This deposit provides full details of the genome wide association study (GWAS) pipeline developed by the MRC-IEU for the full UK Biobank (version 3, March 2018) genetic data. For any issues with use of this documentation please contact: mrc-ieu@bristol.ac.uk. This dataset supersedes the earlier version at https://doi.org/10.5523/bris.2fahpksont1zi26xosyamqo8rr

Summary information on the data from the genome-wide association studies used in the MR analysis.

ObjectiveLow back pain is a prevalent and debilitating condition worldwide, with significant implications for individuals’ quality of life and productivity. The aim of this study was to assess the relationship between mood instability and the risk of developing chronic low back pain, using a rigorously designed mendelian randomization methodology.MethodThe study incorporated both univariate and multivariate mendelian randomization to analysis the causal relationship between mood instability and the risk of developing chronic low back pain. The data on mood instability from the Integrative Epidemiology Unit (IEU) opened Genome-Wide Association Studies (GWAS) project (IEU-opened GWAS project). Data on low back pain were collected from two sources: One source is the IEU open GWAS project (discovery data). Another source is a GWAS meta-analysis (replication data). Inverse variance weighted method, weighted median method, MR-Egger regression, and mendelian randomization pleiotropy residual sum and outlier method were used for mendelian randomization analysis.ResultThe univariable mendelian randomization analysis shows a statistically significant correlation between mood instability and the risk of low back pain. Several methods were performed, including inverse variance weighting (discovery data: odds ratio = 3.544, 95% confidence interval = 1.785–7.039, p = 0.000; replication data: odds ratio = 3.167, 95% confidence interval = 2.476–4.052, p = 0.000), MR-Egger (discovery data: odds ratio = 7.178, 95% confidence interval = 0.057–909.525, p = 0.429; replication data: odds ratio = 2.262, 95% confidence interval = 0.580–8.825, p = 0.246), weighted median (discovery data: odds ratio = 2.730, 95% confidence interval = 1.112–6.702, p = 0.028; replication data: odds ratio = 3.243, 95% confidence interval = 2.378–4.422, p = 0.000), MR-PRESSO (discovery data: odds ratio = 3.544, 95% confidence interval = 1.785–7.039, p = 0.001; replication data: odds ratio = 3.167, 95% confidence interval = 2.476–4.052, p = 0.000) methods. The results were consistent across these methods. The results obtained from discovery data are consistent with those obtained from discovery data. In the multivariable mendelian randomization, after adjusting for various covariates such as body mass index, current tobacco smoking, alcohol intake frequency, Total body bone mineral density, and vigorous physical activity, there is a consistent correlation between mood instability and chronic low back pain.ConclusionThis study provides robust evidence supporting a causal relationship between mood instability and the development of low back pain. Our findings suggest that addressing mood instability may play a crucial role in prevention and management strategies for individuals experiencing low back pain.

Results of intermediate Mendelian randomisation analysis.

Results of multivariate Mendelian randomisation analysis.

Association between BMI/type 2 diabetes and bone mineral density risk under different methods.

BackgroundNumerous observational studies have investigated on the correlation of whole, semi-skimmed, and skimmed milk with coronary artery disease (CAD) and myocardial infarction (MI) risk; However, no consensus has been reached and evidence on any causal links between these exposures and outcomes remains unclear. This study aimed to conduct univariate and multivariate Mendelian randomization (MR) analyses, using publicly released genome-wide association study summary statistics (GWAS) from the IEU GWAS database, to ascertain the causal association of milk with various fat content with CAD and MI risk.MethodsFor the exposure data, 29, 15, and 30 single-nucleotide polymorphisms for whole milk, semi-skimmed milk, and skimmed milk, respectively, obtained from 360,806 Europeans, were used as instrumental variables. CAD and MI comprised 141,217 and 395,795 samples, respectively. We used inverse variance weighted (IVW), weighted median, MR-Egger regression, and MR Pleiotropy Residual Sum and Outlier analyses to determine whether pleiotropy and heterogeneity could skew the MR results. Sensitivity tests were conducted to verify the robustness of the results.ResultsAfter adjusting for false discovery rates (FDR), we discovered proof that skimmed milk intake is a genetically predicted risk factor for CAD (odds ratio [OR] = 5.302; 95% confidence interval [CI] 2.261–12.432; P < 0.001; FDR-corrected P < 0.001) and MI (OR = 2.287; 95% CI 1.218–4.300; P = 0.010; FDR-corrected P = 0.009). Most sensitivity assessments yielded valid results. Multivariable MR for CAD and MI produced results consistent with those obtained using the IVW method. There was no causal relationship between whole or semi-skimmed milk, and CAD or MI.ConclusionOur findings indicate that the consumption of skimmed milk may increase the risk of CAD and MI. This evidence may help inform dietary recommendations for preventing cardiovascular disease. Further studies are required to elucidate the underlying mechanisms.

