eQTL Data Research Articles

Druggable targets for Parkinson’s disease: transcriptomics based Mendelian randomization study

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder. Currently available drugs for PD, can only relieve the symptoms of PD, but cannot prevent the progression of the disease and have serious side effects. Other new druggable therapeutic targets for PD are needed. First, six GEO datasets with transcriptomic data from the substantia nigra (SN) region of the brain were downloaded to find dysregulated druggable genes in PD. Then, Mendelian randomization (MR) and summary statistics-based MR (SMR) analysis were conducted using eQTL data from both brain tissue and blood to investigate the relationship between gene expression and PD. Next, the association between the expression of candidate druggable targets and disease stage was validated in an additional dataset GSE49036. Finally, a phenome-wide MR analysis was carried out to investigate the potential impact of candidate druggable genes on several other complex diseases or traits. Our study revealed 313 differentially expressed genes that may be directly targetable and have an impact on PD (FDR-p < 0.1). Through MR and SMR analysis, P2RX7 and RNASET2 were identified as feasible PD therapeutic targets, which were highly expressed in PD tissues and increased as the Braak stages increased. Phenome-wide MR analysis revealed other effects of targeting RNASET2. This study presents genetic support for the potential therapeutic properties of targeting P2RX7 and RNASET2, which will be useful for developing druggable therapeutic targets for PD.

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

Therapeutic targets for lung cancer: genome-wide Mendelian randomization and colocalization analyses

BackgroundLung cancer, categorized into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), remains a significant global health challenge. The development of drug resistance and the heterogeneity of the disease necessitate the identification of novel therapeutic targets to improve patient outcomes.MethodsWe conducted a genome-wide Mendelian randomization (MR) and colocalization analysis using a comprehensive dataset of 4,302 druggable genes and cis-expressed quantitative trait loci (cis-eQTLs) from 31,884 blood samples. The study integrated genomic analysis with eQTL data to identify key genes associated with lung cancer risk.ResultsThe analysis revealed five actionable therapeutic targets for NSCLC, including LTB4R, LTBP4, MPI, PSMA4, and TCN2. Notably, PSMA4 demonstrated a strong association with both NSCLC and SCLC risks, with odds ratios of 3.168 and 3.183, respectively. Colocalization analysis indicated a shared genetic etiology between these gene expressions and lung cancer risk.ConclusionOur findings contribute to precision medicine by identifying druggable targets that may be exploited for subtype-specific lung cancer therapies.

Cross-Tissue Regulatory Network Analyses Reveal Novel Susceptibility Genes and Potential Mechanisms for Endometriosis

Endometriosis (EMT) is a common gynecological disease with a strong genetic component, while its precise etiology remains elusive. This study aims to integrate transcriptome-wide association study (TWAS), Mendelian randomization (MR), and bioinformatics analyses to reveal novel putatively causal genes and potential mechanisms. We obtained summary-level data of the Genotype-Tissue Expression Project (GTEx), v8 expression quantitative loci (eQTL) data, and the genome-wide association study (GWAS) data of EMT and its subtypes from the R11 release results of the FinnGen consortium for analysis. GWAS data of modifiable risk factors were collected from IEU Open GWAS. Cross-tissue TWAS analyses were performed using the unified test for molecular signature (UTMOST), while functional summary-based imputation (FUSION) was employed for single-tissue TWAS analyses. Furthermore, we also conducted multi-marker analysis of genomic annotation (MAGMA) analyses to validate the significant associations. Subsequent Mendelian randomization (MR) and colocalization analysis elucidated the causal associations between the identified genes across various tissues and EMT. To further delve into mechanisms, two-sample network MR analyses were conducted. At last, bioinformatics analyses were employed to enhance our understanding of the functional implications and expression patterns of these identified genes. For EMT, 22 significant gene signals were identified by UTMOST, 615 by FUSION, and 354 by MAGMA. Ultimately, six genes, including CISD2, EFRB, GREB1, IMMT, SULT1E1, and UBE2D3, were identified as candidate susceptibility genes for EMT. Through similar procedures, we identified GREB1, IL1A, and SULT1E1 for EMT of the ovary, and we identified GREB1 for EMT of the pelvic peritoneum, EMT of rectovaginal septum and vagina, and deep EMT. In MR analyses, the expression of IMMT in 21 tissues, EFR3B in the adrenal gland, CISD2 in 17 tissues, and UBE2D3 in 7 tissues demonstrated causal relationships with EMT risk. In addition, CISD2, IMMT, and UBE2D3, across different tissues, exhibited strong colocalization with EMT (PPH4 &gt; 0.7). Two-sample network MR analyses revealed that CISD2, EFR3B, and UBE2D3 could potentially regulate the levels of blood lipids and hip circumference so as to influence the risk of EMT. Furthermore, bioinformatics analyses confirmed our findings and delved into the biological functions of the identified genes. Our study unveiled seven novel candidate genes whose predicted expression was associated with the risk of EMT, providing new insights into the underlying genetic framework of EMT. These findings will facilitate a deeper comprehension of the tissue-specific transcriptional regulatory mechanisms associated with EMT, paving the way for optimizing the management and treatment of EMT.

