Brain Expression Quantitative Trait Loci Research Articles

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e−06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Rare tandem repeat expansions associate with genes involved in synaptic and neuronal signaling functions in schizophrenia

Tandem repeat expansions (TREs) are associated with over 60 monogenic disorders and have recently been implicated in complex disorders such as cancer and autism spectrum disorder. The role of TREs in schizophrenia is now emerging. In this study, we have performed a genome-wide investigation of TREs in schizophrenia. Using genome sequence data from 1154 Swedish schizophrenia cases and 934 ancestry-matched population controls, we have detected genome-wide rare (<0.1% population frequency) TREs that have motifs with a length of 2–20 base pairs. We find that the proportion of individuals carrying rare TREs is significantly higher in the schizophrenia group. There is a significantly higher burden of rare TREs in schizophrenia cases than in controls in genic regions, particularly in postsynaptic genes, in genes overlapping brain expression quantitative trait loci, and in brain-expressed genes that are differentially expressed between schizophrenia cases and controls. We demonstrate that TRE-associated genes are more constrained and primarily impact synaptic and neuronal signaling functions. These results have been replicated in an independent Canadian sample that consisted of 252 schizophrenia cases of European ancestry and 222 ancestry-matched controls. Our results support the involvement of rare TREs in schizophrenia etiology.

DbBIP: a comprehensive bipolar disorder database for genetic research.

Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one’s own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein–protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL: http://dbbip.xialab.info

Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

BackgroundIdentifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ.MethodsWe systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI.ResultsBoth Sherlock (P = 3. 38 × 10−6) and SMR (P = 1. 90 × 10−8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus.ConclusionsWe confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.

Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood.

Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.

Innovative computational approaches shed light on genetic mechanisms underlying cognitive impairment among children born extremely preterm

BackgroundAlthough survival rates for infants born extremely preterm (gestation < 28 weeks) have improved significantly in recent decades, neurodevelopmental impairment remains a major concern. Children born extremely preterm remain at high risk for cognitive impairment from early childhood to adulthood. However, there is limited evidence on genetic factors associated with cognitive impairment in this population.MethodsFirst, we used a latent profile analysis (LPA) approach to characterize neurocognitive function at age 10 for children born extremely preterm. Children were classified into two groups: (1) no or low cognitive impairment, and (2) moderate-to-severe cognitive impairment. Second, we performed TOPMed-based genotype imputation on samples with genotype array data (n = 528). Third, we then conducted a genome-wide association study (GWAS) for LPA-inferred cognitive impairment. Finally, computational analysis was conducted to explore potential mechanisms underlying the variant x LPA association.ResultsWe identified two loci reaching genome-wide significance (p value < 5e-8): TEA domain transcription factor 4 (TEAD4 at rs11829294, p value = 2.40e-8) and syntaxin 18 (STX18 at rs79453226, p value = 1.91e-8). Integrative analysis with brain expression quantitative trait loci (eQTL), chromatin conformation, and epigenomic annotations suggests tetraspanin 9 (TSPAN9) and protein arginine methyltransferase 8 (PRMT8) as potential functional genes underlying the GWAS signal at the TEAD4 locus.ConclusionsWe conducted a novel computational analysis by utilizing an LPA-inferred phenotype with genetics data for the first time. This study suggests that rs11829294 and its LD buddies have potential regulatory roles on genes that could impact neurocognitive impairment for extreme preterm born children.

Transcriptome-Wide Association Study Provides Insights Into the Genetic Component of Gene Expression in Anxiety.

