drug target, to inform precision drug repurposing. We used this approach to investigate schizophrenia and bipolar disorder and reveal several compounds targeting specific directional anchor genes that could be plausibly repurposed. These genetic risk scores, mapped to the networks associated with target genes, revealed biological insights that cannot be observed in undifferentiated genome-wide polygenic risk score (PRS). For example, an enrichment of these partitioned scores in schizophrenia cases with otherwise low PRS. In summary, genetic risk could be used more specifically to direct drug repurposing candidates that target particular genes implicated in psychiatric and other complex disorders.

Identifying the Common Genetic Basis of Antidepressant Response

Background

Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.

Methods

Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.

Results

Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.

Conclusions

This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

Leveraging gene co-regulation to identify gene sets enriched for disease heritability

Identifying gene sets that are associated to disease can provide valuable biological knowledge, but a fundamental challenge of gene set analyses of GWAS data is linking disease-associated SNPs to genes. Transcriptome-wide association studies (TWASs) detect associations between the genetically predicted expression of a gene and disease risk, thus implicating candidate disease genes. However, causal disease genes at TWAS-associated loci generally remain unknown due to gene co-regulation, which leads to correlations across genes in predicted expression. We developed a method, gene co-regulation score (GCSC) regression, to identify gene sets that are enriched for disease heritability explained by predicted expression. GCSC regresses TWAS chi-square statistics on gene co-regulation scores reflecting correlations in predicted gene expression; a gene set is enriched for heritability if genes with high co-regulation to the set have higher TWAS chi-square statistics than genes with low co-regulation to the set, beyond what is expected based on co-regulation to all genes. We verified via simulations that GCSC is well calibrated and well powered. We applied GCSC to gene expression data from GTEx (48 tissues) and GWAS summary statistics for 43 independent diseases and complex traits analyzing a broad set of biological pathways and specifically expressed gene sets. We identified many enriched sets, recapitulating known biology. For Alzheimer disease, we detected evidence of an immune basis, and specifically a role for antigen presentation, in analyses of both biological pathways and specifically expressed gene sets. Our results highlight the advantages of leveraging gene co-regulation within the TWAS framework to identify enriched gene sets.

Genetics of common cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a leading cause of ischaemic and haemorrhagic stroke and a major contributor to dementia. Covert cSVD, which is detectable with brain MRI but does not manifest as clinical stroke, is highly prevalent in the general population, particularly with increasing age. Advances in technologies and collaborative work have led to substantial progress in the identification of common genetic variants that are associated with cSVD-related stroke (ischaemic and haemorrhagic) and MRI-defined covert cSVD. In this Review, we provide an overview of collaborative studies - mostly genome-wide association studies (GWAS) - that have identified >50 independent genetic loci associated with the risk of cSVD. We describe how these associations have provided novel insights into the biological mechanisms involved in cSVD, revealed patterns of shared genetic variation across cSVD traits, and shed new light on the continuum between rare, monogenic and common, multifactorial cSVD. We consider how GWAS summary statistics have been leveraged for Mendelian randomization studies to explore causal pathways in cSVD and provide genetic evidence for drug effects, and how the combination of findings from GWAS with gene expression resources and drug target databases has enabled identification of putative causal genes and provided proof-of-concept for drug repositioning potential. We also discuss opportunities for polygenic risk prediction, multi-ancestry approaches and integration with other omics data.

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

OBJECTIVES

Gout is a common inflammatory arthritis, which is caused by hyperuricemia. Limited efforts have been paid to systematically explore the relationships between gout and common psychiatric disorders.

METHODS

Genome-wide association study summary data of gout were obtained from the GeneATLAS, which contained 452,264 participants including 3,528 gout cases. Linkage disequilibrium score regression (LDSC) was first conducted to evaluate the genetic relationships between gout and 5 common psychiatric disorders. Transcriptome-wide association studies (TWAS) was then conducted to explore the potential biological mechanism underlying the observed genetic correlation between gout and attention-deficit hyperactivity disorder (ADHD). The Database for Annotation, Visualization and Integrated Discovery online functional annotation system was applied for pathway enrichment analysis and gene ontology enrichment analysis.

