Expression Quantitative Trait Loci Research Articles

Causal association of obesity-related anthropometric traits with myopia and the mediating role of educational attainment: a Mendelian randomization study

AIM: To study the causal relationship between obesity-related anthropometric traits and myopia and the mediating role of educational attainment (EA). METHODS: Univariable Mendelian randomization (UVMR) was performed to evaluate the causal association between body mass index (BMI), height, waist-hip ratio (WHR, adjusted for BMI), and mean spherical equivalent (MSE). BMI was divided into fat and fat-free mass and included in multivariable Mendelian randomization (MVMR) to explore the roles of different BMI components in the causal relationship between BMI and MSE. A mediation analysis based on two-step Mendelian randomization (MR) was carried out. Specifically, UVMR was conducted to estimate the causal effect of BMI on EA. The direct effect of EA on MSE was estimated from MVMR. The mediation effect of EA in the BMI-EA-MSE model was calculated by the product of coefficients method. Expression quantitative trait loci (eQTL)-MR, reverse MR, and Linkage Disequilibrium Score Regression (LDSC) were performed to assess the robustness. RESULTS: Genetically predicted higher BMI had a positive total effect on MSE (βIVW=0.26 D, 95%CI=0.14 to 0.37 D, P<0.001), whereas there was no significant association between height, WHR, and MSE. Fat mass was found to play a significant role in the effect of body mass on MSE (βIVW=0.50 D, 95%CI=0.21 to 0.78 D, P=0.001), but there was no significant association between fat-free mass and MSE. The causal effect of BMI on EA was -0.14 (95%CI=-0.16 to -0.11, P<0.001), and the direct effect of EA on MSE was -0.63 D (95%CI=-0.81 to -0.44 D, P<0.001). The mediating effect of EA in the BMI-EA-MSE model was 0.09 D (95%CI=0.06 to 0.12 D), with a mediation proportion of 33% (95%CI=22.1% to 44.6%). No reverse causal associations were detected except for BMI on EA. The results of eQTL-MR and LDSC were consistent with each MR analysis. CONCLUSION: Genetically predicted higher BMI decreases the degree of myopia with a 33% mediation proportion by EA, and fat mass provides a dominant protective role in body mass-myopia. As a supplement to previous observational studies, it provides strong evidence for the relationship between anthropometric traits and refractive errors and offers a theoretical basis for future measures to prevent and control myopia.

DNA methylation biomarkers and myopia: a multi-omics study integrating GWAS, mQTL and eQTL data.

This study aimed to identify DNA methylation biomarkers associated with myopia using summary-data-based Mendelian randomization (SMR). A systematic search of the PubMed, Web of Science, Cochrane Library, and Embase databases was conducted up to March 27, 2024. SMR analyses were performed to integrate genome-wide association study (GWAS) with methylation quantitative trait loci (mQTL) and expression quantitative trait loci (eQTL) studies. The heterogeneity in the dependent instrument (HEIDI) test was utilized to distinguish pleiotropic associations from linkage disequilibrium. The systematic review identified 26 DNA methylation biomarkers in five studies, with no overlap observed among those identified by different studies. After integrating GWAS with multi-omics data of mQTL and eQTL, six genes were significantly associated with myopia: PRMT6 (cg00944433 and cg15468180), SH3YL1 (cg03299269, cg11361895, and cg13354988), ZKSCAN4 (cg01192291), GATS (cg17830204), NPAT (cg04826772), and UBE2I (cg03545757 and cg08025960). We identified six methylation biomarkers associated with the risk of myopia that may be helpful to elucidate the etiology mechanisms of myopia. Further experimental validation studies are required to corroborate these findings.

Common protein-altering variant in GFAP is associated with white matter lesions in the older Japanese population.

The genetic architecture of white matter lesions (WMLs) in Asian populations has not been well-characterized. Here, we performed a genome-wide association study (GWAS) to identify loci associated with the WML volume. Brain MRI and DNA samples were collected from 9479 participants in the Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD). The GWAS confirmed three known WML-associated loci (SH3PXD2A, GFAP, and TRIM47). The lead variant of GFAP was a common missense variant (p.D295N) in East Asians. Meta-GWAS using the publicly available summary statistics of UK Biobank identified one previously unreported locus 6q23.2 (SLC2A12). Integration with expression quantitative trait locus data implied the newly identified locus affects SLC2A12 expression. The effect sizes of 20 lead variants at the WML-associated loci were moderately correlated between JPSC-AD and UK Biobank. These results indicate that the alteration in GFAP protein caused by the common missense variant in East Asians influences the WML volume.