In the information age, global scientific research results have been shared like never before, and the huge amount of data generated by researchers around the world is centrally stored in several public databases, such as UK Biobank, FinnGen, GWAS Catalog, IEU OpenGWAS, etc. However, the current utilization efficiency of these databases is obviously insufficient.In this paper, the Genome-wide Precision Causal Network Translational Medical Paradigm (GPCN-TMP) is proposed for the first time. The paradigm makes full use of global publicly available genomic and multi-omics databases, with the core approach involving Mendelian Randomization (MR) and Generalized Summary-data-based Mendelian Randomization (GSMR) to discover a large number of potential causal relationships through automated large-scale data-driven analysis and rigorous statistical validation.In this paper, we have conducted a comprehensive validation and demonstration by applying the GPCN-TMP theory using the cystatin family as an example. Without any predetermined research directions, we applied self-developed large-scale automated MR and GSMR batch analysis codes to extract relevant data from global public databases, perform preliminary causal network analysis and obtain a large number of potential relationship results. Subsequent cross-validation with existing literature fully demonstrated the scientific validity and feasibility of the GPCN-TMP paradigm.

In the information age, global scientific research results have been shared like never before, and the huge amount of data generated by researchers around the world is centrally stored in several public databases, such as UK Biobank, FinnGen, GWAS Catalog, IEU OpenGWAS, etc. However, the current utilization efficiency of these databases is obviously insufficient.In this paper, the Genome-wide Precision Causal Network Translational Medical Paradigm (GPCN-TMP) is proposed for the first time. The paradigm makes full use of global publicly available genomic and multi-omics databases, with the core approach involving Mendelian Randomization (MR) and Generalized Summary-data-based Mendelian Randomization (GSMR) to discover a large number of potential causal relationships through automated large-scale data-driven analysis and rigorous statistical validation.In this paper, we have conducted a comprehensive validation and demonstration by applying the GPCN-TMP theory using the cystatin family as an example. Without any predetermined research directions, we applied self-developed large-scale automated MR and GSMR batch analysis codes to extract relevant data from global public databases, perform preliminary causal network analysis and obtain a large number of potential relationship results. Subsequent cross-validation with existing literature fully demonstrated the scientific validity and feasibility of the GPCN-TMP paradigm.

Objective: Although previous epidemiological studies have reported substantial links between inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), and celiac disease (CeD), the causal relationship between the two remains unknown. The purpose of the current study was to evaluate the bidirectional causation between IBD and CeD using Mendelian randomization (MR).Method: We obtained genome-wide association study (GWAS) summary data of IBD (CD and UC) and CeD of thoroughly European ancestry from the IEU GWAS database. We screened eligible instrumental variables (IVs) according to the three assumptions of MR. MR was performed using MR-Egger, weighted median (WM), and inverse variance weighted (IVW) methods. The MR-Egger intercept and MR-PRESSO method investigated the horizontal pleiotropy effect. A leave-one-out analysis was performed to prevent bias caused by a single SNP.Results: The study assessed a bidirectional causal effect between CD and CeD; CD increased the risk of CeD (IVW odds ratio (OR) = 1.27, 95% confidence interval (CI) = 1.19–1.35, p = 3.75E-13) and vice-a-versa (IVW OR = 1.09, 95% CI = 1.05–1.13, p = 1.39E-05). Additionally, CeD was influenced by IBD (IVW OR = 1.24, 95% CI = 1.16–1.34, p = 9.42E-10) and UC (IVW OR = 0.90, 95% CI = 0.83–0.98, p = 0.017). However, we observed no evidence of a causal relationship between CeD and IBD (IVW OR = 1.00, 95% CI = 0.97–1.04, p = 0.900) or UC (IVW OR = 0.96, 95% CI = 0.92–1.02, p = 0.172).Conclusion: The present study revealed that IBD and CeD have a bidirectional causal relationship. However, it is slightly different from the results of previous observational studies, recommending that future studies focus on the mechanisms of interaction between CD and CeD.