RESULTS

LDSC analysis observed significant genetic correlation between gout and ADHD (genetic correlation coefficients = 0.29, standard error = 0.09 and P value = 0.0015). Further TWAS of gout identified 105 genes with P value < 0.05 in muscle skeleton and 228 genes with P value < 0.05 in blood. TWAS of ADHD also detected 300 genes with P value < 0.05 in blood. Further comparing the TWAS results identified 9 common candidate genes shared by gout and ADHD, such as CD300C (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.

Sites of active gene regulation in the prenatal frontal cortex and their role in neuropsychiatric disorders

Common genetic variation appears to largely influence risk for neuropsychiatric disorders through effects on gene regulation. It is therefore possible to shed light on the biology of these conditions by testing for enrichment of associated genetic variation within regulatory genomic regions operating in specific tissues or cell types. Here, we have used the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-Seq) to map open chromatin (an index of active regulatory genomic regions) in bulk tissue, NeuN+ and NeuN- nuclei from the prenatal human frontal cortex, and tested enrichment of single-nucleotide polymorphism (SNP) heritability for five neuropsychiatric disorders (autism spectrum disorder, attention deficit hyperactivity disorder [ADHD], bipolar disorder, major depressive disorder, and schizophrenia) within these regions. We observed significant enrichment of SNP heritability for ADHD, major depressive disorder, and schizophrenia within open chromatin regions (OCRs) mapped in bulk fetal frontal cortex, and for all five tested neuropsychiatric conditions when we restricted these sites to those overlapping histone modifications indicative of enhancers (H3K4me1) or promoters (H3K4me3) in fetal brain. SNP heritability for neuropsychiatric disorders was significantly enriched in OCRs identified in fetal frontal cortex NeuN- as well as NeuN+ nuclei overlapping fetal brain H3K4me1 or H3K4me3 sites. We additionally demonstrate the utility of our mapped OCRs for prioritizing potentially functional SNPs at genome-wide significant risk loci for neuropsychiatric disorders. Our data provide evidence for an early neurodevelopmental component to a range of neuropsychiatric conditions and highlight an important role for regulatory genomic regions active within both NeuN+ and NeuN- cells of the prenatal brain.

Transcriptome-wide association study of treatment-resistant depression and depression subtypes for drug repurposing

Major depressive disorder (MDD) is the single largest contributor to global disability and up to 20-30% of patients do not respond to at least two antidepressants (treatment-resistant depression, TRD). This study leveraged imputed gene expression in TRD to perform a drug repurposing analysis. Among those with MDD, we defined TRD as having at least two antidepressant switches according to primary care records in UK Biobank (UKB). We performed a transcriptome-wide association study (TWAS) of TRD (n = 2165) vs healthy controls (n = 11,188) using FUSION and gene expression levels from 21 tissues. We identified compounds with opposite gene expression signatures (ConnectivityMap data) compared to our TWAS results using the Kolmogorov-Smirnov test, Spearman and Pearson correlation. As symptom patterns are routinely assessed in clinical practice and could be used to provide targeted treatments, we identified MDD subtypes associated with TRD in UKB and analysed them using the same pipeline described for TRD. Anxious MDD (n = 14,954) and MDD with weight gain (n = 4697) were associated with TRD. In the TWAS, two genes were significantly dysregulated (TMEM106B and ATP2A1 for anxious and weight gain MDD, respectively). A muscarinic receptor antagonist was identified as top candidate for repurposing in TRD; inhibition of heat shock protein 90 was the main mechanism of action identified for anxious MDD, while modulators of metabolism such as troglitazone showed promising results for MDD with weight gain. This was the first TWAS of TRD and associated MDD subtypes. Our results shed light on possible pharmacological approaches in individuals with difficult-to-treat depression.

Altered neuronal physiology, development, and function associated with a common chromosome 15 duplication involving CHRNA7

BACKGROUND

Copy number variants (CNVs) linked to genes involved in nervous system development or function are often associated with neuropsychiatric disease. While CNVs involving deletions generally cause severe and highly penetrant patient phenotypes, CNVs leading to duplications tend instead to exhibit widely variable and less penetrant phenotypic expressivity among affected individuals. CNVs located on chromosome 15q13.3 affecting the alpha-7 nicotinic acetylcholine receptor subunit (CHRNA7) gene contribute to multiple neuropsychiatric disorders with highly variable penetrance. However, the basis of such differential penetrance remains uncharacterized. Here, we generated induced pluripotent stem cell (iPSC) models from first-degree relatives with a 15q13.3 duplication and analyzed their cellular phenotypes to uncover a basis for the dissimilar phenotypic expressivity.