Gastroesophageal reflux disease increases predisposition to severe COVID-19: Insights from integrated Mendelian randomization and genetic analysis.

This study aims to investigate the potential causal relationship, shared genomic loci, as well as potential molecular pathways and tissue-specific expression patterns between gastroesophageal reflux disease (GERD) and the risk of hospitalized/severe 2019 coronavirus disease (COVID-19). We employed linkage disequilibrium score regression and bidirectional Mendelian randomization (MR) analysis to explore potential genetic associations between GERD (N=602,604) and hospitalized COVID-19 (N=2095,324) as well as severe COVID-19 (N=1086,211). Additionally, shared genomic loci were extracted from common pivotal regions, further confirmed through corresponding colocalization analyses. GERD-driven molecular pathway network was constructed using extensive literature data mining to understand the molecular-level impacts of GERD on COVID-19. Our results revealed a significant positive genetic correlation between GERD and both hospitalized (rg = 0.418) and severe COVID-19 (rg = 0.314). Furthermore, the MR analysis demonstrated a unidirectional causal effect of genetic predisposition to GERD on COVID-19 outcomes, including hospitalized COVID-19 (odds ratio [OR]: 1.33, 95% confidence interval [CI]: 1.27-1.44, p=9.17e-12) and severe COVID-19 (OR: 1.27, 95% CI: 1.18-1.37, p=1.20e-05). Additionally, GERD and both COVID-19 conditions shared one genomic locus with lead-SNPs rs1011407 and rs1123573, corresponding to the transcription factor BCL11A. Colocalization analysis further demonstrated a significant positive correlation between genome-wide association study and expression quantitative trait locus (eQTL) abnormalities, including rs1011407 (eQTL_p=2.35e-07) and rs1123573 (eQTL_p=2.74e-05). Molecular pathway analysis indicated that GERD might promote the progression of COVID-19 by inducting immune-activated and inflammation-related pathways. These findings confirm that genetically determined GERD may increase the susceptibility to hospitalized/severe COVID-19. The shared genetic loci and the potential molecular pathways offer valuable insights into causal connections between GERD and COVID-19.

Integrated eQTL mapping approach reveals genomic regions regulating candidate genes of the E8-r3 locus in soybean

Deciphering the gene regulatory networks of critical quantitative trait loci associated with early maturity provides information for breeders to unlock soybean’s (Glycine max (L.) Merr.) northern potential and expand its cultivation range. The E8-r3 locus is a genomic region regulating the number of days to maturity under constant short-day photoperiodic conditions in two early-maturing soybean populations (QS15524F2:F3 and QS15544RIL) belonging to maturity groups MG00 and MG000. In this study, we developed a combinatorial expression quantitative trait loci mapping approach using three algorithms (ICIM, IM, and GCIM) to identify the regions that regulate three candidate genes of the E8-r3 locus (Glyma.04G167900/GmLHCA4a, Glyma.04G166300/GmPRR1a, and Glyma.04G159300/GmMDE04). Using this approach, a total of 2,218 trans (2,061 genes)/7 cis (7 genes) and 4,073 trans (2,842 genes)/3,083 cis (2,418 genes) interactions were mapped in the QS15524F2:F3 and QS15544RIL populations, respectively. From these interactions, we successfully identified two hotspots (F2_GM15:49,385,092-49,442,237 and F2_GM18:1,434,182-1,935,386) and three minor regions (RIL_GM04:17,227,512-20,251,662, RIL_GM04:31,408,946-31,525,671 and RIL_GM13:37,289,785-38,620,690) regulating the candidate genes of E8-r3 and several of their homologs. Based on co-expression network and single nucleotide variant analyses, we identified ALTERED PHLOEM DEVELOPMENT (Glyma.15G263700) and DOMAIN-CONTAINING PROTEIN 21 (Glyma.18G025600) as the best candidates for the F2_GM15:49,385,092-49,442,237 and F2_GM18:1,434,182-1,935,386 hotspots. These findings demonstrate that a few key regions are involved in the regulation of the E8-r3 candidates GmLHCA4a, GmPRR1a, and GmMDE04.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables.