Anxiety disorders are common mental disorders that often result in disability. Recently, large-scale genome-wide association studies (GWASs) have identified several novel risk variants and loci for anxiety disorders (or anxiety traits). Nevertheless, how the reported risk variants confer risk of anxiety remains unknown. To identify genes whose cis-regulated expression levels are associated with risk of anxiety traits, we conducted a transcriptome-wide association study (TWAS) by integrating genome-wide associations from a large-scale GWAS (N = 175,163) (which evaluated anxiety traits based on Generalized Anxiety Disorder 2-item scale (GAD-2) score) and brain expression quantitative trait loci (eQTL) data (from the PsychENCODE and GTEx). We identified 19 and 17 transcriptome-wide significant (TWS) genes in the PsychENCODE and GTEx, respectively. Intriguingly, 10 genes showed significant associations with anxiety in both datasets, strongly suggesting that genetic risk variants may confer risk of anxiety traits by regulating the expression of these genes. Top TWS genes included RNF123, KANSL1-AS1, GLYCTK, CRHR1, DND1P1, MAPT and ARHGAP27. Of note, 25 TWS genes were not implicated in the original GWAS. Our TWAS identified 26 risk genes whose cis-regulated expression were significantly associated with anxiety, providing important insights into the genetic component of gene expression in anxiety disorders/traits and new clues for future drug development.

Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets

In recent decades, many genome-wide association studies on insomnia have reported numerous genes harboring multiple risk variants. Nevertheless, the molecular functions of these risk variants conveying risk to insomnia are still ill-studied. In the present study, we integrated GWAS summary statistics (N=386,533) with two independent brain expression quantitative trait loci (eQTL) datasets (N=329) to determine whether expression-associated SNPs convey risk to insomnia. Furthermore, we applied numerous bioinformatics analyses to highlight promising genes associated with insomnia risk. By using Sherlock integrative analysis, we detected 449 significant insomnia-associated genes in the discovery stage. These identified genes were significantly overrepresented in six biological pathways including Huntington’s disease (P=5.58 × 10−5), Alzheimer’s disease (P=5.58 × 10−5), Parkinson’s disease (P=6.34 × 10−5), spliceosome (P=1.17 × 10−4), oxidative phosphorylation (P=1.09 × 10−4), and wnt signaling pathways (P=2.07 × 10−4). Further, five of these identified genes were replicated in an independent brain eQTL dataset. Through a PPI network analysis, we found that there existed highly functional interactions among these five identified genes. Three genes of LDHA (P=0.044), DALRD3 (P=5.0 × 10−5), and HEBP2 (P=0.032) showed significantly lower expression level in brain tissues of insomnic patients than that in controls. In addition, the expression levels of these five genes showed prominently dynamic changes across different time points between behavioral states of sleep and sleep deprivation in mice brain cortex. Together, the evidence of the present study strongly suggested that these five identified genes may represent candidate genes and contributed risk to the etiology of insomnia.

Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder.

Genome-wide association studies (GWASs) have identified numerous single nucleotide polymorphisms (SNPs) associated with bipolar disorder (BD), but what the causal variants are and how they contribute to BD is largely unknown. In this study, we used FUMA, a GWAS annotation tool, to pinpoint potential causal variants and genes from the latest BD GWAS findings, and performed integrative analyses, including brain expression quantitative trait loci (eQTL), gene coexpression network, differential gene expression, protein-protein interaction, and brain intermediate phenotype association analysis to identify the functions of a prioritized gene and its connection to BD. Convergent lines of evidence prioritized protein-coding gene G Protein Nucleolar 3 (GNL3) as a BD risk gene, with integrative analyses revealing GNL3's roles in cell proliferation, neuronal functions, and brain phenotypes. We experimentally revealed that BD-related eQTL SNPs rs10865973, rs12635140, and rs4687644 regulate GNL3 expression using dual luciferase reporter assay and CRISPR interference experiment in human neural progenitor cells. We further identified that GNL3 knockdown and overexpression led to aberrant neuronal proliferation and differentiation, using two-dimensional human neural cell cultures and three-dimensional forebrain organoid model. This study gathers evidence that BD-related genetic variants regulate GNL3 expression which subsequently affects neuronal proliferation and differentiation.

Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants.