RESULTS

The first-degree relatives studied included a boy with autism and emotional dysregulation (the affected proband-AP) and his clinically unaffected mother (UM), with comparison to unrelated control models lacking this duplication. Potential contributors to neuropsychiatric impairment were modeled in iPSC-derived cortical excitatory and inhibitory neurons. The AP-derived model uniquely exhibited disruptions of cellular physiology and neurodevelopment not observed in either the UM or unrelated controls. These included enhanced neural progenitor proliferation but impaired neuronal differentiation, maturation, and migration, and increased endoplasmic reticulum (ER) stress. Both the neuronal migration deficit and elevated ER stress could be selectively rescued by different pharmacologic agents. Neuronal gene expression was also dysregulated in the AP, including reduced expression of genes related to behavior, psychological disorders, neuritogenesis, neuronal migration, and Wnt, axonal guidance, and GABA receptor signaling. The UM model instead exhibited upregulated expression of genes in many of these same pathways, suggesting that molecular compensation could have contributed to the lack of neurodevelopmental phenotypes in this model. However, both AP- and UM-derived neurons exhibited shared alterations of neuronal function, including increased action potential firing and elevated cholinergic activity, consistent with increased homomeric CHRNA7 channel activity.

CONCLUSIONS

These data define both diagnosis-associated cellular phenotypes and shared functional anomalies related to CHRNA7 duplication that may contribute to variable phenotypic penetrance in individuals with 15q13.3 duplication. The capacity for pharmacological agents to rescue some neurodevelopmental anomalies associated with diagnosis suggests avenues for intervention for carriers of this duplication and other CNVs that cause related disorders.

Cis-effects on gene expression in the human prenatal brain associated with genetic risk for neuropsychiatric disorders

The majority of common risk alleles identified for neuropsychiatric disorders reside in noncoding regions of the genome and are therefore likely to impact gene regulation. However, the genes that are primarily affected and the nature and developmental timing of these effects remain unclear. Given the hypothesized role for early neurodevelopmental processes in these conditions, we here define genetic predictors of gene expression in the human fetal brain with which we perform transcriptome-wide association studies (TWASs) of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia. We identify prenatal cis-regulatory effects on 63 genes and 166 individual transcripts associated with genetic risk for these conditions. We observe pleiotropic effects of expression predictors for a number of genes and transcripts, including those of decreased DDHD2 expression in association with risk for schizophrenia and bipolar disorder, increased expression of a ST3GAL3 transcript with risk for schizophrenia and ADHD, and increased expression of an XPNPEP3 transcript with risk for schizophrenia, bipolar disorder, and major depression. For the protocadherin alpha cluster genes PCDHA7 and PCDHA8, we find that predictors of low expression are associated with risk for major depressive disorder while those of higher expression are associated with risk for schizophrenia. Our findings support a role for altered gene regulation in the prenatal brain in susceptibility to various neuropsychiatric disorders and prioritize potential risk genes for further neurobiological investigation.

Imputed gene expression risk scores: a functionally informed component of polygenic risk

Integration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. The predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study. GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed single nucleotide polymorphism (SNP)-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. GeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for height (P = 0.023) to 4% for rheumatoid arthritis (P = 5.9 × 10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalized genes. GeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

Gene Expression Imputation Across Multiple Tissue Types Provides Insight Into the Genetic Architecture of Frontotemporal Dementia and Its Clinical Subtypes

BACKGROUND

The etiology of frontotemporal dementia (FTD) is poorly understood. To identify genes with predicted expression levels associated with FTD, we integrated summary statistics with external reference gene expression data using a transcriptome-wide association study approach.