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer's disease.

The integration of quantitative trait loci (QTLs) with disease genome-wide association studies (GWASs) has proven successful in prioritizing candidate genes at disease-associated loci. QTL mapping has been focused on multi-tissue expression QTLs or plasma protein QTLs (pQTLs). We generated a cerebrospinal fluid (CSF) pQTL atlas by measuring 6,361 proteins in 3,506 samples. We identified 3,885 associations for 1,883 proteins, including 2,885 new pQTLs, demonstrating unique genetic regulation in CSF. We identified CSF-enriched pleiotropic regions on chromosome (chr)3q28 near OSTN and chr19q13.32 near APOE that were enriched for neuron specificity and neurological development. We integrated our associations with Alzheimer's disease (AD) through proteome-wide association study (PWAS), colocalization and Mendelian randomization and identified 38 putative causal proteins, 15 of which have drugs available. Finally, we developed a proteomics-based AD prediction model that outperforms genetics-based models. These findings will be instrumental to further understand the biology and identify causal and druggable proteins for brain and neurological traits.

Identifying the impact of ARHGAP and MAP gene families on autism spectrum disorders.

The rising incidence of Autism Spectrum Disorder (ASD) has become a major concern, affecting children's psychological well-being and placing a significant strain on healthcare systems. Despite its impact, the etiological mechanisms underpinning ASD remain elusive. This study leveraged dorsolateral prefrontal cortex gene data from 452 individuals of European descent, sourced from the CommonMindConsortium, and examined ASD-related gene expression data from the Gene Expression Omnibus (GEO) database (GSE18123), along with Genome-Wide Association Studies (GWAS) data from the Lundbeck Foundation Integrated Psychiatric Research and Psychiatric Genomics Consortium. Expression quantitative trait loci data were sourced from the GTExv8 database. We employed Transcriptome-Wide Association Studies (TWAS) and Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint genes within ASD-associated susceptibility gene families (ARHGAP, MAP). Four genes-ARHGAP27, MAPT, ARHGAP19, and MAP1B-were scrutinized, and their biological implications were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-Protein Interaction (PPI) analysis and conditional analysis within the TWAS framework helped identify pivotal genes (ARHGAP27, MAPT). A subsequent verification phase involving Mendelian Randomization (MR) evaluated the potential causal links between the identified genes and ASD. The findings revealed no causal association between ARHGAP19, MAP1B, and ASD. In contrast, significant causal relationships were established for ARHGAP27 and MAPT, suggesting that ARHGAP27 may elevate ASD risk as a susceptibility gene, whereas MAPT appears to reduce the risk as a protective gene.

Inorganic pyrophosphatase 1: a key player in immune and metabolic reprogramming in ankylosing spondylitis.

The relationships among immune cells, metabolites, and AS events were analyzed via Mendelian randomization (MR), and potential immune cells and metabolites were identified as risk factors for AS. Their relationships were subjected to intermediary MR analysis to identify the final immune cells and metabolites. The vertebral bone marrow blood samples from three patients with and without AS were subjected to 10× single-cell sequencing to further elucidate the role of immune cells in AS. The key genes were screened via expression quantitative trait loci (eQTLs) and MR analyses. The metabolic differences between the two groups were compared through single-cell metabolism analysis. Two subgroups of differentiated (CD)8+ memory T cells and naive B cells were obtained from the combined results of intermediary MR analysis and AS single-cell analysis. After the verification of key genes, inorganic pyrophosphatase 1 (PPA1) was identified as the hub gene, as it is differentially expressed in CD8+ memory T cells and can affect the metabolism of T cells in AS by affecting the expression of ferulic acid (FA)4 sulfate, which participates in the cellular immunity in AS.