Female-only, European ancestry GWASs retrieved from the IEU Open GWAS project to be included as exposures (anthropometric markers) and the outcome (PCOS) in two-sample Mendelian randomization analyses.

Observational studies revealed altered gut microbial composition in patients with allergic diseases, which illustrated a strong association between the gut microbiome and the risk of allergies. However, whether such associations reflect causality remains to be well-documented. Two-sample mendelian randomization (2SMR) was performed to estimate the potential causal effect between the gut microbiota and the risk of allergic diseases. 3, 12, and 16 SNPs at the species, genus, and family levels respectively of 15 microbiome features were obtained as the genetic instruments of the exposure dataset from a previous study. GWAS summary data of a total of 17 independent studies related to allergic diseases were collected from the IEU GWAS database for the outcome dataset. Significant causal relationships were obtained between gut microbiome features including Ruminococcaceae, Eggerthella, Bifidobacterium, Faecalibacterium, and Bacteroides and the risk of allergic diseases. Furthermore, our results also pointed out a number of putative associations between the gut microbiome and allergic diseases. Taken together, this study was the first study using the approach of 2SMR to elucidate the association between gut microbiome and allergic diseases.

BackgroundObservational studies have indicated a potential association between autoimmune diseases and the occurrence of Osteoarthritis (OA), with an increased risk of mortality among affected patients. However, whether a causal relationship exists between the two remains unknown.MethodsIn the Mendelian randomization (MR) study, we accessed exposure Genome-wide association study (GWAS) data from both the MRC Integrative Epidemiology Unit (MRC-IEU) and the FinnGen consortium. GWAS data for OA were obtained from MRC-IEU. We employed univariable, multivariable, and reverse MR analyses to explore potential associations between autoimmune disorders and OA. Additionally, a two-step mediation MR analysis was performed to investigate indirect factors possibly influencing the relationship between autoimmune disorders and OA. Afterward, we conducted an observational analysis to further explore the relationship between autoimmune disease and occurrence as well as of OA using a real-world database (the MIMIC-IV database). Based on public gene expression sequencing data, we further explored the potential shared pathogenesis between autoimmune diseases and OA.ResultsIn our univariable MR study, we identified five autoimmune diseases that are associated with OA. These include Celiac disease (OR = 1.061, 95% CI = 1.018–1.105, p = 0.005), Crohn’s disease (OR = 1.235, 95% CI = 1.149–1.327, p = 9.44E-09), Ankylosing spondylitis (OR = 2.63, 95% CI = 1.21–5.717, p = 0.015), RA (OR = 1.082, 95% CI = 1.034–1.133, p = 0.001), and Ulcerative colitis (OR = 1.175, 95% CI = 1.068–1.294, p = 0.001). In the mediation effect analysis, it was found that there is no correlation between cytokines and autoimmune diseases and OA. Based on transcriptome data analysis, it was found that metabolism-related pathways play a key role in the co-morbidity of autoimmune diseases and OA.ConclusionOur findings revealed that genes associated with Celiac disease, Crohn’s disease, Ankylosing spondylitis, RA, and Ulcerative colitis were independently linked to the development of OA. Furthermore, we conducted an analysis of potential pathogenic genes between these diseases and OA, offering a novel approach for the simultaneous treatment of multiple conditions.

