Summary Data-based Mendelian Randomization Research Articles

A genetic study to identify pathogenic mechanisms and drug targets for benign prostatic hyperplasia: a multi-omics Mendelian randomization study

Benign prostatic hyperplasia (BPH) as a common geriatric disease in urology, the incidence and prevalence are rapidly increasing with the aging society, prompting an urgent need for effective prevention and treatment of BPH. However, limited therapeutic efficacy and higher risk of complications result in the treatment of BPH remaining challenging. The unclear pathogenic mechanism also hampers further exploration of therapeutic approaches for BPH. In this study, we used multi-omics methods to integrate genomics, transcriptomics, immunomics, and metabolomics data and identify biomolecules associated with BPH. We performed transcriptomic imputation, summary data-based Mendelian randomization (SMR), joint/conditional analysis, colocalization analysis, and FOCUS to explore high-confidence genes associated with BPH in blood and prostate tissue. Subsequently, three-step SMR was used to identify the DNA methylation sites regulating high-confidence genes to improve the pathogenic pathways of BPH. We also used cis-instruments of druggable genes to conduct SMR analysis to find potential drug targets for BPH. Finally, we used MR analysis to explore the immune pathways and metabolomics related to BPH. Multiple analytical methods identified BTN3A2 (Blood: TWAS Z score = 5.02912, TWAS P = 4.93 × 10–7; Prostate: TWAS Z score = 4.89, TWAS P = 1.01 × 10–6) and C4A (Blood: TWAS Z score = 4.90754, TWAS P = 9.22 × 10–7; Prostate: TWAS Z score = 5.084, TWAS P = 3.70 × 10–7) as high-confidence genes for BPH and identified the cg14345882-BTN3A2-BPH pathogenic pathway. We also used druggable gene data to identify 30 promising therapeutic target genes, including BTN3A2 and C4A. For MR analysis of immune pathways, we identified immune cell surface molecules as well as the inflammatory factor IL-17 (OR = 1.25, 95% CI = 1.09–1.43, PFDR = 0.12, Maximum likelihood) as risk factors for BPH. In addition, we found that disulfide levels of cysteinylglycine (OR = 1.11, 95% CI = 1.05–1.18, P = 5.18 × 10–4, Weighted median), oxidation levels of cysteinylglycine (OR = 1.09, 95% CI = 1.04–1.14, P = 3.87 × 10–4, Weighted median), and sebacate levels (OR = 1.05, 95% CI = 1.02–1.08, P = 3.0 × 10–4, Maximum likelihood) increase the risk of BPH. This multi-omics study explored biomolecules associated with BPH, improved the pathogenic pathways of BPH, and identified promising therapeutic targets. Our results provide evidence for future studies aimed at developing appropriate therapeutic interventions.

Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders

BackgroundThe genetic links between heart disease and psychiatric disorders are complex and not well understood. This study uses genome-wide association studies (GWAS) and advanced multilevel analyses to explore these connections. MethodsWe analyzed GWAS data from seven psychiatric disorders and five types of heart disease. Genetic correlations and overlaps were examined using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and Genetic analysis incorporating Pleiotropy and Annotation (GPA). Pleiotropic single-nucleotide variations (SNVs) were identified with pleiotropic analysis under the composite null hypothesis (PLACO) and annotated via Functional mapping and annotation of genetic associations (FUMA). Potential pleiotropic genes were identified using Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary data-based Mendelian Randomization (SMR). Mendelian randomization was employed to assess causal relationships. ResultsAmong 35 trait pairs, 32 showed significant genetic correlations or overlaps. PLACO identified 15,077 SNVs, with 287 recognized as pleiotropic loci and 20 colocalization sites. MAGMA and SMR revealed 75 potential pleiotropic genes involved in diverse pathways, including cancer, neurodevelopment, and cellular organization. Mouse Genome Informatics (MGI) queries provided evidence linking multiple genes to heart or psychiatric disorders. ConclusionsThis analysis reveals loci and genes with pleiotropic effects between heart disease and psychiatric disorders, highlighting shared biological pathways. These findings illuminate the genetic mechanisms underlying the brain-heart axis and suggest shared biological foundations for these conditions, offering potential targets for future prevention and treatment strategies.

