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

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BACKGROUND

The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS

We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS

We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION

Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection

Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.

The roles of FLOT1 in human diseases (Review)

FLOT1, a scaffold protein of lipid rafts, is involved in several biological processes, including lipid raft protein‑-dependent or clathrin‑independent endocytosis, and the formation of hippocampal synapses, amongst others. Increasing evidence has shown that FLOT1 can function as both a cancer promoter and cancer suppressor dependent on the type of cancer. FLOT1 can affect the occurrence and development of several types of cancer by affecting epithelial‑mesenchymal transition, proliferation of cancer cells, and relevant signaling pathways, and is regulated by long intergenic non‑coding RNAs or microRNAs. In the nervous system, overexpression or abnormally low expression of FLOT1 may lead to the occurrence of neurological diseases, such as Alzheimer's disease, Parkinson's disease, major depressive disorder and other diseases. Additionally, it is also associated with dilated cardiomyopathy, pathogenic microbial infection, diabetes‑related diseases, and gynecological diseases, amongst other diseases. In the present review, the structure and localization of FLOT1, as well as the physiological processes it is involved in are reviewed, and then the upstream and downstream regulation of FLOT1 in human disease, particularly in different types of cancer and neurological diseases are discussed, with a focus on potentially targeting FLOT1 for the clinical treatment of several diseases.

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.

Identification of MKNK1 and TOP3A as ovarian endometriosis risk-associated genes using integrative genomic analyses and functional experiments

The risk of endometriosis (EM), which is a common complex gynaecological disease, is related to genetic predisposition. However, it is unclear how genetic variants confer the risk of EM. Here, via Sherlock integrative analysis, we combined large-scale genome-wide association studies (GWAS) summary statistics on EM (N = 245,494) with a blood-based eQTL dataset (N = 1490) to identify EM risk-related genes. For validation, we leveraged two independent eQTL datasets (N = 769) for integration with the GWAS data. Thus, we prioritised 14 genes, including GIMAP4, TOP3A, and NMNAT3, which showed significant association with susceptibility to EM. We also utilised two independent methods, Multi-marker Analysis of GenoMic Annotation and S-PrediXcan, to further validate the EM risk-associated genes. Moreover, protein-protein interaction network analysis showed the 14 genes were functionally connected. Functional enrichment analyses further demonstrated that these genes were significantly enriched in metabolic and immune-related pathways. Differential gene expression analysis showed that in peripheral blood samples from patients with ovarian EM, TOP3A, MKNK1, SIPA1L2, and NUCB1 were significantly upregulated, while HOXB2, GIMAP5, and MGMT were significantly downregulated compared with their expression levels in samples from the controls. Immunohistochemistry further confirmed the increased expression levels of MKNK1 and TOP3A in the ectopic and eutopic endometrium compared to normal endometrium, while HOBX2 was downregulated in the endometrium of women with ovarian EM. Finally, in ex vivo functional experiments, MKNK1 knockdown inhibited ectopic endometrial stromal cells (EESCs) migration and invasion. TOP3A knockdown inhibited EESCs proliferation, migration, and invasion, while promoting their apoptosis. Convergent lines of evidence suggested that MKNK1 and TOP3A are novel EM risk-related genes.

Boosting of tau protein aggregation by CD40 and CD48 gene expression in Alzheimer's disease

Neurodegenerative diseases result from the interplay of abnormal gene expression and various pathological factors. Therefore, a disease-specific integrative genetic approach is required to understand the complexities and causes of target diseases. Recent studies have identified the correlation between genes encoding several transmembrane proteins, such as the cluster of differentiation (CD) and Alzheimer's disease (AD) pathogenesis. In this study, CD48 and CD40 gene expression in AD, a neurodegenerative disease, was analyzed to infer this link. Total RNA sequencing was performed using an Alzheimer's disease mouse model brain and blood, and gene expression was determined using a genome-wide association study (GWAS). We observed a marked elevation of CD48 and CD40 genes in Alzheimer's disease. Indeed, the upregulation of both CD48 and CD40 genes was significantly increased in the severe Alzheimer's disease group. With the elevation of CD48 and CD40 genes in Alzheimer's disease, associations of protein levels were also markedly increased in tissues. In addition, overexpression of CD48 and CD40 genes triggered tau aggregation, and co-expression of these genes accelerated aggregation. The nuclear factor kappa B (NF-ĸB) signaling pathway was enriched by CD48 and CD40 gene expression: it was also associated with tau pathology. Our data suggested that the CD48 and CD40 genes are novel AD-related genes, and this approach may be useful as a diagnostic or therapeutic target for the disease.