PurposeTo investigate the causal relationship between gut microbiota (GM) and chalazion through Mendelian randomization (MR) analysis.MethodsGM-related genome-wide association studies (GWAS) were obtained from the International Consortium MiBioGen. Genetic data for chalazion were sourced from the MRC Integrative Epidemiology Unit (IEU) Open GWAS database. Five MR methods, including inverse variance weighted (IVW), were employed to estimate causal relationships. Cochran’s Q test was used to detect heterogeneity, the MR-Egger intercept test and MR-PRESSO regression were utilized to detect horizontal pleiotropy, and the leave-one-out method was employed to validate data stability.ResultsWe identified 1,509 single nucleotide polymorphisms (SNPs) across 119 genera as instrumental variables (IVs) (p 

It remains unclear about the association between stroke and Coronavirus disease 2019 (COVID-19). Therefore, a Mendelian randomization (MR) analysis was conducted to explore the relationship between them. In this study, the most recent large-scale genome-wide association studies (GWASs) were selected from the publicly available COVID-19 GWAS meta-analysis (Round 7) as the exposure. Data from a recent original stroke GWAS were used as the outcome for the experimental group, while the stroke GWAS data from the IEU GWAS database were used as the validation group. In addition, an MR analysis was conducted to explore the causal relationships of susceptibility, hospitalization, and severity of COVID-19 with stroke and its subtypes. In addition, the results of the experimental and validation groups were integrated to perform a meta-analysis. The MR analysis results corroborated that there was a positively causal relationship between the hospitalization (OR, 1.11; p = 0.015) (ORmeta, 1.11; p 

BackgroundAtrial fibrillation (AF) is a common persistent arrhythmia characterized by rapid and chaotic atrial electrical activity, potentially leading to severe complications such as thromboembolism, heart failure, and stroke, significantly affecting patient quality of life and safety. As the global population ages, the prevalence of AF is on the rise, placing considerable strains on individuals and healthcare systems. This study utilizes bioinformatics and Mendelian Randomization (MR) to analyze transcriptome data and genome-wide association study (GWAS) summary statistics, aiming to identify biomarkers causally associated with AF and explore their potential pathogenic pathways.MethodsWe obtained AF microarray datasets GSE41177 and GSE79768 from the Gene Expression Omnibus (GEO) database, merged them, and corrected for batch effects to pinpoint differentially expressed genes (DEGs). We gathered exposure data from expression quantitative trait loci (eQTL) and outcome data from AF GWAS through the IEU Open GWAS database. We employed inverse variance weighting (IVW), MR-Egger, weighted median, and weighted model approaches for MR analysis to assess exposure-outcome causality. IVW was the primary method, supplemented by other techniques. The robustness of our results was evaluated using Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out sensitivity analysis. A “Veen” diagram visualized the overlap of DEGs with significant eQTL genes from MR analysis, referred to as common genes (CGs). Additional analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and immune cell infiltration studies, were conducted on these intersecting genes to reveal their roles in AF pathogenesis.ResultsThe combined dataset revealed 355 differentially expressed genes (DEGs), with 228 showing significant upregulation and 127 downregulated. Mendelian randomization (MR) analysis identified that the autocrine motility factor receptor (AMFR) [IVW: OR = 0.977; 95% CI, 0.956–0.998; P = 0.030], leucine aminopeptidase 3 (LAP3) [IVW: OR = 0.967; 95% CI, 0.934–0.997; P = 0.048], Rab acceptor 1 (RABAC1) [IVW: OR = 0.928; 95% CI, 0.875–0.985; P = 0.015], and tryptase beta 2 (TPSB2) [IVW: OR = 0.971; 95% CI, 0.943–0.999; P = 0.049] are associated with a reduced risk of atrial fibrillation (AF). Conversely, GTPase-activating SH3 domain-binding protein 2 (G3BP2) [IVW: OR = 1.030; 95% CI, 1.004–1.056; P = 0.024], integrin subunit beta 2 (ITGB2) [IVW: OR = 1.050; 95% CI, 1.017–1.084; P = 0.003], glutaminyl-peptide cyclotransferase (QPCT) [IVW: OR = 1.080; 95% CI, 1.010–0.997; P = 1.154], and tripartite motif containing 22 (TRIM22) [IVW: OR = 1.048; 95% CI, 1.003–1.095; P = 0.035] are positively associated with AF risk. Sensitivity analyses indicated a lack of heterogeneity or horizontal pleiotropy (P > 0.05), and leave-one-out analysis did not reveal any single nucleotide polymorphisms (SNPs) impacting the MR results significantly. GO and KEGG analyses showed that CG is involved in processes such as protein polyubiquitination, neutrophil degranulation, specific and tertiary granule formation, protein-macromolecule adaptor activity, molecular adaptor activity, and the SREBP signaling pathway, all significantly enriched. The analysis of immune cell infiltration demonstrated associations of CG with various immune cells, including plasma cells, CD8T cells, resting memory CD4T cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells, activated mast cells, and neutrophils.ConclusionBy integrating bioinformatics and MR approaches, genes such as AMFR, G3BP2, ITGB2, LAP3, QPCT, RABAC1, TPSB2, and TRIM22 are identified as causally linked to AF, enhancing our understanding of its molecular foundations. This strategy may facilitate the development of more precise biomarkers and therapeutic targets for AF diagnosis and treatment.