Exploring Genetic Drug Targets in Acne Vulgaris: A Comprehensive Proteome-Wide Mendelian Randomization Study.

Acne vulgaris presents a substantial clinical challenge due to its complex pathophysiology and significant impact on quality of life. Identification of novel therapeutic targets for acne using genetic tools can guide the development of more effective treatments. Utilizing a dataset comprising 35 559 Icelandic individuals, we performed proteomic analyses to quantify 4709 circulating proteins. We integrated these data with acne-specific genome-wide association studies (GWAS) encompassing 34 422 acne patients and 364 991 controls. Mendelian randomization (MR) analyses employed the TwoSampleMR tool and Summary-data-based Mendelian Randomization (SMR) to estimate the causal effects of identified proteins on acne risk. Colocalization analyses assessed the likelihood of shared genetic etiology between protein levels and acne using the "coloc" R package. Our proteome-wide MR analysis initially identified 128 proteins potentially associated with acne risk. Following multiple testing corrections using the Benjamini-Hochberg method, fatty acid synthase (FASN) and tissue inhibitor of metalloproteinases 4 (TIMP4) remained significantly associated with acne risk. FASN exhibited a protective effect against acne (OR = 0.768, 95% CI: 0.676-0.872, p = 4.685E-05), while TIMP4 was associated with an increased risk (OR = 1.169, 95% CI: 1.103-1.241, p = 1.956E-07). Colocalization analysis supported a shared genetic basis for these protein-acne associations, with posterior probabilities indicating strong evidence of shared causal variants. Our findings highlight the utility of integrative genomic approaches in identifying potential therapeutic targets for acne. FASN and TIMP4, in particular, demonstrate strong potential as targets for therapeutic intervention, pending further validation through clinical research. These results offer a foundation for targeted acne treatment development, aligning with personalized medicine principles.

Proteomic Mendelian randomization to identify protein biomarkers of telomere length

Shortening of telomere length (TL) is correlated with many age-related disorders and is a hallmark of biological aging. This study used proteome-wide Mendelian randomization to identify the protein biomarkers associated with telomere length. Protein quantitative trait loci (pQTL) were derived from two studies, the deCODE Health study (4907 plasma proteins) and the UK Biobank Pharma Proteomics Project (2923 plasma proteins). Summary data from genome-wide association studies (GWAS) for TL were obtained from the UK Biobank (472,174 cases) and GWAS Catalog (418,401 cases). The association between proteins and TL was further assessed using colocalization and summary data-based Mendelian randomization (SMR) analyses. The protein–protein network, druggability assessment, and phenome-wide MR were used to further evaluate the potential biological effects, druggability, and safety of the target proteins. Proteome-wide MR analysis identified 22 plasma proteins that were causally associated with telomere length. Five of these proteins (APOE, SPRED2, MAX, RALY, and PSMB1) had the highest evidence of association with TL and should be prioritized. This study revealed telomere length-related protein biomarkers, providing new insights into the development of new treatment targets for chronic diseases and anti-aging intervention strategies.

Identification of genetic association between mitochondrial dysfunction and knee osteoarthritis through integrating multi-omics: a summary data-based Mendelian randomization study

