Integrating genome-wide association study and expression quantitative trait loci data identifies multiple genes and gene set associated with neuroticism

Identification of immune-inflammation targets for intracranial aneurysms: a multiomics and epigenome-wide study integrating summary-data-based Mendelian randomization, single-cell-type expression analysis, and DNA methylation regulation

BACKGROUND

Dysfunction of the immune system and inflammation plays a vital role in developing intracranial aneurysms (IAs). However, the progress of genetic pathophysiology is complicated and not entirely elaborated. This study aimed to explore the genetic associations of immune-related and inflammation-related genes (IIRGs) with IAs and their subtypes using Mendelian randomization, colocalization test, and integrated multiomics functional analysis.

METHODS

The authors conducted a summary-data-based Mendelian randomization (SMR) analysis using data from several genome-wide association studies of gene expression (31 684 European individuals) and protein quantitative trait loci (35 559 Icelanders), as well as information on IAs and their subtypes from The International Stroke Genetics Consortium (IGSC) for discovery phase and the FinnGen study for replication. This analysis aimed to determine the causal relationship between IIRGs and the risk of IAs and their subtypes. Further functional analyses, including DNA methylation regulation (1980, European individuals), single-cell-type expression analysis, and protein-protein interaction, were conducted to detect the specific cell type with enriched expression and discover potential drug targets.

RESULTS

After integrating multiomics evidence from expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL), the authors found that tier 1: RELT [odds ratio (OR): 0.14, 95% CI: 0.04-0.50], TNFSF12 (OR: 1.24, 95% CI: 1.24-1.43), tier 3: ICAM5 (OR: 0.89, 95% CI: 0.82-0.96), and ERAP2 (OR: 1.07, 95% CI: 1.02-1.12) were associated with the risk of IAs; tier 3: RELT (OR: 0.11, 95% CI: 0.02-0.54), ERAP2 (OR: 1.08, 95% CI: 1.02-1.13), and TNFSF12 (OR: 1.24, 95% CI: 1.05-1.47) were associated with the risk of aneurysmal subarachnoid hemorrhage (aSAH); and tier 1: RELT (OR: 0.04, 95% CI: 0.01-0.30) was associated with the risk of unruptured intracranial aneurysms (uIAs). Further functional analyses showed that RELT was regulated by cg06382664 and cg18850434 and ICAM5 was regulated by cg04295144 in IAs; RELT was regulated by cg06382664, cg08770935, cg16533363, and cg18850434 in aSAH; and RELT was regulated by cg06382664 and cg21810604 in uIAs. In addition, the authors found that H6PD (OR: 1.13, 95% CI: 1.01-1.28), NT5M (OR: 1.91, 95% CI: 1.21-3.01), and NPTXR (OR: 1.13, 95% CI: 1.01-1.26) were associated with IAs; NT5M (OR: 2.13, 95% CI: 1.23-3.66) was associated aSAH; and AP4M1 (OR: 0.06, 95% CI: 0.01-0.42) and STX7 (OR: 3.97, 95% CI: 1.41-11.18) were related to uIAs. STX7 and TNFSF12 were mainly enriched in microglial cells, whereas H6PD, STX7 , and TNFSF12 were mainly enriched in astrocytes.

CONCLUSIONS

After integrating multiomics evidence, the authors eventually identified IIRGs: RELT, TNFSF12, ICAM5 , and ERAP2 were the novel therapy targets for IAs. These new results confirmed a vital role of immune and inflammation in the etiology of IAs, contributing to enhance our understanding of the immune and inflammatory mechanisms in the pathogenesis of IAs and revealing the complex genetic causality of IAs.

A genome-wide gene-environmental interaction study identified novel loci for the relationship between ambient air pollution exposure and depression, anxiety

BACKGROUND

Genetic factors and environmental exposures, including air pollution, contribute to the risk of depression and anxiety. While the association between air pollution and depression and anxiety has been established in the UK Biobank, there has been limited research exploring this relationship from a genetic perspective.

METHODS

Based on individual genotypic and phenotypic data from a cohort of 104,385 participants in the UK Biobank, a polygenic risk score for depression and anxiety was constructed to explore the joint effects of nitric oxide (NO), nitrogen dioxide (NO2), particulate matter (PM) with a diameter of ⩽2.5 μm (PM2.5) and 2.5-10 μm (PMcoarse) with depression and anxiety by linear and logistic regression models. Subsequently, a genome-wide gene-environmental interaction study (GWEIS) was performed using PLINK 2.0 to identify the genes interacting with air pollution for depression and anxiety.