Causal and mediating effects of lipid and facial aging: association study integrating GWAS, eQTL, mQTL, and pQTL data

BackgroundIncreasing evidence suggests a potential causal association between lipid levels and facial aging. The aim of this study was to investigate the relationship between levels of specific lipids and facial aging via Mendelian randomization methods. Additionally, this study aimed to identify mediators and explore relevant genes and drug targets.MethodsIn this study, genome-wide association data on plasma lipids from 7,174 Finnish individuals in the UK Biobank were used. Two-sample Mendelian randomization was applied to assess the causal effects of specific lipids on facial aging. Sensitivity and pleiotropy analyses were conducted to ensure the robustness and reliability of the results. Multivariate Mendelian randomization was conducted to account for the potential impact of confounding factors. Furthermore, summary-data-based Mendelian randomization was used to identify relevant genes, which were validated through multiomics data. Finally, drug‒gene interactions were explored via molecular docking techniques.ResultsTwo-sample Mendelian randomization analysis revealed a causal relationship between lipid levels and facial aging. According to the multivariate Mendelian randomization results, smoking was found to mediate this association, and these lipids remained significantly associated with facial aging, even after accounting for environmental confounders. Using summary-data-based Mendelian randomization, CYP21A2, CCND1, PSMA4, and MED1 were identified as potential gene targets, with MED1 further validated through pQTL and mQTL data. Additionally, the MED1 protein was found to bind spontaneously with astragalin, fenofibrate, and ginsenoside.ConclusionsThe results revealed a causal relationship between lipid levels and facial aging, revealing key gene targets that were still significantly associated with facial aging after controlling for environmental confounders. Additionally, the interactions between MED1 and certain drugs may indicate potential pathways for therapeutic interventions related to facial aging.

Identification of candidate genes for endometrial cancer in multi-omics: a Mendelian randomization analysis

Endometrial cancer is the most common malignant tumor of the uterus, but the underlying genetic mechanisms of EC remain unclear. To identify candidate genes and investigate genetic mechanisms for endometrial cancer, we utilized the summary-data-based Mendelian randomization (SMR) method to investigate causal associations between genetic variants, gene expression, DNA methylation, and endometrial cancer. Three main analyses were conducted utilizing cis-expression and methylation quantitative trait loci (eQTLs and mQTLs) as instrumental variables to examine causal relationships with endometrial cancer, and assessing the causal relationship between DNA methylation and gene expression. Data sources included genetic association data from O'Mara et al. eQTL data from the GTEx database, and mQTL data from McRae et al. Analysis involved the HEIDI test to distinguish pleiotropy, SMR analysis with multiple testing correction, and colocalization analysis to assess associations driven by linkage disequilibrium. Functional enrichment analysis was performed by the Metascape tool. Our study showed that three genes, SNX11, LINC00243, and EVI2A, were identified as causally related to endometrial cancer. SNX11 exhibited a positive causal relationship, while LINC00243 and EVI2A showed negative ones. Furthermore, 24 CpG sites were identified as causally related to endometrial cancer, with cg14424631 (CYP19A1) being the most significant. The study revealed common genes implicated in endometrial cancer, gene expression, and methylation sites, with LINC00243 playing a key role. Colocalization analysis confirmed significant causal relationships between LINC00243, SNX11, and endometrial cancer. Enrichment analysis uncovered pathways like interferon gamma signaling enriched in both endometrial cancer GWAS and e/mQTL. These findings shed light on the molecular mechanisms underlying endometrial cancer development. The study identified candidate genes and DNA methylation loci causally associated with endometrial cancer, which are expected to serve as potential targets for treatment.