Most neuropsychiatric disease risk variants are in noncoding sequences and lack functional interpretation. Because regulatory sequences often reside in open chromatin, we reasoned that neuropsychiatric disease risk variants may affect chromatin accessibility during neurodevelopment. Using human induced pluripotent stem cell (iPSC)-derived neurons that model developing brains, we identified thousands of genetic variants exhibiting allele-specific open chromatin (ASoC). These neuronal ASoCs were partially driven by altered transcription factor binding, overrepresented in brain gene enhancers and expression quantitative trait loci, and frequently associated with distal genes through chromatin contacts. ASoCs were enriched for genetic variants associated with brain disorders, enabling identification of functional schizophrenia risk variants and their cis-target genes. This study highlights ASoC as a functional mechanism of noncoding neuropsychiatric risk variants, providing a powerful framework for identifying disease causal variants and genes.

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

BackgroundRecent 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. MethodsWe 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. ResultsWe 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. ConclusionsOur 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.

Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms.

Stroke ranks the second leading cause of death among people over the age of 60 in the world. Stroke is widely regarded as a complex disease that is affected by genetic and environmental factors. Evidence from twin and family studies suggests that genetic factors may play an important role in its pathogenesis. Therefore, research on the genetic association of susceptibility genes can help understand the mechanism of stroke. Genome-wide association study (GWAS) has found a large number of stroke-related loci, but their mechanism is unknown. In order to explore the function of single-nucleotide polymorphisms (SNPs) at the molecular level, in this paper, we integrated 8 GWAS datasets with brain expression quantitative trait loci (eQTL) dataset to identify SNPs and genes which are related to four types of stroke (ischemic stroke, large artery stroke, cardioembolic stroke, small vessel stroke). Thirty-eight SNPs which can affect 14 genes expression are found to be associated with stroke. Among these 14 genes, 10 genes expression are associated with ischemic stroke, one gene for large artery stroke, six genes for cardioembolic stroke and eight genes for small vessel stroke. To explore the effects of environmental factors on stroke, we identified methylation susceptibility loci associated with stroke using methylation quantitative trait loci (MQTL). Thirty-one of these 38 SNPs are at greater risk of methylation and can significantly change gene expression level. Overall, the genetic pathogenesis of stroke is explored from locus to gene, gene to gene expression and gene expression to phenotype.

Targeted sequencing of the LRRTM gene family in suicide attempters with bipolar disorder.

Glutamatergic signaling is the primary excitatory neurotransmission pathway in the brain, and its relationship to neuropsychiatric disorders is of considerable interest. Our previous attempted suicide genome-wide association study, and numerous studies investigating gene expression, genetic variation, and DNA methylation have implicated aberrant glutamatergic signaling in suicide risk. The glutamatergic pathway gene LRRTM4 was an associated gene identified in our attempted suicide genome-wide association study, with association support seen primarily in females. Recent evidence has also shown that glutamatergic signaling is partly regulated by sex-related hormones. The LRRTM gene family encodes neuronal leucine-rich transmembrane proteins that localize to and promote glutamatergic synapse development. In this study, we sequenced the coding and regulatory regions of all four LRRTM gene members plus a large intronic region of LRRTM4 in 476 bipolar disorder suicide attempters and 473 bipolar disorder nonattempters. We identified two male-specific variants, one female- and five male-specific haplotypes significantly associated with attempted suicide in LRRTM4. Furthermore, variants within significant haplotypes may be brain expression quantitative trait loci for LRRTM4 and some of these variants overlap with predicted hormone response elements. Overall, these results provide supporting evidence for a sex-specific association of genetic variation in LRRTM4 with attempted suicide.