RESULTS

A substantial risk of depression and anxiety development was detected in participants exposed to the high air pollution concomitantly with high genetic risk. GWEIS identified 166, 23, 18, and 164 significant candidate loci interacting with NO, NO2, PM2.5, and PMcoarse for Patient Health Questionnaire-9 (PHQ-9) score, and detected 44, 10, 10, and 114 candidate loci associated with NO, NO2, PM2.5, and PMcoarse for General Anxiety Disorder (GAD-7) score, respectively. And some significant genes overlapped among four air pollutants, like TSN (rs184699498, PNO2 = 3.47 × 10-9; rs139212326, PPM2.5 = 1.51 × 10-8) and HSP90AB7P(rs150987455, PNO2 = 1.63 × 10-11; rs150987455, PPM2.5 = 7.64 × 10-11), which were common genes affecting PHQ-9 score for both NO2 and PM2.5.

CONCLUSION

Our study identified the joint effects of air pollution with genetic susceptibility on the risk of depression and anxiety, and provided several novel candidate genes for the interaction, contributing to an understanding of the genetic architecture of depression and anxiety.

A Potential Role for MAGI-1 in the Bi-Directional Relationship Between Major Depressive Disorder and Cardiovascular Disease

PURPOSE OF REVIEW

Major Depressive Disorder (MDD) is characterized by persistent symptoms such as fatigue, loss of interest in activities, feelings of sadness and worthlessness. MDD often coexist with cardiovascular disease (CVD), yet the precise link between these conditions remains unclear. This review explores factors underlying the development of MDD and CVD, including genetic, epigenetic, platelet activation, inflammation, hypothalamic-pituitary-adrenal (HPA) axis activation, endothelial cell (EC) dysfunction, and blood-brain barrier (BBB) disruption.

RECENT FINDINGS

Single nucleotide polymorphisms (SNPs) in the membrane-associated guanylate kinase WW and PDZ domain-containing protein 1 (MAGI-1) are associated with neuroticism and psychiatric disorders including MDD. SNPs in MAGI-1 are also linked to chronic inflammatory disorders such as spontaneous glomerulosclerosis, celiac disease, ulcerative colitis, and Crohn's disease. Increased MAGI-1 expression has been observed in colonic epithelial samples from Crohn's disease and ulcerative colitis patients. MAGI-1 also plays a role in regulating EC activation and atherogenesis in mice and is essential for Influenza A virus (IAV) infection, endoplasmic reticulum stress-induced EC apoptosis, and thrombin-induced EC permeability. Despite being understudied in human disease; evidence suggests that MAGI-1 may play a role in linking CVD and MDD. Therefore, further investigation of MAG-1 could be warranted to elucidate its potential involvement in these conditions.

Emotional dysregulation, alexithymia and neuroticism: a systematic review on the genetic basis of a subset of psychological traits

Neuroticism, alexithymia and emotion dysregulation are key traits and known risk factors for several psychiatric conditions. In this systematic review, the aim is to evaluate the genetic contribution to these psychological phenotypes. A systematic review of articles found in PubMed was conducted. Search terms included 'genetic', 'GWAS', 'neuroticism', 'alexithymia' and 'emotion dysregulation'. Risk of bias was assessed utilizing the STREGA checklist. Two hundred two papers were selected from existing literature based on the inclusion and exclusion criteria. Among these, 27 were genome-wide studies and 175 were genetic association studies. Single gene association studies focused on selected groups of genes, mostly involved in neurotransmission, with conflicting results. GWAS studies on neuroticism, on the other hand, found several relevant and replicated intergenic and intronic loci affecting the expression and regulation of crucial and well-known genes (such as DRD2 and CRHR1). Mutations in genes coding for trascriptional factors were also found to be associated with neuroticism (DCC, XKR6, TCF4, RBFOX1), as well as a noncoding regulatory RNA (LINC00461). On the other hand, little GWAS data are available on alexythima and emotional dysregulation.

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BACKGROUND

Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.

METHODS

We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.

RESULTS

We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.