Identifying therapeutic targets for primary ovarian insufficiency through integrated genomic analyses

BackgroundPrimary ovarian insufficiency (POI) is a disorder characterized by the premature decline in ovarian function, leading to significant fertility and health impacts on women under 40. The unclear etiology of POI hinders the development of effective treatments, highlighting the need for novel therapeutic targets.MethodsThis study employed genome-wide association analysis (GWAS) integrated with expression quantitative trait loci (eQTL) data from the GTEx and eQTLGen databases. Mendelian randomization (MR) and colocalization analyses were conducted to investigate causal relationships between genetic variants and POI and to identify potential therapeutic targets.ResultsWe identified 431 genes with available index cis-eQTL signals, of which four (HM13, FANCE, RAB2A, and MLLT10) were significantly associated with POI. Colocalization analysis revealed strong evidence for FANCE and RAB2A, indicating their potential as therapeutic targets. Subsequent druggability assessments identified FANCE and RAB2A as promising candidates for POI treatment, supported by their involvement in DNA repair and autophagy regulation, respectively.ConclusionsOur study establishes a causal link between specific genes and POI, highlighting FANCE and RAB2A as potential drug targets. These findings provide a foundation for future research and therapeutic development, aiming to improve outcomes for women with POI. Validation in further trials is necessary to confirm these potential targets.

Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data

BackgroundAtrioventricular block (AVB) is a heterogeneous group of arrhythmias. AVB can lead to sudden arrest of the heart and subsequent syncope or sudden cardiac death. Few scholars have investigated the underlying molecular mechanisms of AVB. Finding molecular markers can facilitate understanding of AVB and exploration of therapeutic targets.MethodsTwo-sample Mendelian randomization (MR) analysis was undertaken with inverse variance weighted (IVW) model and Wald ratio as the primary approach. Reverse MR analysis was undertaken to identify the associated protein targets and gene targets. Expression quantitative trait loci (eQTL) data from the eQTLGen database and protein quantitative trait loci (pQTL) data from three previous large-scale proteomic studies on plasma were retrieved as exposure data. Genome-wide association study (GWAS) summary data (586 cases and 379,215 controls) for AVB were retrieved from the UK Biobank database. Colocalization analyses were undertaken to identify the effect of filtered markers on outcome data. Databases (DrugBank, Therapeutic Target, PubChem) were used to identify drugs that interacted with targets.ResultsWe discovered that 692 genes and 42 proteins showed a significant correlation with the AVB phenotype. Proteins (cadherin-5, sTie-1, Notch 1) and genes (DNAJC30, ABO) were putative molecules to AVB. Drug–interaction analyses revealed anticancer drugs such as tyrosine-kinase inhibitors and TIMD3 inhibitors could cause AVB. Other substances (e.g. toxins, neurological drugs) could also cause AVB.ConclusionsWe identified the proteins (cadherin-5, sTie-1, Notch 1) and gene (DNAJC30, ABO) targets associated with AVB pathogenesis. Anticancer drugs (tyrosine-kinase inhibitors, TIMD3 inhibitors), toxins, or neurological drugs could also cause AVB.

DMRdb: a disease-centric Mendelian randomization database for systematically assessing causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases.

Exploring the causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases is fundamental to the life sciences. However, large-scale research using Mendelian randomization (MR) analysis is currently lacking. To address this, we introduce DMRdb (http://www.inbirg.com/DMRdb/), a disease-centric Mendelian randomization database, designed to systematically assess causal relationships of diseases with genes, proteins, CpG sites, metabolites and other diseases. The database consists of three main components: (i) 6640 high-quality disease genome-wide association studies (GWASs) from public sources that were subjected to rigorous quality filtering and standardization; (ii) over 497 billion results from MR analyses involving 6640 disease GWAS datasets, 16 238 expression quantitative trait loci (eQTLs) data, 2564 protein quantitative trait loci (pQTLs) data, 12 000 methylation quantitative trait locus (meQTLs) data and 825 metabolites data and (iii) over 380 000 causal relationship pairs from 1223 literature sources relevant to MR analyses. A user-friendly online database was developed to allow users to query, search, and download all the results. In summary, we anticipate that DMRdb will be a valuable resource for advancing our understanding of disease mechanisms and identifying new biomarkers and therapeutic targets.

Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.

Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the predicted expression for trait associations. Accurate gene expression prediction in stage 1 is crucial, as it directly impacts the power to identify associations in stage 2. Currently, the first stage of such studies is primarily conducted using linear models like elastic net regression, which fail to capture the nonlinear relationships inherent in biological systems. Deep learning methods have the potential to model such nonlinear effects, but have yet to demonstrably outperform linear methods at this task. To address this gap, we propose a new deep learning architecture to predict gene expression from genotypic variation across individuals. Our method utilizes a learnable input scaling layer in conjunction with a convolutional encoder to capture nonlinear effects and higher-order interactions without compromising on interpretability. We further augment this approach to allow for parameter sharing across multiple networks, enabling us to utilize prior information for individual variants in the form of functional annotations. Evaluations on real-world genomic data show that our method consistently outperforms elastic net regression across a large set of heritable genes. Furthermore, our model statistically significantly improved predictive performance by leveraging functional annotations, whereas elastic net regression failed to show equivalent gains when using the same information, suggesting that our method can capture nonlinear functional information beyond the capability of linear models.

Integrating transcriptomics, eQTL, and Mendelian randomization to dissect monocyte roles in severe COVID-19 and gout flare.

There are considerable similarities between the pathophysiology of gout flare and the dysregulated inflammatory response in severe COVID-19 infection. Monocytes are the key immune cells involved in the pathogenesis of both diseases. Therefore, it is critical to elucidate the molecular basis of the function of monocytes in gout and COVID-19 in order to develop more effective therapeutic approaches. The single-cell RNA sequencing (scRNA-seq), large-scale genome-wide association studies (GWAS), and expression quantitative trait loci (eQTL) data of gout and severe COVID-19 were comprehensively analyzed. Cellular heterogeneity and intercellular communication were identified using the scRNA-seq datasets, and the monocyte-specific differentially expressed genes (DEGs) between COVID-19, gout and normal subjects were screened. In addition, the correlation of the DEGs with severe COVID-19 and gout flare was analyzed through GWAS statistics and eQTL data. The scRNA-seq analysis exhibited that the proportion of classical monocytes was increased in both severe COVID-19 and gout patient groups compared to healthy controls. Differential expression analysis and MR analysis showed that NLRP3 was positively associated with the risk of severe COVID-19 and involved 11 SNPs, of which rs4925547 was not significantly co-localized. In contrast, IER3 was positively associated with the risk of gout and involved 9 SNPs, of which rs1264372 was significantly co-localized. Monocytes have a complex role in gout flare and severe COVID-19, which underscores the potential mechanisms and clinical significance of the interaction between the two diseases.

Integrative genomic analyses identify neuroblastoma risk genes involved in neuronal differentiation.

Genome-Wide Association Studies (GWAS) have been decisive in elucidating the genetic predisposition of neuroblastoma (NB). The majority of genetic variants identified in GWAS are found in non-coding regions, suggesting that they can be causative of pathogenic dysregulations of gene expression. Nonetheless, pinpointing the potential causal genes within implicated genetic loci remains a major challenge. In this study, we integrated NB GWAS and expression Quantitative Trait Loci (eQTL) data from adrenal gland to identify candidate genes impacting NB susceptibility. We found that ZMYM1, CBL,GSKIP and WDR81 expression was dysregulated by NB predisposing variants. We further investigated the functional role of the identified genes through computational analysis of RNA sequencing (RNA-seq) data from single-cell and whole-tissue samples of NB, neural crest, and adrenal gland tissues, as well as through in vitro differentiation assays in NB cell cultures. Our results indicate that dysregulation of ZMYM1, CBL, GSKIP, WDR81 may lead to malignant transformation by affecting early and late stages of normal program of neuronal differentiation. Our findings enhance the understanding of how specific genes contribute to NB pathogenesis by highlighting their influence on neuronal differentiation and emphasizing the impact of genetic risk variants on the regulation of genes involved in critical biological processes.