METHODS

FUSION software was used to leverage FTD summary statistics (all FTD: n = 2154 cases, n = 4308 controls; behavioral variant FTD: n = 1337 cases, n = 2754 controls; semantic dementia: n = 308 cases, n = 616 controls; progressive nonfluent aphasia: n = 269 cases, n = 538 controls; FTD with motor neuron disease: n = 200 cases, n = 400 controls) from the International FTD-Genomics Consortium with 53 expression quantitative loci tissue type panels (n = 12,205; 5 consortia). Significance was assessed using a 5% false discovery rate threshold.

RESULTS

We identified 73 significant gene-tissue associations for FTD, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n = 19 genes, 26%). The 17q21.31 inversion locus contained 23 significant associations, representing 6 unique genes. Other top hits included SEC22B (a gene involved in vesicle trafficking), TRGV5, and ZNF302. A single gene finding (RAB38) was observed for behavioral variant FTD. For other clinical subtypes, no significant associations were observed.

CONCLUSIONS

We identified novel candidate genes (e.g., SEC22B) and previously reported risk regions (e.g., 17q21.31) for FTD. Most significant associations were observed in dorsolateral prefrontal cortex splicing data despite the modest sample size of this reference panel. This suggests that our findings are specific to FTD and are likely to be biologically relevant highlights of genes at different FTD risk loci that are contributing to the disease pathology.

An Integrative Transcriptome-Wide Analysis of Amyotrophic Lateral Sclerosis for the Identification of Potential Genetic Markers and Drug Candidates

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative neuromuscular disease. Although genome-wide association studies (GWAS) have successfully identified many variants significantly associated with ALS, it is still difficult to characterize the underlying biological mechanisms inducing ALS. In this study, we performed a transcriptome-wide association study (TWAS) to identify disease-specific genes in ALS. Using the largest ALS GWAS summary statistic (n = 80,610), we identified seven novel genes using 19 tissue reference panels. We conducted a conditional analysis to verify the genes’ independence and to confirm that they are driven by genetically regulated expressions. Furthermore, we performed a TWAS-based enrichment analysis to highlight the association of important biological pathways, one in each of the four tissue reference panels. Finally, utilizing a connectivity map, a database of human cell expression profiles cultured with bioactive small molecules, we discovered functional associations between genes and drugs to identify 15 bioactive small molecules as potential drug candidates for ALS. We believe that, by integrating the largest ALS GWAS summary statistic with gene expression to identify new risk loci and causal genes, our study provides strong candidates for molecular basis experiments in ALS.

Transcriptome-wide association study reveals two genes that influence mismatch negativity

Mismatch negativity (MMN) is a differential electrophysiological response measuring cortical adaptability to unpredictable stimuli. MMN is consistently attenuated in patients with psychosis. However, the genetics of MMN are uncharted, limiting the validation of MMN as a psychosis endophenotype. Here, we perform a transcriptome-wide association study of 728 individuals, which reveals 2 genes (FAM89A and ENGASE) whose expression in cortical tissues is associated with MMN. Enrichment analyses of neurodevelopmental expression signatures show that genes associated with MMN tend to be overexpressed in the frontal cortex during prenatal development but are significantly downregulated in adulthood. Endophenotype ranking value calculations comparing MMN and three other candidate psychosis endophenotypes (lateral ventricular volume and two auditory-verbal learning measures) find MMN to be considerably superior. These results yield promising insights into sensory processing in the cortex and endorse the notion of MMN as a psychosis endophenotype.

Delineating the Genetic Component of Gene Expression in Major Depression

BACKGROUND

Major depression (MD) is determined by a multitude of factors including genetic risk variants that regulate gene expression. We examined the genetic component of gene expression in MD by performing a transcriptome-wide association study (TWAS), inferring gene expression-trait relationships from genetic, transcriptomic, and phenotypic information.

METHODS

Genes differentially expressed in depression were identified with the TWAS FUSION method, based on summary statistics from the largest genome-wide association analysis of MD (n = 135,458 cases, n = 344,901 controls) and gene expression levels from 21 tissue datasets (brain; blood; thyroid, adrenal, and pituitary glands). Follow-up analyses were performed to extensively characterize the identified associations: colocalization, conditional, and fine-mapping analyses together with TWAS-based pathway investigations.