Benchmarking post-GWAS analysis tools in major depression: Challenges and implications

Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.

Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression

Understanding the molecular basis of major depression is critical for identifying new potential biomarkers and drug targets to alleviate its burden on society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of major depression-associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with major depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis and alteration of transcription factor binding sites. The fine-mapping process uncovered putative causally associated variants whose proximal genes were linked with major depression pathophysiology. Four colocalizing genetic variants altered the expression of five genes, highlighting the role of SLC12A5 in neuronal chlorine homeostasis and MYRF in nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of rs62259947 in modulating P4HTM expression by altering the YY1 binding site, altogether regulating hypoxia response. Overall, our pipeline could prioritize putative causal genetic variants in major depression. More importantly, it can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

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.

Serotonin Receptor and Transporter Endocytosis Is an Important Factor in the Cellular Basis of Depression and Anxiety

Depression and anxiety are common, debilitating psychiatric conditions affecting millions of people throughout the world. Current treatments revolve around selective serotonin reuptake inhibitors (SSRIs), yet these drugs are only moderately effective at relieving depression. Moreover, up to 30% of sufferers are SSRI non-responders. Endocytosis, the process by which plasma membrane and extracellular constituents are internalized into the cell, plays a central role in the regulation of serotonin (5-hydroxytryptophan, 5-HT) signaling, SSRI function and depression and anxiety pathogenesis. Despite their therapeutic potential, surprisingly little is known about the endocytosis of the serotonin receptors (5-HT receptors) or the serotonin transporter (SERT). A subset of 5-HT receptors are endocytosed by clathrin-mediated endocytosis following serotonin binding, while for the majority of 5-HT receptors the endocytic regulation is not known. SERT internalizes serotonin from the extracellular space into the cell to limit the availability of serotonin for receptor binding and signaling. Endocytosis of SERT reduces serotonin uptake, facilitating serotonin signaling. SSRIs predominantly inhibit SERT, preventing serotonin uptake to enhance 5-HT receptor signaling, while hallucinogenic compounds directly activate specific 5-HT receptors, altering their interaction with endocytic adaptor proteins to induce alternate signaling outcomes. Further, multiple polymorphisms and transcriptional/proteomic alterations have been linked to depression, anxiety, and SSRI non-response. In this review, we detail the endocytic regulation of 5-HT receptors and SERT and outline how SSRIs and hallucinogenic compounds modulate serotonin signaling through endocytosis. Finally, we will examine the deregulated proteomes in depression and anxiety and link these with 5-HT receptor and SERT endocytosis. Ultimately, in attempting to integrate the current studies on the cellular biology of depression and anxiety, we propose that endocytosis is an important factor in the cellular basis of depression and anxiety. We will highlight how a thorough understanding 5-HT receptor and SERT endocytosis is integral to understanding the biological basis of depression and anxiety, and to facilitate the development of a next generation of specific, efficacious antidepressant treatments.

Biological, Psychological, and Social Determinants of Depression: A Review of Recent Literature

Depression is one of the leading causes of disability, and, if left unmanaged, it can increase the risk for suicide. The evidence base on the determinants of depression is fragmented, which makes the interpretation of the results across studies difficult. The objective of this study is to conduct a thorough synthesis of the literature assessing the biological, psychological, and social determinants of depression in order to piece together the puzzle of the key factors that are related to this condition. Titles and abstracts published between 2017 and 2020 were identified in PubMed, as well as Medline, Scopus, and PsycInfo. Key words relating to biological, social, and psychological determinants as well as depression were applied to the databases, and the screening and data charting of the documents took place. We included 470 documents in this literature review. The findings showed that there are a plethora of risk and protective factors (relating to biological, psychological, and social determinants) that are related to depression; these determinants are interlinked and influence depression outcomes through a web of causation. In this paper, we describe and present the vast, fragmented, and complex literature related to this topic. This review may be used to guide practice, public health efforts, policy, and research related to mental health and, specifically, depression.