BackgroundMany observational studies have been reported that patients with autoimmune or allergic diseases seem to have a higher risk of developing senile cataract, but the views are not consistent. In order to minimize the influence of reverse causality and potential confounding factors, we performed Mendelian Randomization (MR) analysis to investigate the genetic causal associations between autoimmune, allergic diseases and senile cataract.MethodsSingle nucleotide polymorphisms associated with ten common autoimmune and allergic diseases were obtained from the IEU Open genome-wide association studies (GWAS) database. Summary-level GWAS statistics for clinically diagnosed senile cataract were obtained from the FinnGen research project GWAS, which consisted of 59,522 individuals with senile cataracts and 312,864 control individuals. MR analysis was conducted using mainly inverse variance weighted (IVW) method and further sensitivity analysis was performed to test robustness.ResultsAs for ten diseases, IVW results confirmed that type 1 diabetes (OR = 1.06; 95% CI = 1.05-1.08; p = 2.24×10-12), rheumatoid arthritis (OR = 1.05; 95% CI = 1.02-1.08; p = 1.83×10-4), hypothyroidism (OR = 2.4; 95% CI = 1.42-4.06; p = 1.12×10-3), systemic lupus erythematosus (OR = 1.02; 95% CI = 1.01-1.03; p = 2.27×10-3), asthma (OR = 1.02; 95% CI = 1.01-1.03; p = 1.2×10-3) and allergic rhinitis (OR = 1.07; 95% CI = 1.02-1.11; p = 2.15×10-3) were correlated with the risk of senile cataract. Celiac disease (OR = 1.04; 95% CI = 1.01-1.08; P = 0.0437) and atopic dermatitis (OR = 1.05; 95% CI = 1.01-1.10; P = 0.0426) exhibited a suggestive connection with senile cataract after Bonferroni correction. These associations are consistent across weighted median and MR Egger methods, with similar causal estimates in direction and magnitude. Sensitivity analysis further proved that these associations were reliable.ConclusionsThe results of the MR analysis showed that there were causal relationships between type 1 diabetes, rheumatoid arthritis, hypothyroidism, systemic lupus erythematosus, asthma, allergic rhinitis and senile cataract. To clarify the possible role of autoimmune and allergy in the pathophysiology of senile cataract, further studies are needed.

BackgroundConstipation is affected by a number of risk variables, including cardiovascular disease and growth factors. However, the impacts of gut flora on constipation incidence has not been shown. This work, Single-Variable Mendelian Randomization (SVMR) was utilized to estimate the causal relationship between the Eubacterium genus or Rumphococcus, and constipation.MethodsData for constipation, Eubacterium genus and Rumphococcus were taken from the Integrated Epidemiology Unit (IEU) open GWAS database. Including 218,792 constipation samples, and there were 16,380,466 Single Nucleotide Polymorphisms (SNPs) for constipation. The ids of Eubacterium genus and Rumphococcus were sourced from MiBioGen database. The sample count for the Eubacterium genus was 17,380, with 656 SNPs. In addition, the sample size for Rumphococcus was 15,339, with 545 SNPs. The SVMR was performed to assess the risk of Eubacterium genus and Rumphococcus in constipation using weighted median, MR Egger, simple mode, inverse variance weighted (IVW), and weighted mode. Finally, we did a sensitivity analysis that included a heterogeneity, horizontal pleiotropy, and Leave-One-Out (LOO) test to examine the viability of the MR data.ResultsThe SVMR revealed that the Eubacterium genus and Rumphococcus were causally connected to constipation, with Rumphococcus (P = 0.042, OR = 1.074) as a hazardous factor and Eubacterium genus (P = 0.004, OR = 0.909) as a safety factor. Sensitivity tests then revealed the absence of variability between the constipation and the exposure factors (Eubacterium genus and Rumphococcus). Additionally, there were no other confounding factors and the examined SNPs could only influence constipation through the aforementioned exposure factors, respectively. As a result, the MR results were fairly robust.ConclusionOur investigation verified the causal links between the Eubacterium genus or Rumphococcus, and constipation, with greater Rumphococcus expression increasing the likelihood of constipation and the opposite being true for the Eubacterium genus.