RESULTS

Transcriptome-wide significant differences between cases and controls were found at 94 genes, approximately half of which were novel. Of the 94 significant genes, 6 represented strong, colocalized, and potentially causal associations with depression. Such high-confidence associations include NEGR1, CTC-467M3.3, TMEM106B, LRFN5, ESR2, and PROX2. Lastly, TWAS-based enrichment analysis highlighted dysregulation of gene sets for, among others, neuronal and synaptic processes.

CONCLUSIONS

This study sheds further light on the genetic component of gene expression in depression by characterizing the identified associations, unraveling novel risk genes, and determining which associations are congruent with a causal model. These findings can be used as a resource for prioritizing and designing subsequent functional studies of MD.

Comprehensive Integrative Analyses Identify TIGD5 rs75547282 as a Risk Variant for Autism Spectrum Disorder

Although recent genome-wide association studies have identified risk loci that strongly associates with autism spectrum disorder (ASD), how to pinpoint the causal genes remains a challenge. We aimed to pinpoint the potential causal genes and explore the possible susceptibility and mechanism. A convergent functional genomics (CFG) method was used to prioritize the candidate genes by combining lines of evidence, including Sherlock analysis, spatio-temporal expression patterns, expression analysis, protein-protein interactions, co-expression and association with brain structure. A higher score in the CFG approach suggested that more evidence supported this gene as an ASD risk gene. We screened genes with higher CFG scores for candidate functional single nucleotide polymorphisms (SNPs). A genotyping experiment (602 ASD children and 604 healthy sex-matched children) and the dual-luciferase reporter gene assay were followed to validate the effects of SNPs. We identified three genes (MAPT, ZNF285, and TIGD5) as candidate causal genes using the CFG approach. The genotyping experiment showed that TIGD5 rs75547282 was associated with an increased risk of ASD under the dominant model (OR = 1.37, 95% CI = 1.09-1.72, P = 0.006) though the statistical power was limited (5.2%). The T allele of rs75547282 activated the expression of TIGD5 compared with the C allele in the dual-luciferase reporter assay. Our study indicates that such comprehensive integrative analyses may be an effective way to explore promising ASD susceptibility variants and needs to be further investigated in future research. Genotyping experiments should, however, be based on a larger population sample to increase statistical power. LAY SUMMARY: We set out to pinpoint the potential causal genes of ASD and explore the possible susceptibility and mechanism by combining lines of evidence from different analyses. Our results show that TIGD5 rs75547282 is associated with the risk of ASD in the Han Chinese population. In addition, a similar framework to seek promising ASD risk variants could be further investigated in future research Autism Res 2021, 14: 631-644. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia

Schizophrenia is a complex highly heritable disorder. Genome-wide association studies (GWAS) have identified multiple loci that influence the risk of developing schizophrenia, although the causal variants driving these associations and their impacts on specific genes are largely unknown. We identify a significant correlation between schizophrenia risk and expression at 89 genes in the dorsolateral prefrontal cortex (P ≤ 9.43 × 10-6), including 20 novel genes. Genes whose expression correlate with schizophrenia were enriched for those involved in abnormal CNS synaptic transmission (PFDR = 0.02) and antigen processing and presentation of peptide antigen via MHC class I (PFDR = 0.02). Within the CNS synaptic transmission set, we identify individual significant candidate genes to which we assign direction of expression changes in schizophrenia. The findings provide strong candidates for experimentally probing the molecular basis of synaptic pathology in schizophrenia.

Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

In clinical practice, an antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We predicted the transcriptomic profile of citalopram remitters by performing an in silico transcriptomic-wide association study on STAR*D GWAS data (N = 1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity-map database. Spearman correlation, Pearson correlation, and the Kolmogorov-Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p value for each rank by using a permutation procedure. The drugs with the top ranks were those having a high positive correlation with the expression profiles of remitters and that may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p = 0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second-highest ranked antidepressants, respectively (p = 0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters' and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements, it can be applicable at the individual level to tailor treatment prescription.

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies

BACKGROUND

Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.

METHODS

We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.

RESULTS

We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.