A missense variant in NDUFA6 confers schizophrenia risk by affecting YY1 binding and NAGA expression

Genome-wide association studies (GWASs) have revealed that genetic variants at the 22q13.2 risk locus were robustly associated with schizophrenia. However, the causal variants at this risk locus and their roles in schizophrenia remain elusive. Here we identify the risk missense variant rs1801311 (located in the 1st exon of NDUFA6 gene) as likely causal for schizophrenia at 22q13.2 by disrupting binding of YY1, TAF1, and POLR2A. We systematically elucidated the regulatory mechanisms of rs1801311 and validated the regulatory effect of this missense variant. Intriguingly, rs1801311 physically interacted with NAGA (encodes the alpha-N-acetylgalactosaminidase, which is mainly involved in regulating metabolisms of glycoproteins and glycolipids in lysosome) and showed the most significant association with NAGA expression in the human brain, with the risk allele (G) associated with higher NAGA expression. Consistent with eQTL analysis, expression analysis showed that NAGA was significantly upregulated in brains of schizophrenia cases compared with controls, further supporting that rs1801311 may confer schizophrenia risk by regulating NAGA expression. Of note, we found that NAGA regulates important neurodevelopmental processes, including proliferation and differentiation of neural stem cells. Transcriptome analysis corroborated that NAGA regulates pathways associated with neuronal differentiation. Finally, we independently confirmed the association between rs1801311 and schizophrenia in a large Chinese cohort. Our study elucidates the regulatory mechanisms of the missense schizophrenia risk variant rs1801311 and provides mechanistic links between risk variant and schizophrenia etiology. In addition, this study also revealed the novel role of coding variants in gene regulation and schizophrenia risk, i.e., genetic variant in coding region of a specific gene may confer disease risk through regulating distal genes (act as regulatory variant for distal genes).

From genetics to systems biology of stress-related mental disorders

Many individuals will be exposed to some form of traumatic stress in their lifetime which, in turn, increases the likelihood of developing stress-related disorders such as post-traumatic stress disorder (PTSD), major depressive disorder (MDD) and anxiety disorders (ANX). The development of these disorders is also influenced by genetics and have heritability estimates ranging between ∼30 and 70%. In this review, we provide an overview of the findings of genome-wide association studies for PTSD, depression and ANX, and we observe a clear genetic overlap between these three diagnostic categories. We go on to highlight the results from transcriptomic and epigenomic studies, and, given the multifactorial nature of stress-related disorders, we provide an overview of the gene-environment studies that have been conducted to date. Finally, we discuss systems biology approaches that are now seeing wider utility in determining a more holistic view of these complex disorders.

The genetic basis of major depression

Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene-environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care.

Potential depression and antidepressant-response biomarkers in human lymphoblast cell lines from treatment-responsive and treatment-resistant subjects: roles of SSRIs and omega-3 polyunsaturated fatty acids

While several therapeutic strategies exist for depression, most antidepressant drugs require several weeks before reaching full biochemical efficacy and remission is not achieved in many patients. Therefore, biomarkers for depression and drug-response would help tailor treatment strategies. This study made use of banked human lymphoblast cell lines (LCLs) from normal and depressed subjects; the latter divided into remitters and non-remitters. Due to the fact that previous studies have shown effects on growth factors, cytokines, and elements of the cAMP-generating system as potential biomarkers for depression and antidepressant action, these were examined in LCLs. Initial gene and protein expression profiles for signaling cascades related to neuroendocrine and inflammatory functions differ among the three groups. Growth factor genes, including VEGFA and BDNF were significantly down-regulated in cells from depressed subjects. In addition, omega-3 polyunsaturated fatty acids (n-3 PUFAs) have been reported to act as both antidepressants and anti-inflammatories, but the mechanisms for these effects are not established. Here we showed that n-3 PUFAs and escitalopram (selective serotonin reuptake inhibitors, SSRIs) treatment increased adenylyl cyclase (AC) and BDNF gene expression in LCLs. These data are consistent with clinical observations showing that n-3 PUFA and SSRI have antidepressant affects, which may be additive. Contrary to observations made in neuronal and glial cells, n-3 PUFA treatment attenuated cAMP accumulation in LCLs. However, while lymphoblasts show paradoxical responses to neurons and glia, patient-derived lymphoblasts appear to carry potential depression biomarkers making them an important tool for studying precision medicine in depressive patients. Furthermore, these data validate usefulness of n-3 PUFAs in treatment for depression.