ObjectiveAssociation between mitochondrial dysfunction and osteoarthritis (OA) has been consistently investigated, yet their genetic association remains obscure. In this study, mitochondrial-related genes were used as instrumental variables to proxy for mitochondrial dysfunction, and summary data of knee OA (KOA) were used as outcome to examine their genetic association.MethodsWe obtained 1136 mitochondrial-related genes from the human MitoCarta3.0 database. Genetic proxy instruments for mitochondrial-related genes from studies of corresponding gene expression (n = 31,684) and protein (n = 35,559) quantitative trait locus (eQTLs and pQTLs), respectively. Aggregated data for KOA (62,497 KOA cases and 333,557 controls) were extracted from the largest OA genome-wide association study (GWAS). We integrated QTL data with KOA GWAS data to estimate their genetic association using summary data-based Mendelian randomization analysis (SMR). Additionally, we implemented Bayesian colocalization analysis to reveal whether suggestive mitochondrial-related genes and KOA were driven by a same genetic variant. Finally, to validate the primary findings, replication study (24,955 cases and 378,169 controls) and multi-SNP-based SMR (SMR-multi) test was performed.ResultsThrough SMR analysis, we found that the expression levels of 2 mitochondrial-related genes were associated with KOA risk. Specifically, elevated gene expression levels of the IMMP2L (odds ratio [OR] = 1.056; 95% confidence interval [CI] = 1.030–1.082; P-FDR = 0.004) increased the risk of KOA. Conversely, increased gene expression levels of AKAP10 decreased the risk of KOA (OR = 0.955; 95% CI, 0.934–0.977; P-FDR = 0.019). Colocalization analysis demonstrated that AKAP10 (PP.H4 = 0.84) and IMMP2L (PP.H4 = 0.91) shared the same genetic variant with KOA. In addition, consistent results were found in replication study and SMR-multi test, further demonstrating the reliability of our findings.ConclusionsIn summary, our analyses revealed the genetic association between mitochondrial dysfunction proxied by mitochondrial-related genes and KOA, providing new insight into potential pathogenesis of KOA. Furthermore, these identified candidate genes offer the possibility of clinical drug target development for KOA.Key points• This is the first SMR study to explore the genetic association between mitochondrial dysfunction proxied by mitochondrial-related genes and KOA.• Sufficient evidence to support genetic association between the expression levels of AKAP10 and IMMP2L, and KOA• Our MR analysis may provide novel new insight into potential pathogenesis of KOA.• These identified candidate genes offer the possibility of clinical drug target development for KOA

Mendelian randomization reveals potential causal relationships between cellular senescence-related genes and multiple cancer risks

Cellular senescence is widely acknowledged as having strong associations with cancer. However, the intricate relationships between cellular senescence-related (CSR) genes and cancer risk remain poorly explored, with insights on causality remaining elusive. In this study, Mendelian Randomization (MR) analyses were used to draw causal inferences from 866 CSR genes as exposures and summary statistics for 18 common cancers as outcomes. We focused on genetic variants affecting gene expression, DNA methylation, and protein expression quantitative trait loci (cis-eQTL, cis-mQTL, and cis-pQTL, respectively), which were strongly linked to CSR genes alterations. Variants were selected as instrumental variables (IVs) and analyzed for causality with cancer using both summary-data-based MR (SMR) and two-sample MR (TSMR) approaches. Bayesian colocalization was used to unravel potential regulatory mechanisms underpinning risk variants in cancer, and further validate the robustness of MR results. We identified five CSR genes (CNOT6, DNMT3B, MAP2K1, TBPL1, and SREBF1), 18 DNA methylation genes, and LAYN protein expression which were all causally associated with different cancer types. Beyond causality, a comprehensive analysis of gene function, pathways, and druggability values was also conducted. These findings provide a robust foundation for unravelling CSR genes molecular mechanisms and promoting clinical drug development for cancer.

Genetic analysis from multiple cohorts implies causality between 2200 druggable genes, telomere length, and leukemia