CONCLUSIONS

Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

Integration of CRISPR-engineering and hiPSC-based models of psychiatric genomics

Neuropsychiatric disorders are highly heritable polygenic disorders arising from the complex interplay of highly penetrant rare variants and common variants of small effect. There is a large index of comorbidity and shared genetic risk between disorders, reflecting the pleiotropy of individual variants as well as predicted downstream pathway-level convergence. Importantly, the mechanism(s) through which psychiatric disease-associated variants interact to contribute to disease risk remains unknown. Human induced pluripotent stem cell (hiPSC)-based models are increasingly useful for the systematic study of the complex genetics associated with brain diseases, particularly when combined with CRISPR-mediated genomic engineering, which together facilitate isogenic comparisons of defined neuronal cell types. In this review, we discuss the latest CRISPR technologies and consider how they can be successfully applied to the functional characterization of the growing list genetic variants linked to psychiatric disease.

The level of GNE and its relationship with behavioral phenotypes in children with autism spectrum disorder

Autism spectrum disorder (ASD) is a serious nervous system disease, and the cause is not known. Sialic acid (SA) is an indispensable nutrient for early brain development. In previous study, it was found that the SA level of ASD group was lower than that of control group. However, the reason for this has not well explained. A case-control study was conducted to understand the association between the SA synthase enzyme regulatory gene and ASD. The study sample included 65 ASD children and 64 healthy children. The levels of the GNE gene were measured, which encodes UDP-GlcNAc 2-epimerase/ManNAc kinase (GNE), a key enzyme in SA biosynthesis. The symptom severity, intelligence development level, and behavioral performance of ASD children were estimated. There was a significant difference in the levels of GNE between the ASD and control groups (t = 2.028, P = .045). Moreover, the levels of GNE were negatively related to stereotypical behaviors according to the Autism Diagnostic Observation Schedule (ADOS) assessment (r = -0.386, P = .039). However, there is no the correlation between the levels of GNE and autistic severity. As evaluated through the Social Responsiveness Scale (SRS), the levels of GNE were negatively associated with autistic mannerisms scores, social cognition scores and SRS total scores in the children with ASD (r = -0.314, P = .020). These results indicate that the GNE gene may be associated with autism spectrum disorder, and it is also related to autistic behavioral performance, such as stereotypical behaviors, autistic mannerisms, and social cognition ability. Our data suggest that future studies to explore the causal relationship between GNE and the etiology of ASD may be needed.

Identification of amygdala-expressed genes associated with autism spectrum disorder

BACKGROUND

Studies of individuals with autism spectrum disorder (ASD) have revealed a strong multigenic basis with the identification of hundreds of ASD susceptibility genes. ASD is characterized by social deficits and a range of other phenotypes, implicating complex genetics and involvement of a variety of brain regions. However, how mutations and mis-expression of select gene sets are associated with the behavioral components of ASD remains unknown. We reasoned that for genes to be associated with ASD core behaviors they must be: (1) expressed in brain regions relevant to ASD social behaviors and (2) expressed during the ASD susceptible window of brain development.

METHODS

Focusing on the amygdala, a brain region whose dysfunction has been highly implicated in the social component of ASD, we mined publicly available gene expression databases to identify ASD-susceptibility genes expressed during human and mouse amygdala development. We found that a large cohort of known ASD susceptibility genes is expressed in the developing human and mouse amygdala. We further performed analysis of single-nucleus RNA-seq (snRNA-seq) data from microdissected amygdala tissue from five ASD and five control human postmortem brains ranging in age from 4 to 20 years to elucidate cell type specificity of amygdala-expressed genes and their dysregulation in ASD.

RESULTS

Our analyses revealed that of the high-ranking ASD susceptibility genes, 80 are expressed in both human and mouse amygdala during fetal to early postnatal stages of development. Our human snRNA-seq analyses revealed cohorts of genes with altered expression in the ASD amygdala postnatally, especially within excitatory neurons, with dysregulated expression of seven genes predicted from our datamining pipeline.

LIMITATIONS

We were limited by the ages for which we were able to obtain human tissue; therefore, the results from our datamining pipeline approach will require validation, to the extent possible, in human tissue from earlier developmental stages.