CONCLUSIONS

Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Variability in the antioxidant MSRA gene affects the psychopathology of patients with anorexia nervosa

The objective is to determine whether variability in the MSRA gene, related to obesity and several psychiatric conditions, may be relevant for psychopathological symptoms common in Anorexia Nervosa (AN) and/or for the susceptibility to the disorder. A total of 629 women (233 AN patients and 396 controls) were genotyped for 14 tag-SNPs. Psychometric evaluation was performed with the EDI-2 and SCL-90R questionnaires. Genetic associations were carried out by logistic regression controlling for age and adjusting for multiple comparisons (FDR method). Two tag-SNPs, rs11249969 and rs81442 (with a pairwise r2 value of 0.41), were associated with the global EDI-2 score, which measures EDI-related psychopathology (adjusted FDR-q = 0.02 and 0.04, respectively). Moreover, rs81442 significantly modulated all the scales of the SCL-90R test that evaluates general psychopathology (FDR-q values ranged from 4.1E-04 to 0.011). A sliding-window analysis using adjacent 3-SNP haplotypes revealed a proximal region of the MSRA gene spanning 187.8 Kbp whose variability deeply affected psychopathological symptoms of the AN patients. Depression was the symptom that showed the strongest association with any of the constructed haplotypes (FDR-q = 3.60E-06). No variants were found to be linked to AN risk or anthropometric parameters in patients or controls. Variability in the MSRA gene locus modulates psychopathology often presented by AN patients.

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.

Integrative analysis of genome-wide association study and common meQTLs for exploring the effects of DNA methylation on the development of neuroticism

BACKGROUND

Neuroticism is one of the important traits of personality, which has strong genetic components. However, the underlying genetic mechanism is still unclear.

METHODS

To better understand the genetic basis of neuroticism, we conducted an integrative analysis of genome-wide association studies (GWAS) and life course consistent methylation quantitative trait loci (meQTLs) data. The GWAS data of neuroticism was derived from a published study of neuroticism (including 170,906 subjects). Life course consistent meQTLs were obtained from a large scale longitudinal meQTLs analysis (including 1,018 mother-child pairs).Gene prioritization, pathway and tissue/cell type enrichment analyses were implemented by DEPICT.

RESULTS

We identified multiple genes, pathways and tissues associated with neuroticism, such as NEIL2 (P value = 1.31 × 10^-2), ARHGAP27 (P value = 1.40 × 10^-2), REACTOME_CLATHRIN_DERIVED_VESICLE_BUDDING(P value =4.92 × 10^-6) ,REACTOME_TRANS:GOLGI_NETWORK_VESICLE_BUDDING (P value =4.92 × 10^-6), frontal lobe(P value =3.83 × 10^-3) and visual cortex (P value =8.46 × 10^-3).

CONCLUSIONS

Our results provide novel insights for understanding the genetic mechanism of neuroticism.

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

BACKGROUND

Genome-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.

METHODS

We 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.

RESULTS

Through 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).

CONCLUSION

Collectively, 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.

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.

Methionine sulfoxide reductase (Msr) dysfunction in human brain disease

Extensive research has shown that oxidative stress is strongly associated with aging, senescence and several diseases, including neurodegenerative and psychiatric disorders. Oxidative stress is caused by the overproduction of reactive oxygen species (ROS) that can be counteracted by both enzymatic and nonenzymatic antioxidants. One of these antioxidant mechanisms is the widely studied methionine sulfoxide reductase system (Msr). Methionine is one of the most easily oxidized amino acids and Msr can reverse this oxidation and restore protein function, with MsrA and MsrB reducing different stereoisomers. This article focuses on experimental and genetic research performed on Msr and its link to brain diseases. Studies on several model systems as well as genome-wide association studies are compiled to highlight the role of MSRA in schizophrenia, Alzheimer's disease, and Parkinson's disease. Genetic variation of MSRA may also contribute to the risk of psychosis, personality traits, and metabolic factors.

Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes

It is estimated that the impact of related genes on the risk of Alzheimer's disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Because of linkage disequilibrium (LD), neither the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data-based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs, which belong to 20 genes by eQTL analysis and 379 significant SNPs, which belong to seven known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.

Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method

Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered.

The genetics of human personality

Personality traits are the relatively enduring patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances. Twin and family studies have showed that personality traits are moderately heritable, and can predict various lifetime outcomes, including psychopathology. The Research Domain Criteria characterizes psychiatric diseases as extremes of normal tendencies, including specific personality traits. This implies that heritable variation in personality traits, such as neuroticism, would share a common genetic basis with psychiatric diseases, such as major depressive disorder. Despite considerable efforts over the past several decades, the genetic variants that influence personality are only beginning to be identified. We review these recent and increasingly rapid developments, which focus on the assessment of personality via several commonly used personality questionnaires in healthy human subjects. Study designs covered include twin, linkage, candidate gene association studies, genome-wide association studies and polygenic analyses. Findings from genetic studies of personality have furthered our understanding about the genetic etiology of personality, which, like neuropsychiatric diseases themselves, is highly polygenic. Polygenic analyses have showed genetic correlations between personality and psychopathology, confirming that genetic studies of personality can help to elucidate the etiology of several neuropsychiatric diseases.