Linkage Disequilibrium Score Regression Research Articles

The causal relationship between human blood metabolites and the risk of visceral obesity: a mendelian randomization analysis

BackgroundWe aimed to explore the causal relationship between blood metabolites and the risk of visceral obesity, as measured by visceral adipose tissue (VAT).MethodsSummary statistics for 486 blood metabolites and total, as well as sex-stratified, MRI-derived VAT measurements, adjusted for body mass index (BMI) and height, were collected from previous genome-wide association studies (GWAS). A two-sample Mendelian Randomization (MR) design was used. Comprehensive evaluation was further conducted, including sensitivity analysis, linkage disequilibrium score (LDSC) regression, Steiger test, and metabolic pathway analysis.ResultsAfter multiple testing correction, arachidonate (20:4n6) has been implicated in VAT accumulation (β = 0.35, 95%CI:0.18–0.52, P < 0.001; FDR = 0.025). Additionally, several blood metabolites were identified as potentially having causal relationship (FDR < 0.10). Among them, lysine (β = 0.67, 95%CI: 0.28–1.06, P < 0.001; FDR = 0.074), proline (β = 0.30, 95%CI:0.13–0.48, P < 0.001; FDR = 0.082), valerate (β = 0.50, 95%CI:0.23–0.78, P < 0.001, FDR = 0.091) are associated with an increased risk of VAT accumulation. On the other hand, glycine (β=-0.21, 95%CI: -0.33–0.09), P < 0.001, FDR = 0.076) have a protective effect against VAT accumulation. Most blood metabolites showed consistent trends between different sexes. Multivariable MR analysis demonstrated the effect of genetically predicted arachidonate (20:4n6) and proline on VAT remained after accounting for BMI and glycated hemoglobin (HbA1c). There is no evidence of heterogeneity, pleiotropy, and reverse causality.ConclusionOur MR findings suggest that these metabolites may serve as biomarkers, as well as for future mechanistic exploration and drug target selection of visceral obesity.

Mendelian Randomization Highlights Gut Microbiota of Short-chain Fatty Acids' Producer as Protective Factor of Cerebrovascular Disease.

Recent research advancements have indicated a potential association between gut microbiota and cerebrovascular diseases, although the precise causative pathways and the directionality of this association remain to be fully elucidated. This study utilized a bidirectional two-sample Mendelian Randomization (MR) methodology to explore the causal impact of gut microbiota compositions on the risk of cerebrovascular disease. Genome-wide Association Study (GWAS) data pertaining to gut microbiota were obtained from the MiBioGen consortium. For Ischemic Stroke (IS), Transient Ischemic Attack (TIA), Vascular Dementia (VD), and Subarachnoid Hemorrhage (SAH), GWAS summary data were sourced from the FinnGen consortium, the IEU Open GWAS project, and the GWAS catalog, respectively. Our MR analyses identified that specific bacterial strains, notably those involved in the production of Short-chain Fatty Acids (SCFAs), including Barnesiella, Ruminococcus torques group, and Coprobacter, serve as protective factors against IS, TIA, and SAH. Linkage Disequilibrium Score Regression (LDSC) analysis corroborated a significant genetic correlation between these gut microbiota strains and various forms of cerebrovascular disease. In contrast, reverse MR analysis failed to establish a bidirectional causal relationship between genetically inferred gut microbiota profiles and these cerebrovascular conditions. This investigation has pinpointed particular strains of gut microbiota that play protective or detrimental roles in cerebrovascular disease pathogenesis. These findings offer valuable insights that could be pivotal for the clinical management, prevention, and treatment of cerebrovascular diseases.

Evidence for the gut-skin axis: Common genetic structures in inflammatory bowel disease and psoriasis.