Transcriptome-wide association study identifies new susceptibility genes and pathways for depression

Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.

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.

Integration of summary data from GWAS and eQTL studies identified novel risk genes for coronary artery disease

Several genetic loci have been reported to be significantly associated with coronary artery disease (CAD) by multiple genome-wide association studies (GWAS). Nevertheless, the biological and functional effects of these genetic variants on CAD remain largely equivocal. In the current study, we performed an integrative genomics analysis by integrating large-scale GWAS data (N = 459,534) and 2 independent expression quantitative trait loci (eQTL) datasets (N = 1890) to determine whether CAD-associated risk single nucleotide polymorphisms (SNPs) exert regulatory effects on gene expression. By using Sherlock Bayesian, MAGMA gene-based, multidimensional scaling (MDS), functional enrichment, and in silico permutation analyses for independent technical and biological replications, we highlighted 4 susceptible genes (CHCHD1, TUBG1, LY6G6C, and MRPS17) associated with CAD risk. Based on the protein-protein interaction (PPI) network analysis, these 4 genes were found to interact with each other. We detected a remarkably altered co-expression pattern among these 4 genes between CAD patients and controls. In addition, 3 genes of CHCHD1 (P = .0013), TUBG1 (P = .004), and LY6G6C (P = .038) showed significantly different expressions between CAD patients and controls. Together, we provide evidence to support that these identified genes such as CHCHD1 and TUBG1 are indicative factors of CAD.

Pharmacogenomics with red cells: a model to study protein variants of drug transporter genes

The PharmacoScan pharmacogenomics platform screens for variation in genes that affect drug absorption, distribution, metabolism, elimination, immune adverse reactions and targets. Among the 1,191 genes tested on the platform, 12 genes are expressed in the red cell membrane: ABCC1, ABCC4, ABCC5, ABCG2, CFTR, SLC16A1, SLC19A1, SLC29A1, ATP7A, CYP4F3, EPHX1 and FLOT1. These genes represent 5 ATP-binding cassette proteins, 3 solute carrier proteins, 1 ATP transport protein and 3 genes associated with drug metabolism and adverse drug reactions. Only ABCG2 and SLC29A1 encode blood group systems, JR and AUG, respectively. We propose red cells as an ex vivo model system to study the effect of heritable variants in genes encoding the transport proteins on the pharmacokinetics of drugs. Altered pharmacodynamics in red cells could also cause adverse reactions, such as haemolysis, hitherto unexplained by other mechanisms.

Multi-omics analyses of cognitive traits and psychiatric disorders highlights brain-dependent mechanisms

Integrating findings from genome-wide association studies with molecular datasets can develop insight into the underlying functional mechanisms responsible for trait-associated genetic variants. We have applied the principles of Mendelian randomization (MR) to investigate whether brain-derived gene expression (n = 1194) may be responsible for mediating the effect of genetic variants on eight cognitive and psychological outcomes (attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, depression, intelligence, insomnia, neuroticism and schizophrenia). Transcriptome-wide analyses identified 83 genes associated with at least one outcome (PBonferroni < 6.72 × 10-6), with multiple-trait colocalization also implicating changes to brain-derived DNA methylation at nine of these loci. Comparing effects between outcomes identified evidence of enrichment which may reflect putative causal relationships, such as an inverse relationship between genetic liability towards schizophrenia risk and cognitive ability in later life. Repeating these analyses in whole blood (n = 31 684), we replicated 58.2% of brain-derived effects (based on P < 0.05). Finally, we undertook phenome-wide evaluations at associated loci to investigate pleiotropic effects with 700 complex traits. This highlighted pleiotropic loci such as FURIN (initially implicated in schizophrenia risk (P = 1.05 × 10-7)) which had evidence of an effect on 28 other outcomes, as well as genes which may have a more specific role in disease pathogenesis (e.g. SLC12A5 which only provided evidence of an effect on depression (P = 7.13 × 10-10)). Our results support the utility of whole blood as a valuable proxy for informing initial target identification but also suggest that gene discovery in a tissue-specific manner may be more informative. Finally, non-pleiotropic loci highlighted by our study may be of use for therapeutic translational endeavours.

Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

Background

Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown.

Methods

In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma.

Results

In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10^− 6), type I diabetes mellitus (Corrected P = 7.09 × 10^− 5), and asthma (Corrected P = 1.72 × 10^− 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (P_eQTL = 2.98 × 10^− 8 and P_GWAS = 3.40 × 10^− 8), rs11265180 (P_eQTL = 6.0 × 10^− 6 and P_GWAS = 1.99 × 10^− 3), and rs1867087 (P_eQTL = 1.0 × 10^− 4 and P_GWAS = 1.84 × 10^− 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10^− 6).

Conclusions

Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BACKGROUND

Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.

METHODS

In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.

RESULTS

Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.

CONCLUSIONS

We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

Regional Variation of Splicing QTLs in Human Brain

A major question in human genetics is how sequence variants of broadly expressed genes produce tissue- and cell type-specific molecular phenotypes. Genetic variation of alternative splicing is a prevalent source of transcriptomic and proteomic diversity in human populations. We investigated splicing quantitative trait loci (sQTLs) in 1,209 samples from 13 human brain regions, using RNA sequencing (RNA-seq) and genotype data from the Genotype-Tissue Expression (GTEx) project. Hundreds of sQTLs were identified in each brain region. Some sQTLs were shared across brain regions, whereas others displayed regional specificity. These “regionally ubiquitous” and “regionally specific” sQTLs showed distinct positional distributions of single-nucleotide polymorphisms (SNPs) within and outside essential splice sites, respectively, suggesting their regulation by distinct molecular mechanisms. Integrating the binding motifs and expression patterns of RNA binding proteins with exon splicing profiles, we uncovered likely causal variants underlying brain region-specific sQTLs. Notably, SNP rs17651213 created a putative binding site for the splicing factor RBFOX2 and was associated with increased splicing of MAPT exon 3 in cerebellar tissues, where RBFOX2 was highly expressed. Overall, our study reveals a more comprehensive spectrum and regional variation of sQTLs in human brain and demonstrates that such regional variation can be used to fine map potential causal variants of sQTLs and their associated neurological diseases.

Ex vivo Quantitative Proteomic Analysis of Serotonin Transporter Interactome: Network Impact of the SERT Ala56 Coding Variant

Altered serotonin (5-HT) signaling is associated with multiple brain disorders, including major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and autism spectrum disorder (ASD). The presynaptic, high-affinity 5-HT transporter (SERT) tightly regulates 5-HT clearance after release from serotonergic neurons in the brain and enteric nervous systems, among other sites. Accumulating evidence suggests that SERT is dynamically regulated in distinct activity states as a result of environmental and intracellular stimuli, with regulation perturbed by disease-associated coding variants. Our lab identified a rare, hypermorphic SERT coding substitution, Gly56Ala, in subjects with ASD, finding that the Ala56 variant stabilizes a high-affinity outward-facing conformation (SERT^∗) that leads to elevated 5-HT uptake in vitro and in vivo. Hyperactive SERT Ala56 appears to preclude further activity enhancements by p38α mitogen-activated protein kinase (MAPK) and can be normalized by pharmacological p38α MAPK inhibition, consistent with SERT Ala56 mimicking, constitutively, a high-activity conformation entered into transiently by p38α MAPK activation. We hypothesize that changes in SERT-interacting proteins (SIPs) support the shift of SERT into the SERT^∗ state which may be captured by comparing the composition of SERT Ala56 protein complexes with those of wildtype (WT) SERT, defining specific interactions through comparisons of protein complexes recovered using preparations from SERT^–/– (knockout; KO) mice. Using quantitative proteomic-based approaches, we identify a total of 459 SIPs, that demonstrate both SERT specificity and sensitivity to the Gly56Ala substitution, with a striking bias being a loss of SIP interactions with SERT Ala56 compared to WT SERT. Among this group are previously validated SIPs, such as flotillin-1 (FLOT1) and protein phosphatase 2A (PP2A), whose functions are believed to contribute to SERT microdomain localization and regulation. Interestingly, our studies nominate a number of novel SIPs implicated in ASD, including fragile X mental retardation 1 protein (FMR1) and SH3 and multiple ankyrin repeat domains protein 3 (SHANK3), of potential relevance to long-standing evidence of serotonergic contributions to ASD. Further investigation of these SIPs, and the broader networks they engage, may afford a greater understanding of ASD as well as other brain and peripheral disorders associated with perturbed 5-HT signaling.