ObjectivesThis study aims to investigate the causal relationship between Alzheimer’s Disease (AD) and Diabetic Retinopathy (DR).MethodsEmploying Mendelian Randomization (MR), Generalized Summary-data-based Mendelian Randomization (GSMR), and the MR-Steiger test, this study scrutinizes the genetic underpinnings of the hypothesized causal association between AD and DR, as well as its Proliferative DR (PDR) and Non-Proliferative DR (NPDR) subtypes. Comprehensive data from Genome-Wide Association Studies (GWAS) were analyzed, specifically AD data from the Psychiatric Genomics Consortium (71,880 cases/383,378 controls), and DR, PDR, and NPDR data from both the FinnGen consortium (FinnGen release R8, DR: 5,988 cases/314,042 controls; PDR: 8,383 cases/329,756 controls; NPDR: 3,446 cases/314,042 controls) and the IEU OpenGWAS (DR: 14,584 cases/176,010 controls; PDR: 8,681 cases/204,208 controls; NPDR: 2,026 cases/204,208 controls). The study also incorporated Functional Mapping and Annotation (FUMA) for an in-depth analysis of the GWAS results.ResultsThe MR analyses revealed that genetic susceptibility to AD significantly increases the risk of DR, as evidenced by GWAS data from the FinnGen consortium (OR: 2.5090; 95% confidence interval (CI):1.2102-5.2018, false discovery rate P-value (PFDR)=0.0201; GSMR: bxy=0.8936, bxy_se=0.3759, P=0.0174), NPDR (OR: 2.7455; 95% CI: 1.3178-5.7197, PFDR=0.0166; GSMR: bxy=0.9682, bxy_se=0.3802, P=0.0126), and PDR (OR: 2.3098; 95% CI: 1.2411-4.2986, PFDR=0.0164; GSMR: bxy=0.7962, bxy_se=0.3205, P=0.0129) using DR GWAS from FinnGen consortium. These results were corroborated by DR GWAS datasets from IEU OpenGWAS. The MR-Steiger test confirmed a significant association of all identified instrumental variables (IVs) with AD. While a potential causal effect of DR and its subtypes on AD was identified, the robustness of these results was constrained by a low power value. FUMA analysis identified OARD1, NFYA, TREM1 as shared risk genes between DR and AD, suggesting a potential genetic overlap between these complex diseases.DiscussionThis study underscores the contribution of AD to an increased risk of DR, as well as NPDR and PDR subtypes, underscoring the necessity of a holistic approach in the management of patients affected by these conditions.

BackgroundResearch findings indicate a putative indirect or latent association between phenylalanine (Phe) and Parkinson’s disease (PD). In this study, we aimed to analyze the causal relationship between Phe and PD by two sample Mendelian randomization (MR) analysis.MethodsIn this study, the PD-related dataset and Phe-related dataset were downloaded from Integrative Epidemiology U1nit (IEU) Open Genome-Wide Association Study (GWAS) database. Four algorithms (MR Egger, maximum likelihood, inverse variance weighting (IVW) and unweighted regression) were used to perform MR analysis. The sensitivity analysis (heterogeneity test, horizontal pleiotropy test and Leave-One-Out (LOO) analysis) was used to assess the reliability of MR analyses.ResultsIn the forward MR analysis, Phe was a safety factor for PD (p-value < 0.05 and odds ratios (OR) < 1). The results of reverse MR analysis showed that there was no causal relationship between PD and Phe (p-value > 0.05). In addition, sensitivity analysis showed that MR analysis was reliable.ConclusionThe results of this study revealed that Phe was a safety factor for PD, meaning that Phe reduced the risk of PD.