Inflammatory bowel disease (IBD) and psoriasis (Ps) are common immune-mediated diseases that exhibit clinical comorbidity, possibly due to a common genetic structure. However, the exact mechanism remains unknown. The study population consisted of IBD and Ps genome-wide association study (GWAS) data. Genetic correlations were first evaluated. Then, the overall evaluation employed LD score regression (LDSC), while the local assessment utilized heritability estimation from summary statistics (HESS). Causality assessment was conducted through two-sample Mendelian randomization (2SMR), and genetic overlap analysis utilized the conditional false discovery rate/conjunctional FDR (cond/conjFDR) method. Finally, LDSC applied to specifically expressed genes (LDSC-SEG) was performed at the tissue level. For IBD and Ps-specific expressed genes, genetic correlation, causality, shared genetics, and trait-specific associated tissues were methodically examined. At the genomic level, both overall and local genetic correlations were found between IBD and Ps. MR analysis indicated a positive causal relationship between Ps and IBD. The conjFDR analysis with a threshold of<0.01 identified 43 loci shared between IBD and Ps. Subsequent investigations into disease-associated tissues indicated a close association of IBD and Ps with whole blood, lung, spleen, and EBV-transformed lymphocytes. The current research offers a novel perspective on the association between IBD and Ps. It contributes to an enhanced comprehension of the genetic structure and mechanisms of comorbidities in both diseases.

Shared genetic risk between anorexia nervosa and cardiovascular disease events: Evidence from genome-wide association studies.

Cardiovascular complications occur in up to 80% of patients with anorexia nervosa (AN), yet the underlying mechanisms warrant further investigation. We assessed the genetic correlation (rg ) between AN and cardiovascular disease (CVD) events to inform whether elevated cardiovascular risk among individuals with AN is due to shared genetic effects. We used genome-wide association study summary statistics for AN (N=72,517), AN with binge eating (N=12,630), AN without binge eating (N=12,516), and six CVD events (N=390,142 to 977,323). We calculated the rg s via linkage disequilibrium score regression and corrected for multiple testing using false discovery rate. Significant rg s emerged between AN with heart failure (rg =-0.11, SE=0.05, q=.04) and myocardial infarction (rg =-0.10, SE=0.03, q=.01). AN with binge eating had a significant rg with myocardial infarction (rg =-0.15, SE=0.06, q=.02). No significant rg emerged between AN without binge eating and any CVD event. Some loci affect the liability to AN and CVD in opposite directions and the shared genetic effects may not be consistent across all CVD events. Our results provide further evidence suggesting that the elevated cardiovascular risk in AN may not be due to shared genetic underpinnings, but more likely a downstream consequence of the disease.

Investigating the shared genetic architecture between hypothyroidism and rheumatoid arthritis.

There is still controversy regarding the relationship between hypothyroidism and rheumatoid arthritis (RA), and there has been a dearth of studies on this association. The purpose of our study was to explore the shared genetic architecture between hypothyroidism and RA. Using public genome-wide association studies summary statistics of hypothyroidism and RA, we explored shared genetics between hypothyroidism and RA using linkage disequilibrium score regression, ρ-HESS, Pleiotropic analysis under a composite null hypothesis (PLACO), colocalization analysis, Multi-Trait Analysis of GWAS (MTAG), and transcriptome-wide association study (TWAS), and investigated causal associations using Mendelian randomization (MR). We found a positive genetic association between hypothyroidism and RA, particularly in local genomic regions. Mendelian randomization analysis suggested a potential causal association of hypothyroidism with RA. Incorporating gene expression data, we observed that the genetic associations between hypothyroidism and RA were enriched in various tissues, including the spleen, lung, small intestine, adipose visceral, and blood. A comprehensive approach integrating PLACO, Bayesian colocalization analysis, MTAG, and TWAS, we successfully identified TYK2, IL2RA, and IRF5 as shared risk genes for both hypothyroidism and RA. Our investigation unveiled a shared genetic architecture between these two diseases, providing novel insights into the underlying biological mechanisms and establishing a foundation for more effective interventions.

Relationship between autism and brain cortex surface area: genetic correlation and a two-sample Mendelian randomization study

BackgroundAlterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA.MethodsSummarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship.ResultsLDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (β (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10− 3, PFDR = 3.09 × 10− 2), posterior cingulate gyrus (β (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10− 2, PFDR = 4.26 × 10− 2), supramarginal gyrus (β (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10− 2, PFDR = 4.31 × 10− 2).ConclusionOur results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.