The causal association between COVID-19 and ischemic stroke: a mendelian randomization study

BackgroundCurrent observational data indicates that ischemic stroke (IS) affects a significant proportion of people with COVID-19. The current study sought to evaluate the causal relationship between COVID-19 and IS.MethodsA two-sample Mendelian randomization (2 S-MR) approach was used to probe the relationship between genetic determinants of three COVID-19 parameters (SARS-CoV‐2 infection, COVID-19 hospitalization, and severe COVID-19) and the incidence of IS based on genome-wide association studies (GWAS) data. Using this 2 S-MR technique, expression quantitative trait loci (eQTL) and GWAS studies were further assessed for overlap to identify common causative genes associated with severe COVID-19 and IS.ResultsIVW approaches indicated the genetic variants linked to COVID-19 hospitalization (OR 1.04, 95% CI 1.01–1.08, p = 0.023) and severe COVID-19 (OR 1.03, 95% CI 1.01–1.05, p = 0.007) were both significantly linked to greater odds of IS. In contrast, there was no causal association between genetic SARS-CoV-2 infection susceptibility and the occurrence of IS (OR 0.99, 95% CI 0.92–1.06, p = 0.694). Ten shared causal genes (TNFSF8, CFL2, TPM1, C15orf39, LHFPL6, FAM20C, SPAG9, KCNJ2, PELI1, and HLA-L) were established as possible mediators of the interplay between severe COVID-19 and the development of IS, with these genes primarily being enriched in immune-related and renin-angiotensin-aldosterone system pathways.ConclusionThese findings indicate a possible causative relationship between IS risk and COVID-19 severity, offering crucial new information for managing COVID-19 patients. Promising options for therapeutic therapies for severe COVID-19 complicated by IS include the common genes found in the present study.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

ELF1 serves as a potential biomarker for the disease activity and renal involvement in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease that affects multiple organs, yet its underlying mechanisms remain unclear, and precise biomarkers are lacking. In this study, we employed Mendelian randomization and HEIDI tools to comprehensively analyze large-scale Genome-Wide Association Study (GWAS) and expression Quantitative Trait Loci (eQTL) data, leading to the identification of seven novel potential functional genes associated with SLE, including BLK, ELF1, STIM1, B3GALT6, APOLD1, INPP5B, and FHL3. Subsequent investigations revealed a significant downregulation of ELF1 gene expression in CD4+ T cells of SLE patients compared to healthy controls. Moreover, within various SLE subgroups, such as those with decreased serum complement C3 levels, positive urinary protein, new-onset skin rashes, and SLE Disease Activity Index (SLEDAI) scores ≥ 5, ELF1 expression displayed a consistent decreasing trend. Notably, ROC curve analysis highlighted the diagnostic potential of ELF1 expression in SLE (AUC = 0.9493), as well as its value in assessing disease activity (AUC = 0.6852) and renal involvement (AUC = 0.7363). In conclusion, this study underscores the potential of ELF1 as a SLE biomarker for diagnosis and evaluation, offering insights into the underlying mechanisms of SLE and paving the way for future therapeutic research.

The study of Alzheimer's disease risk diagnosis based on natural killer cell marker genes in the multi-omics data.

Alzheimer's disease (AD) is a common neurodegenerative disorder, currently lacking effective early diagnostic methods. However, natural killer (NK) cells may play a potential role in AD pathogenesis. This study aims to identify AD-related feature genes from NK cell markers to construct a diagnostic model and explore their potential biological mechanisms in AD. Single-cell RNA sequencing data was used to identify NK cell markers. A novel feature selection algorithm, adaptive dynamic graph convolutional network (ADGCN), was proposed to extract AD-related feature genes and construct a diagnostic model. Differential, correlation and enrichment analyses were performed to understand the biological mechanisms of these genes. Immune infiltration analysis compared the immune microenvironment between AD and controls. Two regulatory networks explored interactions between feature genes, transcription factors and microRNAs. The association between SNPs and feature genes' expression was examined through expression quantitative trait loci analysis. Differential CpG sites were identified to analyze their association with the NK cell markers' expression. We developed an optimal diagnostic model (ADGCN-XGBoost) with 17 feature genes, demonstrating high diagnostic effectiveness across datasets. These genes were primarily related to macromolecule biosynthesis, cytoplasmic translation biological processes and ribosome pathway, and potentially modulated immune infiltration of AD patients. We predicted 27 target miRNAs and 21 transcription factors influencing these genes. Multimodal analysis identified 57 significant SNP-gene associations and seven CpG-gene pairs. This study proposed a novel feature selection algorithm and developed a diagnostic model based on 17 feature genes, providing new potential biomarkers for AD diagnosis.