BackgroundClinical therapeutic targets for leukemia remain to be identified and the causality between leukemia and telomere length is unclear. MethodsThis work employed cis expression quantitative trait locus (eQTL) for 2,200 druggable genes from the eQTLGen Consortium and genome-wide association studies (GWAS) summary data for telomere length in seven blood cell types from the UK Biobank, Netherlands Cohort as exposures. GWAS data for lymphoid leukemia (LL) and myeloid leukemia (ML) from FinnGen and Lee Lab were used as outcomes for discovery and replication cohorts, respectively. Robust Mendelian randomization (MR) findings were generated from seven MR models and a series of sensitivity analyses. Summary-data-based MR (SMR) analysis and transcriptome-wide association studies (TWAS) were further implemented to verify the association between identified druggable genes and leukemia. Single-cell type expression analysis was employed to identify the specific expression of leukemia casual genes on human bone marrow and peripheral blood immune cells. Multivariable MR analysis, linkage disequilibrium score regression (LDSC), and Bayesian colocalization analysis were performed to further validate the relationship between telomere length and leukemia. Mediation analysis was used to assess the effects of identified druggable genes affecting leukemia via telomere length. Phenome-wide MR (Phe-MR) analysis for assessing the effect of leukemia causal genes and telomere length on 1,403 disease phenotypes. ResultsCombining the results of the meta-analysis for MR estimates from two cohorts, SMR and TWAS analysis, we identified five LL causal genes (TYMP, DSTYK, PPIF, GDF15, FAM20A) and three ML causal genes (LY75, ADA, ABCA2) as promising drug targets for leukemia. Univariable MR analysis showed genetically predicted higher leukocyte telomere length increased the risk of LL (odds ratio [OR] = 2.33, 95 % confidence interval [95 % CI] 1.70–3.18; P = 1.33E-07), and there was no heterogeneity and horizontal pleiotropy. Evidence from the meta-analysis of two cohorts strengthened this finding (OR = 1.88, 95 % CI 1.06–3.05; P = 0.01). Multivariable MR analysis showed the causality between leukocyte telomere length and LL without interference from the other six blood cell telomere length (OR = 2.72, 95 % CI 1.88–3.93; P = 1.23E-07). Evidence from LDSC supported the positive genetic correlation between leukocyte telomere length and LL (rg = 0.309, P = 0.0001). Colocalization analysis revealed that the causality from leukocyte telomere length on LL was driven by the genetic variant rs770526 in the TERT region. The mediation analysis via two-step MR showed that the causal effect from TYMP on LL was partly mediated by leukocyte telomere length, with a mediated proportion of 12 %. ConclusionOur study identified several druggable genes associated with leukemia risk and provided new insights into the etiology and drug development of leukemia. We also found that genetically predicted higher leukocyte telomere length increased LL risk and its potential mechanism of action.

The impact of immunity on the risk of coronary artery disease: insights from a multiomics study.

Immune inflammation is intricately associated with coronary artery disease (CAD) progression, necessitating the pursuit of more efficacious therapeutic strategies. This study aimed to uncover potential therapeutic targets for CAD and myocardial infarction (MI) by elucidating the causal connection between regulatory immune-related genes (RIRGs) and these disorders. We performed summary data-based Mendelian randomization analysis to assess the therapeutic targets linked to expression quantitative trait loci and methylation quantitative trait loci of RIRGs in relation to CAD and MI. Independent validation cohorts and datasets from coronary artery and left ventricular heart tissue were analyzed. To strengthen causal inference, colocalization analysis and PhenoScanner phenotype scans were employed. Utilizing multiomics integration, we pinpointed EIF2B2, FCHO1, and DDT as CAD risk genes. Notably, EIF2B2 and FCHO1 displayed significant associations with MI. High EIF2B2 expression, regulated by cg16144293, heightened CAD and MI risk at rs175438. In contrast, enhanced FCHO1 expression, modulated by cg18329931, reduced CAD and MI risk at rs13382133. DDT upregulation influenced by cg11060661 and cg09664220 was associated with decreased CAD risk at rs5760120. Colocalization analysis firmly established these relationships. EIF2B2, FCHO1, and DDT represent risk loci for CAD progression within RIRGs. Our identification of these genes enhances understanding of CAD pathogenesis and directs future drug development efforts.

Exploring novel drug targets for atopic dermatitis through plasma proteome with genome.