Genetic Associations With an Amyotrophic Lateral Sclerosis Reversal Phenotype.

The term "ALS Reversal" describes patients who initially meet diagnostic criteria for amyotrophic lateral sclerosis (ALS) or had clinical features most consistent with progressive muscular atrophy (PMA) but subsequently demonstrated substantial and sustained clinical improvement. The objective of this genome-wide association study (GWAS) was to identify correlates of this unusual clinical phenotype. Participants were recruited from a previously created database of individuals with the ALS Reversal phenotype. Whole-genome sequencing (WGS) data were compared with ethnicity-matched patients with typically progressive ALS enrolled through the CReATe Consortium's Phenotype-Genotype-Biomarker (PGB) study. These results were replicated using an independent ethnically matched WGS data set from Target ALS. Significant results were further explored with available databases of genetic regulatory markers and expression quantitative trait loci (eQTL) analysis. WGS from 22 participants with documented ALS Reversals was compared with the PGB primary cohort (n = 103) and the Target ALS validation cohort (n = 140). Two genetic loci met predefined criteria for statistical significance (two-sided permutation p ≤ 0.01) and remained plausible after fine-mapping. The lead single nucleotide variant (SNV) from the first locus was rs4242007 (primary cohort GWAS OR = 12.0, 95% CI 4.1 to 34.6), which is in an IGFBP7 intron and is in near-perfect linkage disequilibrium with a SNV in the IGFBP7 promoter region. Both SNVs are associated with decreased frontal cortex IGFBP7 expression in eQTL data sets. Notably, 3 Reversals, but none of the typically progressive individuals (n = 243), were homozygous for rs4242007. The importance of the second locus, located near GRIP1, is uncertain given the absence of an associated effect on nearby gene transcription. We found a significant association between the Reversal phenotype and an IGFBP7 noncoding SNV that is associated with IGFBP7 expression. This is biologically relevant as IGFBP7 is a reported inhibitor of the insulin growth factor-1 (IGF-1) receptor that activates the possibly neuroprotective IGF-1 signaling pathway. This finding is limited by small sample size but suggests that there may be merit in further exploration of IGF-1 pathway signaling as a therapeutic mechanism for ALS. This study was registered with ClinicalTrials.gov (NCT03464903) on March 14, 2018. The first participant was enrolled on June 22, 2018.

Multi-omic characterization of air pollution effects: Applications of AirSigOmniTWP Hub

Air pollution, particularly fine particulate matter and gaseous pollutants including NO2 and NOx, presents significant public health challenges. While the harmful effects of these pollutants are well-documented, the molecular mechanisms underlying their impact on health remain incompletely understood. In this study, we utilized genome-wide association study (GWAS) data from the UK Biobank, expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and protein quantitative trait loci (pQTL) data from the Atherosclerosis Risk in Communities (ARIC) study to conduct comprehensive analyses using the Unified Test for Molecular Signatures (UTMOST), Transcriptome-wide Association Studies (TWAS), and Proteome-wide Association Studies (PWAS). To integrate and synthesize these analyses, we developed the AirSigOmniTWP Hub, a specialized platform designed to consolidate and interpret the results from UTMOST, TWAS, and PWAS. TWAS analysis identified a significant association between PM10 exposure and the gene INO80E in females (P = 4.37×10⁻⁵, FDR = 0.0383), suggesting a potential role in chromatin remodeling. PWAS analysis revealed a significant association between NOx exposure and the gene PIP in females (P = 2.28×10⁻⁵, FDR = 0.0299), implicating its involvement in inflammatory pathways. Additionally, UTMOST analyses uncovered significant associations between various pollutants and genes including NCOA4P3 and SPATS2L with PM2.5 exposure, indicating potential mechanisms related to transcriptional regulation and gene-environment interactions.

Deciphering the role of oxidative stress genes in idiopathic pulmonary fibrosis: a multi-omics mendelian randomization approach.