Integrating genome-wide association study and expression quantitative trait loci data identifies NEGR1 as a causal risk gene of major depression disorder

BackgroundGenome-wide association studies (GWAS) have identified several genetic variants associated with major depression disorder (MDD). However, pinpointing the causal variants which are responsible for the association signal at a risk locus remains a major challenge. MethodsWe used Summary data-based Mendelian Randomization (SMR) with Psychiatric Genomics Consortium (PGC) GWAS summary and brain expression quantitative trait loci (eQTL) data to identify genes whose expression levels are causally associated with MDD. Then we performed differential expression analysis, methylation quantitative trait loci analysis, and cognitive genetics analysis to investigate the potential roles of risk genes in the pathogenesis of MDD. ResultsThrough SMR integrative analysis, we identified the SNP rs10789336 located in Neuronal growth regulator 1 (NEGR1) gene significantly affected the expression level of RPL31P12 in brain tissues and contributed to the risk of MDD (P = 1.96 × 10−6). Consistently, the SNP rs10789336 was associated with the methylation levels of three nearby DNA methylation sites, including cg09256413 (NEGR1, P=1.72 × 10−10), cg11418303 (prostaglandin E receptor 3 [PTGER3], P = 4.78 × 10−6), and cg23032215 (ZRANB2 antisense RNA 2 [ZRANB2-AS2], P = 1.23 × 10−4). Differential expression analysis suggested that the NEGR1 gene was upregulated in prefrontal cortex (P = 5.14 × 10−3). Cognitive genetics analysis showed that the SNP rs10789336 was associated with cognitive performance (P = 2.41 × 10−16), educational attainment (P = 1.75 × 10−14), general cognitive function (P = 2.65 × 10−12), and verbal numerical reasoning (P = 1.36 × 10−12). ConclusionCollectively, our results revealed that the SNP rs10789336 in NEGR1 might confer risk to MDD. Further investigation of the roles of NEGR1 in the pathogenesis of MDD is warranted.

Synergistic effects of common schizophrenia risk variants.

The mechanisms by which common risk variants of small effect interact to contribute to complex genetic disorders remain unclear. Here, we apply a genetic approach, using isogenic human induced pluripotent stem cells (hiPSCs), to evaluate the effects of schizophrenia-associated common variants predicted to function as brain expression quantitative trait loci (SZ-eQTLs). By integrating CRISPR-mediated gene editing, activation and repression technologies to study one putative SZ-eQTL (FURIN rs4702) and four top-ranked SZ-eQTL genes (FURIN, SNAP91, TSNARE1, CLCN3), our platform resolves pre- and post-synaptic neuronal deficits, recapitulates genotype-dependent gene expression differences, and identifies convergence downstream of SZ-eQTL gene perturbations. Our observations highlight the cell-type-specific effects of common variants and demonstrate a synergistic effect between SZ-eQTL genes that converges on synaptic function. We propose that the links between rare and common variants implicated in psychiatric disease risk constitute a potentially generalizable phenomenon occurring more widely in complex genetic disorders.

Identification of the primate-specific gene BTN3A2 as an additional schizophrenia risk gene in the MHC loci

BackgroundSchizophrenia is a complex mental disorder resulting in poor life quality and high social and economic burden. Despite the fact that genome-wide association studies (GWASs) have successfully identified a number of risk loci for schizophrenia, identifying the causal genes at the risk loci and elucidating their roles in disease pathogenesis remain major challenges. MethodsThe summary data-based Mendelian randomization analysis (SMR) was used to integrate a large-scale GWAS of schizophrenia with brain expression quantitative trait loci (eQTL) data and brain methylation expression quantitative trait loci (meQTL) data, to identify novel risk gene(s) for schizophrenia. We then analyzed the mRNA expression and methylation statuses of the gene hit BTN3A2 during the early brain development. Electrophysiological analyses of CA1 pyramidal neurons were performed to evaluate the excitatory and inhibitory synaptic activity after overexpression of BTN3A2 in rat hippocampal slices. Cell surface binding assay was used to test the interaction of BTN3A2 and neurexins. FindingsWe identified BTN3A2 as a potential risk gene for schizophrenia. The mRNA expression and methylation data showed that BTN3A2 expression in human brain is highest post-natally. Further electrophysiological analyses of rat hippocampal slices showed that BTN3A2 overexpression specifically suppressed the excitatory synaptic activity onto CA1 pyramidal neurons, most likely through its interaction with the presynaptic adhesion molecule neurexins. InterpretationIncreased expression of BTN3A2 might confer risk for schizophrenia by altering excitatory synaptic function. Our result constitutes a paradigm for distilling risk gene using an integrative analysis and functional characterization in the post-GWAS era. FundThis study was supported by the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB02020003 to Y-GY), the National Natural Science Foundation of China (31730037 to Y-GY), and the Bureau of Frontier Sciences and Education, Chinese Academy of Sciences (QYZDJ-SSW-SMC005 to Y-GY).