Atopic dermatitis (AD) is a chronic inflammatory disease with a complex and heterogeneous clinical presentation, leading to treatment limitations. Therefore, there is an urgent demand for new therapeutic drug targets. This study utilized Summary-data-based Mendelian randomization (SMR) to identify potential drug targets for AD. Summary statistics for 2,940 human plasma proteins were obtained from the UK Biobank, while AD statistics came from the Early Genetics and Epidemiology of Life Processes consortium and the FinnGen consortium. Furthermore, subsequent colocalization analyses confirmed the causal roles of candidate proteins. Moreover, Phenome-Wide Association Studies (PheWAS), protein-protein interaction (PPI), enrichment analysis, and single cell-type expression analysis provided additional insights. Additionally, drug prediction, druggability prediction, and molecular docking informed the discovery of novel drug targets. SMR analysis showed that eight plasma proteins were causally associated with AD: PVALB and TST were associated with a reduced risk of AD, while CA14, ECM1, IL22, IL6R, IL18R1, and MMP12 were associated with an increased risk of AD. Colocalization analysis confirmed significant associations for TST, IL22, and CA14. PheWAS further revealed that candidate drug targets were mainly linked to other allergic diseases. The corresponding protein-coding genes are predominantly expressed in melanocytes, T cells, and macrophages in skin tissue. Importantly, these proteins were identified to be involved in cytokine-cytokine receptor interaction, Th17 cell differentiation, and the JAK-STAT signaling pathway. All of these proteins are druggable, and six of them show great potential as drug targets. In conclusion, this study identified eight plasma proteins causally associated with AD and provided new insights into the etiology and potential drug targets for AD.

Assessing the causal relationship between plasma proteins and osteoporosis: novel insights into pathological mechanisms and therapeutic implications.

This study aims to identify potential therapeutic targets for OP using summary data-based Mendelian randomization (SMR) and colocalization analysis methods. Furthermore, we seek to explore the biological significance and pharmacological value of these drug targets. To identify potential therapeutic targets for OP, we conducted SMR and colocalization analysis. Plasma protein (pQTL, exposure) data were sourced from the study by Ferkingstad et al. (n = 35,559). Summary statistics for bone mineral density (BMD, outcome) were obtained from the GWAS Catalog (n = 56,284). Additionally, we utilized enrichment analysis, protein-protein interaction (PPI) network analysis, drug prediction, and molecular docking to further analyze the biological significance and pharmacological value of these drug targets. In the SMR analysis, while 20 proteins showed significance, only 8 potential drug targets (GCKR, ERBB3, CFHR1, GPN1, SDF2, VTN, BET1L, and SERPING1) received support from colocalization (PP.H4 > 0.8). These proteins are closely associated with immune function in terms of biological significance. Molecular docking also demonstrated favorable binding of drugs to proteins, consistent with existing structural data, further substantiating the pharmacological value of these targets. The study identified 8 potential drug targets for OP. These prospective targets are believed to have a higher chance of success in clinical trials, thus aiding in prioritizing OP drug development and reducing development costs.

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.

Genetic association of lipid and lipid-lowering drug target genes with atopic dermatitis: a drug target Mendelian randomization study

Observational studies suggest dyslipidemia as an atopic dermatitis (AD) risk factor and posit that lipid-lowering drugs may influence AD risk, but the causal link remains elusive. Mendelian randomization was applied to elucidate the causal role of serum lipids in AD and assess the therapeutic potential of lipid-lowering drug targets. Genetic variants related to serum lipid traits and lipid-lowering drug targets were sourced from the Global Lipid Genetics Consortium GWAS data. Comprehensive AD data were collated from the UK Biobank, FinnGen, and Biobank Japan. Colocalization, Summary-data-based Mendelian Randomization (SMR), and mediation analyses were utilized to validate the results and pinpoint potential mediators. Among assessed targets, only Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) was significantly linked to a reduced AD risk, corroborated across three separate AD cohorts. No association between serum lipid concentrations or other lipid-lowering drug targets and diminished AD risk was observed. Mediation analysis revealed that beta nerve growth factor (b-NGF) might mediate approximately 12.8% of PCSK9's influence on AD susceptibility. Our findings refute dyslipidemia's role in AD pathogenesis. Among explored lipid-lowering drug targets, PCSK9 stands out as a promising therapeutic agent for AD.