From Phenotype to Molecules: Unveiling the Genetic and Immunological Bridges Between Autoimmune Diseases and Vitiligo.

Vitiligo is an autoimmune disease characterized by the loss of skin pigmentation. This study aims to explore genetic associations between vitiligo and 21 autoimmune diseases using Mendelian randomization (MR) analysis, with a focus on identifying potential risk and protective factors. We performed univariable and multivariable Mendelian randomization analyses to assess the causal associations between 21 autoimmune diseases and vitiligo. Confounding factors, including smoking, alcohol consumption, and Body Mass Index (BMI), were integrated into the multivariable analysis. Strongly associated single nucleotide polymorphisms (SNPs) were mapped to genes, followed by Summary-data-based Mendelian Randomization (SMR) analysis with expression Quantitative Trait Loci (eQTL) and methylation Quantitative Trait Loci (mQTL) data. Risk and protective factors were further identified by evaluating inflammatory mediators and immune cell phenotypes. The MR analysis identified seven autoimmune diseases with potential causal associations with vitiligo. However, after accounting for confounding factors, only Hashimoto's thyroiditis and type 1 diabetes maintained genetic associations with vitiligo. Gene mapping revealed 25 intersecting genes between these two diseases and vitiligo. SMR analysis confirmed Sulfite Oxidase (SUOX) as a protective gene across multiple tissues. Furthermore, several inflammatory factors were identified as risk factors, including C-X-C motif chemokine ligand 9 (CXCL9), C-X-C motif chemokine ligand 10 (CXCL10), Tumor Necrosis Factor (TNF), and Signaling Lymphocytic Activation Molecule (SLAM). In contrast, Osteoprotegerin (OPG) was identified as a protective factor. This study provides novel insights into the shared molecular mechanisms linking vitiligo with other autoimmune diseases. The identification of SUOX as a common protective gene and the discovery of specific inflammatory and immune-related factors may facilitate future therapeutic strategies.

Landscape of structural variants reveals insights for local adaptations in the Asian corn borer

Capturing the genetic diversity of different wild populations is crucial for unraveling the mechanisms of adaptation and establishing links between genome evolution and local adaptation. The Asian corn borer (ACB) moth has undergone natural selection during its adaptative evolution. However, structural variants (SVs), which play significant roles in these adaptation processes, have not been previously identified. Here, we constructed a multi-assembly graph pan-genome to highlight the importance of SVs in local adaptation. Our analysis revealed that the graph pan-genome contained 176.60 Mb (∼37.33%) of unique sequences. Subsequently, we performed an analysis of expression quantitative trait loci (QTLs) to explore the impact of SVs on gene expression regulation. Notably, through QTL mapping analysis, we identified the FTZ-F1 gene as a potential candidate gene associated with the traits of larval development rate. In sum, we explored the impact of SVs on the local adaptation of pests, therefore facilitating accelerated pest management strategies.

Single Nucleotide Polymorphisms (SNPs) in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer.

Esophageal cancer is a complex disease influenced by genetic and environmental factors. Single nucleotide polymorphisms (SNPs) in non-coding regions of the genome have emerged as crucial contributors to esophageal cancer susceptibility. This review provides a comprehensive overview of the role of SNPs in non-coding regions and their association with esophageal cancer. The accumulation of SNPs in the genome has been implicated in esophageal cancer risk. Various studies have identified specific locations in the genome where SNPs are more likely to occur, suggesting a location-specific response. Chromatin conformational studies have shed light on the localization of SNPs and their impact on gene transcription, posttranscriptional modifications, gene expression regulation, and histone modification. Furthermore, miRNA-related SNPs have been found to play a significant role in esophageal squamous cell carcinoma (ESCC). These SNPs can affect miRNA binding sites, thereby altering target gene regulation and contributing to ESCC development. Additionally, the risk of ESCC has been linked to base excision repair, suggesting that SNPs in this pathway may influence disease susceptibility. Somatic DNA segment alterations and modified expression quantitative trait loci (eQTL) have also been associated with ESCC. These alterations can lead to disrupted gene expression and cellular processes, ultimately contributing to cancer development and progression. Moreover, SNPs have been found to be associated with the long non-coding RNA HOTAIR, which plays a crucial role in ESCC pathogenesis. This review concludes with a discussion of the current and future perspectives in the field of SNPs in non-coding regions and their relevance to esophageal cancer. Understanding the functional implications of these SNPs may lead to the identification of novel therapeutic targets and the development of personalized approaches for esophageal cancer prevention and treatment.

Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More of Genes.

Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More of Genes.

Two-Sample Mendelian Randomization Study Identifies Tissue-Dependent Risk Genes in Autoimmune Diseases

Autoimmune diseases are among the most prevalent diseases across the world with genetic and environmental factors that contribute to their etiology. Because the exact causes of autoimmune diseases are largely unknown, a Mendelian randomization (MR) approach is used here to examine the potential causal association between gene expression levels and disease risk across various tissues. Specifically, this study focuses on six autoimmune diseases including Crohn’s disease, ulcerative colitis, rheumatoid arthritis, multiple sclerosis, type 1 diabetes mellitus, and systemic lupus erythematosus. Several of these diseases are currently treatable with immunosuppressants that target specific genes, such as TNF-alpha, IL-23, CD20, and more. In this study, a two-sample MR analysis is performed with multitissue expression quantitative trait loci (eQTLs) and large-scale genome-wide association studies to investigate how gene expression can influence the risk of developing these diseases. Our results show that genes HLA-DQA1/2, HLA-DRB1/6, HLA-DQB2, C4A, CYP21A2, and HLA-DQB1-AS1 have a high causal effect across several diseases and tissues, and almost all of these findings originate from the major histocompatibility complex (MHC) region on Chromosome 6. Our findings support the current knowledge of genes associated with these diseases while also revealing novel genes that can be used for drug therapies in the future. Although several drug therapies currently exist to treat this selection of autoimmune diseases, we provide further insights into the main, common pathways responsible for autoimmune disease pathogenesis and discuss novel genes that lack research focus.

Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.

Hypnotic use and the risk of cardiovascular diseases in insomnia patients

Abstract Background Insomnia, commonly treated with hypnotic agents, is an independent risk factor of cardiovascular diseases. Previous research demonstrated the detrimental effect of hypnotics on the prognosis of cardiovascular patients. Purpose We aimed to examine the association between hypnotic agents and cardiovascular outcomes in general individuals with insomnia. Methods In a propensity score matched cohort of UK Biobank (UKB) participants with insomnia, Cox proportional hazard model was used to estimate the association between regular use of hypnotic agents and predetermined cardiovascular outcomes including incident coronary heart diseases (CHD), heart failure (HF), stroke, and cardiovascular death. Inverse probability of treatment weighting and competing risk models were further performed during sensitivity analysis. Drug-target Mendelian randomization (MR) analyses were employed for further evaluation of hypnotics-related genes and cardiovascular disease association. The genome-wide association data of CHD, HF, and stroke were there large-scale genome-wide association analyses. Tissue-specified expression quantitative trait loci (eQTL) data was derived from BrainSeq phase II. Results During a median follow-up of 14.3 years, the matched cohort documented a total of 1,019 CHD cases, 406 HF cases, 285 stroke cases, and 197 cardiovascular deaths. No significant association was detected between Z-meds and CHD, stroke, and cardiovascular mortality. Benzodiazepine use was significantly associated with the increased risk of CHD, HF, and cardiovascular mortality (Figure 1). The inverse probability of treatment weighting and competing risk models didn’t alter the above associations. Moreover, drug-target MR analyses corroborated the safety of Z-meds in the general population regarding cardiovascular health. However, GABRG1—a drug target for benzodiazepines—was associated with heightened risks for heart failure, and ischemic stroke (Figure 2). Conclusions Our findings suggested the heterogeneous associations between different categories of hypnotics and incident cardiovascular events in individuals with insomnia. Z-meds are safe regarding the risk of cardiovascular outcomes in the UKB population with insomnia.Figure 1Figure 2