Oxidative stress (OS) is crucial in idiopathic pulmonary fibrosis (IPF) pathogenesis, with its genes potentially acting as both causes and consequences of the disease. We identified OS-related genes from GeneCards and performed a meta-analysis on pulmonary transcriptome datasets to discover differentially expressed genes (DEGs) related to OS in IPF. We integrated this data with the largest available IPF GWAS summaries, expression quantitative trait loci (eQTLs), and DNA methylation QTLs (mQTLs) from blood. This approach aimed to identify blood OS genes and regulatory elements linked to IPF risk, incorporating the latest pulmonary eQTLs and bronchoalveolar lavage fluid microbial QTLs (bmQTLs) for a comprehensive view of gene-lung microbiota interactions through SMR and colocalization analyses. Sensitivity analyses were conducted using two additional mendelian randomization (MR) methods. Meta-analysis revealed 1090 differentially expressed OS genes between IPF patients and controls. Integration with IPF GWAS, eQTL, and mQTL data identified key genes and regulatory elements involved in IPF pathogenesis, highlighting the role of specific genes such as KCNMA1 and SLC22A5 in modulating IPF risk through epigenetic mechanisms. Colocalization analysis further identified potential interactions between gene expression and lung microbiota. Our findings elucidate the complex interplay between OS genes and IPF, suggesting potential therapeutic targets and highlighting the importance of considering epigenetic and microbial interactions in the disease's etiology and progression.

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

Insights into preeclampsia: a bioinformatics approach to deciphering genetic and immune contributions.

Preeclampsia (PE) is a global pregnancy concern, characterized by hypertension with an unclear etiology. This study employs Mendelian randomization (MR) and single-cell RNA sequencing (scRNA-seq) to clarify its genetic and molecular roots, offering insights into diagnosis and treatment avenues. We integrated PE-specific genome-wide association study (GWAS) data, expression and protein quantitative trait loci (eQTL and pQTL) data, and single-cell data from peripheral blood mononuclear cells (PBMCs). We identified highly variable genes using single-cell information and employed MR to determine potential causality. We also combined pQTL and GWAS data, discerned genes positively associated with PE through scRNA-seq, and leveraged the Enrichr platform to unearth drug-gene interactions. Our scRNA-seq pinpointed notable cell type distribution variances, especially in T helper cells (Th cells), between PE and control groups. We unveiled 591 highly variable genes and 6 directly PE-associated genes. Although MR revealed correlations with PE risk, pQTL analysis was inconclusive due to data constraints. Using DSigDB, 93 potential therapeutic agents, like Retinoic acid targeting core genes (IFITM3, NINJ1, COTL1, CD69, and YWHAZ), emerged as prospective multi-target treatments. Utilizing MR and scRNA-seq, this study underscores significant cellular disparities, particularly in Th cells, and identifies crucial genes related to PE. Despite some limitations, these genes have been revealed in PE's underlying mechanism. Potential therapeutic agents, such as Retinoic acid, suggest promising treatment pathways.

Mendelian randomization analysis identified potential genes pleiotropically associated with gout.

This study aims to prioritize genes potentially involved in multifactorial or causal relationships with gout. Using the Summary Data-based Mendelian Randomization (SMR) approach, this research analyzed expression quantitative trait loci (eQTL) data from blood and renal tissues and genome-wide association study (GWAS) data related to gout. It sought to identify genetic loci potentially involved in gout. Heterogeneity testing was conducted with the HEIDI test, and results were adjusted for the False Discovery Rate (FDR). Blood cis-eQTL data were sourced from the eQTLGen Consortium's summary-level data, and renal tissue data came from the V8 release of the GTEx eQTL summary data. Gout GWAS data was sourced from the FinnGen Documentation of the R10 release. SMR analysis identified 14 gene probes in the eQTLGen blood summary-level data significantly associated with gout. The top five ranked genes are: ENSG00000169231 (labeled THBS3, PSMR = 4.16 × 10-13), ENSG00000231064 (labeled THBS3-AS1, PSMR = 1.88 × 10-8), ENSG00000163463 (labeled KRTCAP2, PSMR = 3.88 × 10-6), ENSG00000172977 (labeled KAT5, PSMR = 1.70 × 10-5), and ENSG00000161395 (labeled PGAP3, PSMR = 3.24 × 10-5). Notably, increased expression of KRTCAP2 and PGAP3 is associated with an increased risk of gout, whereas increased expression of THBS3, THBS3-AS1, and KAT5 is associated with a reduced gout risk. No significant gene associations with gout were observed in renal tissue, likely due to the limited sample size of kidney tissue. Our findings have highlighted several genes potentially involved in the pathogenesis of gout. These results offer valuable insights into the mechanisms of gout and identify potential therapeutic targets for its treatment.