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.

Exploring the causal effect between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study

BackgroundIdiopathic pulmonary fibrosis (IPF) is a respiratory disorder of obscure etiology and limited treatment options, possibly linked to dysregulation in lipid metabolism. While several observational studies suggest that lipid-lowering agents may decrease the risk of IPF, the evidence is inconsistent. The present Mendelian randomization (MR) study aims to determine the association between circulating lipid traits and IPF and to assess the potential influence of lipid-modifying medications for IPF.MethodsSummary statistics of 5 lipid traits (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, apolipoprotein A, and apolipoprotein B) and IPF were sourced from the UK Biobank and FinnGen Project Round 10. The study’s focus on lipid-regulatory genes encompassed PCSK9, NPC1L1, ABCG5, ABCG8, HMGCR, APOB, LDLR, CETP, ANGPTL3, APOC3, LPL, and PPARA. The primary effect estimates were determined using the inverse-variance-weighted method, with additional analyses employing the contamination mixture method, robust adjusted profile score, the weighted median, weighted mode methods, and MR-Egger. Summary-data-based Mendelian randomization (SMR) was used to confirm significant lipid-modifying drug targets, leveraging data on expressed quantitative trait loci in relevant tissues. Sensitivity analyses included assessments of heterogeneity, horizontal pleiotropy, and leave-one-out methods.ResultsThere was no significant effect of blood lipid traits on IPF risk (all P＞0.05). Drug-target MR analysis indicated that genetic mimicry for inhibitor of NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 were associated with increased IPF risks, with odds ratios (ORs) and 95% confidence intervals (CIs) as follows: 2.74 (1.05–7.12, P = 0.039), 1.36 (1.02–1.82, P = 0.037), 1.66 (1.12–2.45, P = 0.011), 1.68 (1.14–2.48, P = 0.009), and 1.42 (1.20–1.67, P = 3.17×10-5), respectively. The SMR method identified a significant association between PCSK9 gene expression in whole blood and reduced IPF risk (OR = 0.71, 95% CI: 0.50–0.99, P = 0.043). Sensitivity analyses showed no evidence of bias.ConclusionsSerum lipid traits did not significantly affect the risk of idiopathic pulmonary fibrosis. Drug targets MR studies examining 12 lipid-modifying drugs indicated that PCSK9 inhibitors could dramatically increase IPF risk, a mechanism that may differ from their lipid-lowering actions and thus warrants further investigation.

Causal Effects of Oxidative Stress on Diabetes Mellitus and Microvascular Complications: Insights Integrating Genome-Wide Mendelian Randomization, DNA Methylation, and Proteome.

Oxidative stress (OS) is involved in the development of diabetes, but the genetic mechanisms are not completely understood. We integrated multi-omics data in order to explore the genetic relations between OS-related genes, diabetes mellitus, and microvascular complications using Mendelian randomization and colocalization analysis. Summary-level data related to OS were acquired from respective studies of methylation, expression, and protein abundance quantitative trait loci. Genetic associations concerning diabetes, diabetic nephropathy (DN), and diabetic retinopathy (DR) were derived from the FinnGen study. Summary-data-based Mendelian randomization (SMR) analysis was conducted to evaluate the correlations between molecular features concerned with OS-related genes and diabetes mellitus, along with its microvascular complications. Additionally, we performed colocalization analysis to determine if the detected signal pairs shared a causal genetic variant. At the genetic level, we identified ten potential causal associations of oxidative stress genes with diabetes, along with microvascular complications, through SMR and colocalization analysis. After integrating the DNA methylation quantitative trait loci (mQTL) and expression QTL (eQTL) data, our analyses revealed a correlation between the methylation site cg26343298 and reduced expression of TP53INP1, supporting the protective role of cg26343298 methylation on type 2 diabetes (T2D) and diabetic nephropathy. Similarly, an inverse association was observed between gene methylation and expression in CHEK1 (cg07110182), confirming the beneficial effect of modification of CHEK1 by cg07110182 in diabetic retinopathy. In addition, upregulation of SUOX expression by cg22580629 was linked to a reduced risk of diabetic retinopathy. At circulating protein levels, genetically predicted a higher level of ICAM1 (OR 1.05, 95%CI 1.03-1.08) was positively connected with the risk of diabetic retinopathy. This SMR study elucidated that the TP53INP1 gene was putatively associated with T2D and DN risk, while the SUOX and CHEK1 genes were associated with DR risk through oxidative stress mechanisms. Additionally, our study showed a positive correlation between the ICAM-1 protein and DR. These findings may enhance our understanding of their pathogenesis and suggest new therapeutic targets for clinical practice.