CONCLUSIONS

Our pipeline narrows down the number of amygdala-expressed genes possibly involved in the social pathophysiology of ASD. Our human single-nucleus gene expression analyses revealed that ASD is characterized by changes in gene expression in specific cell types in the early postnatal amygdala.

Genomics, transcriptomics, proteomics and big data analysis in the discovery of new diagnostic markers and targets for therapy development

Highly complex endophenotypes and underlying molecular mechanisms have prevented effective diagnosis and treatment of autism spectrum disorder. Despite extensive studies to identify relevant biosignatures, no biomarker and therapeutic targets are available in the current clinical practice. While our current knowledge is still largely incomplete, -omics technology and machine learning-based big data analysis have provided novel insights on the etiology of autism spectrum disorders, elucidating systemic impairments that can be translated into biomarker and therapy target candidates. However, more integrated and sophisticated approaches are vital to realize molecular stratification and individualized treatment strategy. Ultimately, systemic approaches based on -omics and big data analysis will significantly contribute to more effective biomarker and therapy development for autism spectrum disorder.

Genome-wide association study of MRI markers of cerebral small vessel disease in 42,310 participants

Cerebral small vessel disease is a major cause of stroke and dementia, but its genetic basis is incompletely understood. We perform a genetic study of three MRI markers of the disease in UK Biobank imaging data and other sources: white matter hyperintensities (N = 42,310), fractional anisotropy (N = 17,663) and mean diffusivity (N = 17,467). Our aim is to better understand the disease pathophysiology. Across the three traits, we identify 31 loci, of which 21 were previously unreported. We perform a transcriptome-wide association study to identify associations with gene expression in relevant tissues, identifying 66 associated genes across the three traits. This genetic study provides insights into the understanding of the biological mechanisms underlying small vessel disease.

Emerging biomarkers in autism spectrum disorder: a systematic review

Autism spectrum disorder (ASD) affects approximately 2% of children in the United States (US) yet its etiology is unclear and effective treatments are lacking. Therapeutic interventions are most effective if started early in life, yet diagnosis often remains delayed, partly because the diagnosis of ASD is based on identifying abnormal behaviors that may not emerge until the disorder is well established. Biomarkers that identify children at risk during the pre-symptomatic period, assist with early diagnosis, confirm behavioral observations, stratify patients into subgroups, and predict therapeutic response would be a great advance. Here we underwent a systematic review of the literature on ASD to identify promising biomarkers and rated the biomarkers in regards to a Level of Evidence and Grade of Recommendation using the Oxford Centre for Evidence-Based Medicine scale. Biomarkers identified by our review included physiological biomarkers that identify neuroimmune and metabolic abnormalities, neurological biomarkers including abnormalities in brain structure, function and neurophysiology, subtle behavioral biomarkers including atypical development of visual attention, genetic biomarkers and gastrointestinal biomarkers. Biomarkers of ASD may be found prior to birth and after diagnosis and some may predict response to specific treatments. Many promising biomarkers have been developed for ASD. However, many biomarkers are preliminary and need to be validated and their role in the diagnosis and treatment of ASD needs to be defined. It is likely that biomarkers will need to be combined to be effective to identify ASD early and guide treatment.

Association of genes with phenotype in autism spectrum disorder

Autism spectrum disorder (ASD) is a genetic heterogeneous neurodevelopmental disorder that is characterized by impairments in social interaction and speech development and is accompanied by stereotypical behaviors such as body rocking, hand flapping, spinning objects, sniffing and restricted behaviors. The considerable significance of the genetics associated with autism has led to the identification of many risk genes for ASD used for the probing of ASD specificity and shared cognitive features over the past few decades. Identification of ASD risk genes helps to unravel various genetic variants and signaling pathways which are involved in ASD. This review highlights the role of ASD risk genes in gene transcription and translation regulation processes, as well as neuronal activity modulation, synaptic plasticity, disrupted key biological signaling pathways, and the novel candidate genes that play a significant role in the pathophysiology of ASD. The current emphasis on autism spectrum disorders has generated new opportunities in the field of neuroscience, and further advancements in the identification of different biomarkers, risk genes, and genetic pathways can help in the early diagnosis and development of new clinical and pharmacological treatments for ASD.