Identification of a functional human-unique 351-bp Alu insertion polymorphism associated with major depressive disorder in the 1p31.1 GWAS risk loci

Genome-wide association studies (GWAS) have reported substantial single-nucleotide polymorphisms (SNPs) associated with major depressive disorder (MDD), but the underlying functional variations in the GWAS risk loci are unclear. Here we show that the European MDD genome-wide risk-associated allele of rs12129573 at 1p31.1 is associated with MDD in Han Chinese, and this SNP is in strong linkage disequilibrium (LD) with a human-unique Alu insertion polymorphism (rs70959274) in the 5' flanking region of a long non-coding RNA (lncRNA) LINC01360 (Long Intergenic Non-Protein Coding RNA 1360), which is preferably expressed in human testis in the currently available expression datasets. The risk allele at rs12129573 is almost completely linked with the absence of this Alu insertion. The Alu insertion polymorphism (rs70959274) is significantly associated with a lower RNA level of LINC01360 and acts as a transcription silencer likely through modulating the methylation of its internal CpG sites. Luciferase assays confirm that the presence of Alu insertion at rs70959274 suppresses transcriptional activities in human cells, and deletion of the Alu insertion through CRISPR/Cas9-directed genome editing increases RNA expression of LINC01360. Deletion of the Alu insertion in human cells also leads to dysregulation of gene expression, biological processes and pathways relevant to MDD, such as the alterations of mRNA levels of DRD2 and FLOT1, transcription of genes involved in synaptic transmission, neurogenesis, learning or memory, and the PI3K-Akt signaling pathway. In summary, we identify a human-unique DNA repetitive polymorphism in robust LD with the MDD risk-associated SNP at the prominent 1p31.1 GWAS loci, and offer insights into the molecular basis of the illness.

Integrative genomics analysis of eQTL and GWAS summary data identifies PPP1CB as a novel bone mineral density risk genes

In recent years, multiple genome-wide association studies (GWAS) have identified numerous susceptibility variants and risk genes that demonstrate significant associations with bone mineral density (BMD). However, exploring how these genetic variants contribute risk to BMD remains a major challenge. We systematically integrated two independent expression quantitative trait loci (eQTL) data (N = 1890) and GWAS summary statistical data of BMD (N = 142,487) using Sherlock integrative analysis to reveal whether expression-associated variants confer risk to BMD. By using Sherlock integrative analysis and MAGMA gene-based analysis, we found there existed 36 promising genes, for example, PPP1CB, XBP1, and FDFT1, whose expression alterations may contribute susceptibility to BMD. Through a protein-protein interaction (PPI) network analysis, we further prioritized the PPP1CB as a hub gene that has interactions with predicted genes and BMD-associated genes. Two eSNPs of rs9309664 (PeQTL = 1.42 × 10-17 and PGWAS = 1.40 × 10-11) and rs7475 (PeQTL = 2.10 × 10-6 and PGWAS = 1.70 × 10-7) in PPP1CB were identified to be significantly associated with BMD risk. Consistently, differential gene expression analysis found that the PPP1CB gene showed significantly higher expression in low BMD samples than that in high BMD samples based on two independent expression datasets (P = 0.0026 and P = 0.043, respectively). Together, we provide a convergent line of evidence to support that the PPP1CB gene involves in the etiology of osteoporosis.

Integration of genome-wide association study and expression quantitative trait loci data identifies AIM2 as a risk gene of periodontitis

AIM

To identify risk variants associated with gene expression in peripheral blood and to identify genes whose expression change may contribute to the susceptibility to periodontitis.

MATERIAL AND METHODS

We systematically integrated the genetic associations from a recent large-scale periodontitis GWAS and blood expression quantitative trait loci (eQTL) data using Sherlock, a Bayesian statistical framework. We then validated the potential causal genes in independent gene expression data sets. Gene co-expression analysis was used to explore the functional relationship for the identified causal genes.