Integration of GWAS and brain eQTL identifies FLOT1 as a risk gene for major depressive disorder

Major depressive disorder (MDD) is the most prevalent mental disorder that affects more than 200 million people worldwide. Recent large-scale genome-wide association studies (GWAS) have identified multiple risk variants that show robust association with MDD. Nevertheless, how the identified risk variants confer risk of MDD remains largely unknown. To identify risk variants that are associated with gene expression in human brain and to identify genes whose expression change may contribute to the susceptibility of MDD, we systematically integrated the genetic associations from a large-scale MDD GWAS (N = 480,359) and brain expression quantitative trait loci (eQTL) data (N = 494) using a Bayesian statistical framework (Sherlock). Sherlock integrative analysis showed that FLOT1 was significantly associated with MDD (P = 6.02 × 10-6), suggesting that risk variants may contribute to MDD susceptibility through affecting FLOT1 expression. We further examined the expression level of FLOT1 in MDD cases and controls and found that FLOT1 was significantly upregulated in brains and peripheral blood of MDD cases compared with controls (European sample). Interestingly, we found that FLOT1 expression was also significantly upregulated in peripheral blood of first-episode drug-naive MDD cases compared with controls (P = 1.01 × 10-7, Chinese sample). Our study identified FLOT1 as a novel MDD risk gene whose expression level may play a role in MDD. In addition, our findings also suggest that risk variants may confer risk of MDD through affecting expression of FLOT1. Further functional investigation of FLOT1 may provide new insights for MDD pathogenesis.

M97 - TRANSCRIPTIONAL SIGNATURES OF CHILDHOOD ONSET SCHIZOPHRENIA IN HIPSC-DERIVED NPCS AND NEURONS ARE CONCORDANT WITH SIGNATURES FROM POST MORTEM ADULT BRAINS

Background Large-scale Genome Wide Association Studies (GWAS) revealed that Schizophrenia (SZ) risk reflects both rare and common variants. Whereas highly penetrant rare variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. Childhood-Onset-SZ (COS) patients have both a higher rate of rare SZ-associated Copy Number Variations (CNVs), and also stronger common SZ polygenic risk scores. Methods We developed a case/control hiPSC cohort (12 individuals with COS and 10 controls) with which to conduct a comprehensive evaluation of global gene expression in hiPSC-derived neural progenitor cells (NPCs) and neurons. Results Although we identified many sources of variation across our 94 RNAseq samples, this was partially addressed by establishing a rigorous series of bioinformatic practices to reduce and assess the variance inherent in this approach. We report that the donor-specific signal is enriched for post- mortem brain Expression Quantitative Trait Loci (eQTLs), but also that there is a significant correlation between the case/control differential expression observed in our hiPSC-derived COS neurons and the CommonMind Consortium post-mortem study. While the issue of small sample size is shared between post-mortem and hiPSC-based studies, and may be exacerbated in hiPSC-based experiments through the variability that arises as a result of the reprogramming and neural differentiation processes, we find that the common variation effects predicted in GWAS and detected in much larger post-mortem analyses are reflected in hiPSC-neurons. Discussion We predict a growing convergence of common variation findings between hiPSC and post-mortem studies as both approaches expand to larger cohort sizes.