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.

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- and inflammation-related genes (IIRGs) with IAs and their subtypes using Mendelian randomization, colocalization test, and integrated multiomics functional analysis. We 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. After integrating multi-omics evidence from expression quantitative trait loci (eQTL)and protein quantitative trait loci(pQTL), we found that tier 1: RELT [odds ratio (OR): 0.14, 95% confidence interval (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, we 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. After integrating multi-omics evidence, we 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.

Multi-omics Mendelian randomization integrating GWAS, eQTL and pQTL data revealed GSTM4 as a potential drug target for migraine

IntroductionMigraine, as a complex neurological disease, brings heavy burden to patients and society. Despite the availability of established therapies, existing medications have limited efficacy. Thus, we aimed to find the drug targets that improve the prognosis of migraine.MethodWe used Mendelian Randomization (MR) and Summary-data-based MR (SMR) analyses to study possible drug targets of migraine by summary statistics from FinnGen cohorts (nCase = 44,616, nControl = 367,565), with further replication in UK Biobank (nCase = 26,052, nControl = 487,214). Genetic instruments were obtained from eQTLGen and UKB-PPP to verify the drug targets at the gene expression and protein levels. The additional analyses including Bayesian co-localization, the heterogeneity in dependent instruments(HEIDI), Linkage Disequilibrium Score(LDSC), bidirectional MR, multivariate MR(MVMR), heterogeneity test, horizontal pleiotropy test, and Steiger filtering were implemented to consolidate the findings further. Lastly, drug prediction analysis and phenome-wide association study(PheWAS) were employed to imply the possibility of drug targets for future clinical applications.ResultThe MR analysis of eQTL data showed that four drug targets (PROCR, GSTM4, SLC4A1, and TNFRSF10A) were significantly associated with migraine risk in both the FinnGen and UK Biobank cohorts. However, only GSTM4 exhibited consistent effect directions across the two outcomes(Discovery cohort: OR(95%CI) = 0.94(0.93–0.96); p = 2.70e − 10; Replication cohort: OR(95%CI) = 0.93(0.91–0.94); p = 4.21e − 17). Furthermore, GSTM4 passed the SMR at p < 0.05 and HEIDI test at p > 0.05 at both the gene expression and protein levels. The protein-level MR analysis revealed a strong correlation between genetically predicted GSTM4 with a lower incidence of migraine and its subtypes(Overall migraine: OR(95%CI) = 0.91(0.87–0.95); p = 6.98e-05; Migraine with aura(MA): OR(95%CI) = 0.90(0.85–0.96); p = 2.54e-03; Migraine without aura(MO): OR(95%CI) = 0.90(0.83–0.96); p = 2.87e-03), indicating a strong co-localization relationship (PPH4 = 0.86). Further analyses provided additional validation for the possibility of GSTM4 as a migraine treatment target.ConclusionThis study identifies GSTM4 as a potential druggable gene and promising therapeutic target for migraine.