New Insights on the Therapeutic Potential of Runt-Related Transcription Factor2 for Osteoarthritis: Evidence from Mendelian Randomization.

Research has highlighted the role of runt-related transcription factor2 (Runx2) in the development of osteoarthritis (OA); however, its causal association remains unclear. This study aimed to explore whether Runx2 expression is causally associated with OA and assess its therapeutic potential for OA. Genetic proxy instruments for Runx2 expression were obtained from gene expression quantitative trait locus (eQTLs) study of eQTLGen Consortium (n = 31,684). Aggregated genome-wide association study (GWAS) data for OA (including all OA [177,517 cases and 649,173 controls], knee OA (KOA) [62,497 cases and 333,557 controls], and hip OA (HOA) [36,445 cases and 316,943 controls]) were extracted from the Genetics of Osteoarthritis Consortium. We integrated eQTLs data with OA GWAS data to estimate their causal association and to estimate the potential of Runx2 as a drug target in the treatment of OA using summary data-based Mendelian randomization (SMR) analysis. Furthermore, different OA GWAS data (including all OA [77,052 cases and 378,169 controls], KOA [24,955 cases and 378,169 controls], and HOA [15,704 cases and 378,169 controls]) derived from the GWAS Catalog database were used for replication study. SMR analysis showed that high expression levels of Runx2 were associated with an increased risk of all OA [odds ratio (OR) 1.044, 95% confidence interval (CI) 1.023-1.067; P = 5.03 × 10-5], KOA (OR1.040, 95%CI 1.006-1.075; P = 0.021), and HOA (OR1.067, 95%CI 1.022-1.113; P = 0.003). This suggests that Runx2 inhibitors may have promising potential for the treatment of OA. Notably, the causal effects of Runx2 with all OA (OR1.053, 95%CI 1.027-1.079; P = 3.95 × 10-5) and KOA (OR1.043, 95%CI 1.001-1.087; P = 0.045) were repeated in the replication study, but limited evidence supported the association of Runx2 expression levels with HOA (OR1.045, 95%CI 0.993-1.101; P = 0.094). Our analyses indicate a positive correlation between Runx2 expression and OA risk across all three phenotypes, suggesting the potential of Runx2 inhibitors in the treatment of OA and providing evidence from a genetic perspective.

Decoding the genetic landscape of juvenile dermatomyositis: insights from phosphorylation-associated single nucleotide polymorphisms.

Genome-wide association studies (GWASs) have identified genetic susceptibility loci associated with juvenile dermatomyositis (JDM). Single nucleotide polymorphisms related to phosphorylation (phosSNPs) are critical nonsynonymous mutations exerting substantial influence on gene expression regulation. The aim of this study was to identify JDM susceptibility genes in the GWAS loci by the use of phosSNPs. We explored quantitative trait loci (QTLs) among the phosSNPs associated with JDM using data from eQTL (bulk tissues and single-cell) and pQTL studies. For gene expression and protein levels significantly influenced by JDM-associated phosSNPs, we assessed their associations with JDM through MR analyses. Additionally, we conducted differential expression gene analyses, incorporating single-cell transcriptomic profiling of 6 JDM cases and 11 juvenile controls (99,396 cells). We identified 31 phosSNPs situated in the 6p21 locus that were associated with JDM. Half of these phosSNPs showed effects on gene expression in various cells and circulating protein levels. In MR analyses, we established associations between the expression levels of pivotal JDM-associated genes, including MICB, C4A, HLA-DRB1, HLA-DRB5, and PSMB9, in skin, muscle, or blood cells and circulating levels of C4A, with JDM. Utilizing single-cell eQTL data, we identified a total of 276 association signals across 14 distinct immune cell types for 28 phosSNPs. Further insights were gained through single-cell differential expression analysis, revealing differential expression of PSMB9, HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1 in immune cells. The present study pinpointed phosSNPs within susceptibility genes for JDM and unraveled the intricate relationships among these SNPs, gene expression levels, and JDM.