RESULTS

Sherlock analysis identified 10 genes (rs7403881 for MT1L, rs12459542 for SIGLEC5, rs12459542 for SIGLEC14, rs6680386 for S100A12, rs10489524 for TRIM33, rs11962642 for HIST1H3E, rs2814770 for AIM2, rs7593959 for FASTKD2, rs10416904 for PKN1, and rs10508204 for WDR37) whose expression may influence periodontitis. Among these genes, AIM2 was consistent significantly upregulated in periodontium of periodontitis patients across four data sets. The cis-eQTL (rs2814770, ~16 kb upstream of AIM2) showed significant association with AIM2 (p = 6.63 × 10^-6 ) and suggestive association with periodontitis (p = 7.52 × 10^-4 ). We also validated the significant association between rs2814770 and AIM2 expression in independent expression data set. Pathway analysis revealed that genes co-expressed with AIM2 were significantly enriched in immune- and inflammation-related pathways.

CONCLUSION

Our findings implicate that AIM2 is a susceptibility gene, expression of which in gingiva may influence periodontitis risk. Further functional investigation of AIM2 may provide new insight for periodontitis pathogenesis.

An association study between methamphetamine use disorder with psychosis and polymorphisms in MiRNA

BACKGROUND

Methamphetamine (MA) is an addictive psychostimulant substance that mainly leads to schizophrenia-like psychotic symptoms. The expression of miRNAs in brain plays an important role in neurological disorders and may affect by genetic variant(s) in the target site (MiRSNPs). In this study, we investigated whether polymorphisms in miRNAs are associated with MA disorder with psychosis.

METHODS

We carried out a case-control association study in 400 MA users with psychotic characters and 448 controls. Six MiRSNPs with predicted functional relevance miRNAs (miR-181b, miR-181a, miR-15b, miR-let-7e and miRlet-7d) were selected for genotyping. Allele and genotype frequencies were compared between MA users and healthy individuals. The expression of five miRNAs were measured by quantitative real-time RT-PCR in 55 cases and 57 controls. We also explored an expression Quantitative Trait Loci (eQTL) analysis based on the miRNAs expression and SNP genotype.

RESULTS

The SNP rs10760371 within miR-181a was nominally associated with MA disorder (P = 0.046). For rs1099308, rs10760371 and rs10993081 in strong linkage disequilibrium (LD), no significant association had been detected from haplotype analysis. Discrepancy had been found between MA users and healthy individuals (P < 0.01) in terms of the expression of miR-181a, miR-15b, miR-let-7e and miR-let-7d. and no noticeable difference had been found from the eQTL analysis.

CONCLUSION

Our findings suggest that rs10760371 within miR-181a may relate to the development of MA dependence with psychosis. The miRNAs expression is unlikely to be regulated by the SNPs within it.

The depression GWAS risk allele predicts smaller cerebellar gray matter volume and reduced SIRT1 mRNA expression in Chinese population

Major depressive disorder (MDD) is recognized as a primary cause of disability worldwide, and effective management of this illness has been a great challenge. While genetic component is supposed to play pivotal roles in MDD pathogenesis, the genetic and phenotypic heterogeneity of the illness has hampered the discovery of its genetic determinants. In this study, in an independent Han Chinese sample (1824 MDD cases and 3031 controls), we conducted replication analyses of two genetic loci highlighted in a previous Chinese MDD genome-wide association study (GWAS), and confirmed the significant association of a single nucleotide polymorphism (SNP) rs12415800 near SIRT1. Subsequently, using hypothesis-free whole-brain analysis in two independent Han Chinese imaging samples, we found that individuals carrying the MDD risk allele of rs12415800 exhibited aberrant gray matter volume in the left posterior cerebellar lobe compared with those carrying the non-risk allele. Besides, in independent Han Chinese postmortem brain and peripheral blood samples, the MDD risk allele of rs12415800 predicted lower SIRT1 mRNA levels, which was consistent with the reduced expression of this gene in MDD patients compared with healthy subjects. These results provide further evidence for the involvement of SIRT1 in MDD, and suggest that this gene might participate in the illness via affecting the development of cerebellum, a brain region that is potentially underestimated in previous MDD studies.