Exploring Potential Causal Genes for Uterine Leiomyomas: A Summary Data-Based Mendelian Randomization and FUMA Analysis
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ABSTRACT:
SUBMITTER: Dai Y
PROVIDER: S-EPMC9315954 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundImmunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. This study aimed to explore genes underlying the pathogenesis of IgAN.MethodsWe conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are potentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed SMR analysis using Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data and replicated the analysis using Genotype-Tissue Expression (GTEx) eQTL data.ResultsUsing the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes whose expression were pleiotropically associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, PSMR= 2.10 × 10-18), ILMN_1682717 (tagging IER3, PSMR= 1.07 × 10-16) and ILMN_1661439 (tagging FLOT1, PSMR=1.16 × 10-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes whose expressions were pleiotropically associated with IgAN, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, PSMR= 1.44 × 10-10), ENSG00000186470.9 (tagging BTN3A2, PSMR= 2.28 × 10-10), and ENSG00000224389.4 (tagging C4B, PSMR= 1.23 × 10 -9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes that are potentially involved in the pathogenesis of IgAN.ConclusionWe identified many genetic variants/loci that are potentially involved in the pathogenesis of IgAN. More studies are needed to elucidate the exact mechanisms of the identified genetic variants/loci in the etiology of IgAN.
| S-EPMC9884184 | biostudies-literature
Project description:Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total causal effect between two traits, while it would be useful to infer the direct causal effect between any two of many traits (by accounting for indirect or mediating effects through other traits). For this purpose we propose a two-step approach: we first apply an extended MR method to infer (i.e. both estimate and test) a causal network of total effects among multiple traits, then we modify a graph deconvolution algorithm to infer the corresponding network of direct effects. Simulation studies showed much better performance of our proposed method than existing ones. We applied the method to 17 large-scale GWAS summary datasets (with median N = 256879 and median #IVs = 48) to infer the causal networks of both total and direct effects among 11 common cardiometabolic risk factors, 4 cardiometabolic diseases (coronary artery disease, stroke, type 2 diabetes, atrial fibrillation), Alzheimer's disease and asthma, identifying some interesting causal pathways. We also provide an R Shiny app (https://zhaotongl.shinyapps.io/cMLgraph/) for users to explore any subset of the 17 traits of interest.
| S-EPMC10231771 | biostudies-literature
Project description:Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of methylation on multiple sclerosis, methylation on expression and expression on multiple sclerosis susceptibility, we prioritize genetic loci with evidence of influencing multiple sclerosis susceptibility. We overlay these findings onto a list of 'druggable' genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and search tool for the retrieval of interacting genes/proteins to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr virus-infected B cells, lymphoblastoid cell lines. We conducted a systematic review of prioritized genes using the Open Targets platform to identify completed and planned trials targeting prioritized genes in multiple sclerosis and related disease areas. Expression of 45 genes in peripheral blood was strongly associated with multiple sclerosis susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leucocyte signalling. We refined this prioritized gene list by restricting to loci where CpG site methylation was associated with multiple sclerosis susceptibility, with gene expression and where expression was associated with multiple sclerosis susceptibility. This approach yielded a list of 15 prioritized druggable target genes for which there was evidence of a pathway linking methylation, expression and multiple sclerosis. Five of these 15 genes are targeted by existing drugs and three were replicated in a smaller expression Quantitative Trait Loci dataset (CD40, MERTK and PARP1). In lymphoblastoid cell lines, this approach prioritized 7 druggable gene targets, of which only one was prioritized by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritized genes in disorders related to multiple sclerosis. We use public datasets and summary-data-based Mendelian randomization to identify a list of prioritized druggable genetic targets in multiple sclerosis. We hope our findings could be translated into a platform for developing targeted preventive therapies.
| S-EPMC7519728 | biostudies-literature
Project description:The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.
| S-EPMC7317850 | biostudies-literature
Project description:ObjectiveTo explore the impact of inflammatory factors on the incidence of cerebral small vessel disease (CSVD), we performed a mendelian randomization (MR) study to analyze the causal relationship between multiple inflammatory factors and CSVD imaging markers and utilized summary-data-based mendelian randomization (SMR) analysis to infer whether the impact of instrumental variables (IVs) on disease is mediated by gene expression or DNA methylation.MethodsUsing public databases such as UKB and IEU, and original genome-wide association studies, we obtained IVs related to exposure (inflammatory factors) and outcome (CSVD imaging markers). We performed the inverse variance weighted, weighted median, and MR-Egger methods to assess causal effects between exposure and outcome in univariate MR analysis. To evaluate their heterogeneity, a series of sensitivity analyses were conducted, including the Cochrane Q test, MR-Egger intercept test, MR-Presso, and leave-one-out analysis. We also applied mediation and multivariate MR analysis to explore the interactions between positive exposures on the same outcome. Additionally, we conducted the SMR, which utilizes instruments within or near relevant genes in blood or brain tissues, to elucidate the causal associations with CSVD markers.ResultsABO Univariate MR of multiple cohorts revealed that the risk of small vessel stroke (SVS) increases with elevated levels of TNF-related apoptosis-inducing ligand (TRAIL, OR, 1.23, 95% CI, 1.08-1.39) and interleukin-1 receptor-like 2, (IL-1RL2, OR, 1.29, 95% CI, 1.04-1.61). IL-18 was a potential risk factor for extensive basal ganglia perivascular space burden (BGPVS, OR, 1.02, 95% CI, 1.00-1.05). Moreover, the risk of extensive white matter perivascular space burden (WMPVS) decreased with rising levels of E-selectin (OR, .98, 95% CI, .97-1.00), IL-1RL2 (OR, .97, 95% CI, .95-1.00), IL-3 receptor subunit alpha (IL-3Ra, OR, .98, 95% CI, .97-1.00), and IL-5 receptor subunit alpha (IL-5Ra, OR, .98, 95% CI, .97-1.00). Mediation and multivariate MR analysis indicated that E-selectin and IL-3Ra might interact during the pathogenesis of WMPVS. SMR estimates showed that TRAIL-related IVs rs5030044 and rs2304456 increased the risk of SVS by increasing the expression of gene Kininogen-1 (KNG1) in the cerebral cortex, particularly in the frontal cortex (βsmr = .10, Psmr = .003, FDR = .04). Instruments (rs507666 and rs2519093) related to E-selectin and IL-3Ra could increase the risk of WMPVS by enhancing DNA methylation of the gene ABO in blood tissue (βsmr = .01-.02, Psmr = .001, FDR = .01-.03).ConclusionAccording to MR and SMR analysis, higher levels of TRAIL increased the risk of SVS by upregulating gene expression of KNG1 in brain cortex tissues. In addition, protective effects of E-selectin and IL-3a levels on WMPVS were regulated by increased DNA methylation of gene ABO in blood tissue.