Association between genetically proxied PPARG activation and psoriasis vulgaris: a Mendelian randomization study

Background Psoriasis is a common autoimmune disease in clinical practice, and previous observational studies have suggested that PPARG agonists such as Pioglitazone may be potential therapeutic agents. However, due to interference from various confounding factors, different observational studies have not reached a unified conclusion. We aim to evaluate the potential use of PPARG agonists for treating psoriasis from a new perspective through drug-targeted Mendelian randomization (MR) analysis. Materials and methods This study includes data on 8,876 individuals for acute myocardial infarction from GWAS, and LDL cholesterol data from 343,621 Europeans. FinnGen contributed psoriasis vulgaris data for 403,972 individuals. The DrugBank10 databases function to identify genes encoding protein products targeted by active constituents of lipid-modifying targets. A two-sample MR analysis and summary-data-based MR (SMR) analysis estimated the associations between expressions of drug target genes and symptoms of psoriasis vulgaris. A multivariable MR study was further conducted to examine if the observed association was direct association. Results SMR analysis revealed that enhanced PPARG gene expression in the blood (equivalent to a one standard deviation increase) was a protective factor for psoriasis vulgaris (beta = −0.2017, se = 0.0723, p = 0.0053). Besides, there exists an MR association between LDL mediated by PPARG and psoriasis vulgaris outcomes (beta = −3.9169, se = 0.5676, p = 5.17E-12). These results indicate that PPARG is a therapeutic target for psoriasis, suggesting that psoriasis may be a potential indication for PPARG agonists. Conclusion This study confirms that therapeutic activation of PPARG helps suppress the development of psoriasis. Psoriasis may be a new indication for PPARG agonists, such as Pioglitazone. In the future, new anti-psoriatic drugs could be developed targeting PPARG.

Integrating plasma proteome with genome reveals novel protein biomarkers in colorectal cancer.

Biomarkers for colorectal cancer (CRC) can complement population screening methods, but so far, few plasma proteins have been identified as biomarkers for CRC. This study aims to identify potential protein biomarkers and therapeutic targets for CRC within the proteome range. We extracted summary-level data of circulating protein from 7 published genome-wide association studies (GWASs) of plasma proteome for Mendelian randomization (MR), summary-data-based MR (SMR), and co-localization analyses to screen and validate proteins with causal effects in CRC. In addition, we further conducted druggability evaluation, prognosis analysis at the transcriptional level, and enrichment expression at the single-cell level, highlighting the important role of these plasma protein biomarkers in CRC. We identified 117 plasma protein biomarkers associated with CRC risk, with 9 proteins showing stronger genetic correlations in Bayesian co-localization (PP.H4 > 0.70). Further, we found 26 protein-coding genes already used in targeted drug development and may potentially become therapeutic targets for CRC. In prognosis analysis, the encoding genes of plasma proteins exhibited consistent effects with MR analysis and can serve as prognostic biomarkers for CRC. Additionally, we also found that the differentially expressed proteins are mainly expressed in fibroblasts, endothelial cells, macrophages, and T cells. Our study has identified plasma protein biomarkers associated with CRC risk, which may complement population screening methods for CRC and achieve more precise treatment for patients.

Druggable genome-wide Mendelian randomization for identifying the role of integrated stress response in therapeutic targets of bipolar disorder

For bipolar disorder (BD), the inconsistency of treatment guidelines and the long phases of pharmacological adjustment remain major challenges. BD is known to be comorbid with many medical and psychiatric conditions and they may share inflammatory and stress-related aetiologies, which could give rise to this association. The integrated stress response (ISR) responds to various stress conditions that lead to alterations in cellular homeostasis. However, as a causative mechanism underlying cognitive deficits and neurodegeneration in a broad range of brain disorders, the impact of ISR on BD is understudied. Mendelian randomization has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for BD and analyze their pathophysiological mechanisms, using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on BD and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 41,917 BD cases and 371,549 controls from the Psychiatric Genomics Consortium and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. The SMR analysis identified the EIF2B5 gene that was associated with BD due to no linkage but pleiotropy or causality. The COLOC analysis strongly suggested that EIF2B5 and the trait of BD were affected by shared causal variants, and thus were colocalized. Utilizing data in EpiGraphDB we find other putative causal BD genes (EIF2AK4 and GSK3B) to prioritize potential alternative drug targets.