| S-EPMC10858724 | biostudies-literature
Project description:Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.
| S-EPMC7614127 | biostudies-literature
Project description:Mendelian randomization (MR) is becoming more and more popular for inferring causal relationship between an exposure and a trait. Typically, instrument SNPs are selected from an exposure GWAS based on their summary statistics and the same summary statistics on the selected SNPs are used for subsequent analyses. However, this practice suffers from selection bias and can invalidate MR methods, as showcased via two popular methods: the summary data-based MR (SMR) method and the two-sample MR Steiger method. The SMR method is conservative while the MR Steiger method can be either conservative or liberal. A simple and yet more powerful alternative to SMR is proposed.
| S-EPMC8027662 | biostudies-literature
Project description:ObjectivesPrevious studies have reported that a few inflammatory cytokines have associations with systemic lupus erythematosus (SLE)-for example, IL-6, IL-17, and macrophage inflammatory protein (MIP). This Mendelian randomization was conducted to further assess the causal correlations between 41 inflammatory cytokines and SLE.MethodsThe two-sample Mendelian randomization utilized genetic variances of SLE from a large publicly available genome-wide association study (GWAS) (7,219 cases and 15,991 controls of European ancestry) and inflammatory cytokines from a GWAS summary containing 8,293 healthy participants. Causalities of exposures and outcomes were explored mainly using inverse variance weighted method. In addition, multiple sensitivity analyses including MR-Egger, weighted median, simple mode, weighted mode, and MR-PRESSO were simultaneously applied to strengthen the final results.ResultsThe results indicated that cutaneous T cell-attracting chemokine (CTACK) and IL-17 may be suggestively associated with the risk of SLE (odds ratio, OR: 1.21, 95%CI: 1.04-1.41, p = 0.015; OR: 1.37, 95%CI: 1.03-1.82, p = 0.029). In addition, cytokines including beta nerve growth factor, basic fibroblast growth factor, IL-4, IL-6, interferon gamma-induced protein 10, monokine induced by interferon-gamma, MIP1b, stromal cell-derived factor-1 alpha, and tumor necrosis factor-alpha are suggested to be the consequences of SLE disease (Beta: 0.035, p = 0.014; Beta: 0.021, p = 0.032; Beta: 0.024, p = 0.013; Beta: 0.019, p = 0.042; Beta: 0.040, p = 0.005; Beta: 0.046, p = 0.001; Beta: 0.021, p = 0.029; Beta: 0.019, p = 0.045; Beta: 0.029, p = 0.048).ConclusionThis study suggested that CTACK and IL-17 are probably the factors correlated with SLE etiology, while a couple of inflammatory cytokines are more likely to be involved in SLE development downstream.
| S-EPMC9893779 | biostudies-literature
Project description:Observational studies showed that metabolic phenotypes were associated with the risk of developing breast cancer (BC). However, those results are inconsistent regarding the magnitude of the association, particularly by subtypes of breast cancer. Furthermore, the mechanisms of the association remain unclear. We performed two-sample Mendelian randomization (MR) analyses to evaluate the causal effect of metabolic risk factors on breast cancer in the European population. Assessed individually using MR, body mass index (BMI) (odds ratio [OR] 0.94, 95% Confidence interval [CI] 0.90-0.98, P = 0.007), high-density lipoprotein cholesterol (HDL-C) (OR 1.10, 95% CI 1.07-1.13, P = 6.10 × 10-11) and triglycerides (TG) (OR 0.92, 95% CI 0.90-0.96, P = 1.58 × 10-6) were causally related to breast cancer risk. In multivariable MR, only HDL-C (OR 1.08; 95% CI 1.02-1.14; P = 0.02) retained a robust effect, suggesting that the genetic association between BMI, HDL-C and TG with breast cancer risk in univariable analysis was explained via HDL-C. These findings suggest a possible causal role of HDL-C in breast cancer etiology.
| S-EPMC10471756 | biostudies-literature
Project description:BackgroundMendelian randomization (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions.MethodsHere, a new method - the mode-based estimate (MBE) - is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk.ResultsThe MBE presented less bias and lower type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared with the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia.ConclusionsThe MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses.
| S-EPMC5837715 | biostudies-literature