SumVg: Total Heritability Explained by All Variants in Genome-Wide Association Studies Based on Summary Statistics with Standard Error Estimates.

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the "true" effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

Genetic evidence for the causal relations between metabolic syndrome and psychiatric disorders: a Mendelian randomization study

Emerging evidence reveals associations between metabolic syndrome (MetS) and psychiatric disorders (PDs), although causality remains uncertain. Consequently, we conducted Mendelian randomization (MR) to systematically evaluate the causality between MetS and PDs. Linkage disequilibrium score regression estimated the heritability of PDs and their genetic correlations with MetS. In primary analyses, the main model employed inverse variance weighting method, with sensitivity analyses using various MR models to ensure robustness. Replication MR analyses, involving cohorts distinct from those in the primary analyses, were performed to validate the generalizability of the findings. Multivariable MR analyses were carried out to account for genetically predicted body mass index (BMI). As a result, genetic correlations of MetS with attention-deficit/hyperactivity disorder(ADHD), anorexia nervosa(ANO), major depressive disorder(MDD), and schizophrenia were identified. Causal effects of MetS on ADHD (OR: 1.59 [95% CI:1.45–1.74]), ANO (OR: 1.42 [95% CI:1.25–1.61]), MDD(OR: 1.23 [95% CI: 1.13–1.33]), and the effects of ADHD (OR: 1.03 [95% CI: 1.02–1.04]) and ANO (OR: 1.01 [95% CI: 1.01–1.02]) on MetS were observed in primary analyses. Results from sensitivity analyses and replication analyses were generally consistent with the primary analyses, confirming the robustness and generalizability of the findings. Associations between MetS and ADHD, as well as ANO persisted after adjusting for BMI, whereas the statistical significance of the association between MetS and MDD was no longer observable. These results contribute to a deeper understanding of the mechanisms underlying PDs, suggesting potential modifiable targets for public prevention and clinical intervention in specific PDs related to metabolic pathways.

Mendelian randomization analyses of known and suspected risk factors and biomarkers for myasthenia gravis overall and by subtypes

BackgroundMyasthenia gravis (MG) is an autoimmune disease that affects neuromuscular junction. The literature suggests the involvement of circulating cytokines (CK), gut microbiota (GM), and serum metabolites (SM) with MG. However, this research is limited to observational trials, and comprehensive causal relationship studies have not been conducted. Based on published datasets, this investigation employed Mendelian Randomization (MR) to analyze the known and suspected risk factors and biomarkers causal association of MG and its subtypes.MethodsThis research used two-sample MR and linkage disequilibrium score (LDSC) regression of multiple datasets to aggregate datasets acquired from the genome-wide association studies (GWAS) to assess the association of MG with 41-CK, 221-GM, and 486-SM. For sensitivity analysis and to validate the robustness of the acquired data, six methods were utilized, including MR-Egger regression, inverse variance weighting (IVW), weighted median, and MR-PRESSO.ResultsThe MR method identified 20 factors significantly associated with MG, including 2 CKs, 6 GMs, and 9 SMs. Further analysis of the factors related to the two MG subtypes, early-onset MG (EOMG) and late-onset MG (LOMG), showed that EOMG had a high overlap with MG in the intestinal flora, while LOMG had a greater similarity in CKs and SMs. Furthermore, LDSC regression analysis indicated that Peptococcaceae, oxidized biliverdin, and Kynurenine had significant genetic correlations with general MG, whereas EOMG was highly correlated with Intestinibacter, while LOMG had significant genetic associations with Kynurenine and Glucose.ConclusionThis research furnishes evidence for the potential causal associations of various risk factors with MG and indicates a heterogeneous relationship between CKs, GMs, and SMs with MG subtypes.

Major depressive disorder and aneurysm: A genetic study of association and causality

BackgroundAssociation between depression and aneurysm has been implicated but the specific role of depression in aneurysm remains unclear. We aimed to comprehensively characterize the relation of major depressive disorder (MDD) with aneurysm by subtype. MethodsHarnessing summary statistics from genome-wide association studies (Ncase/Ncontrol = 7603/317,899 for aortic aneurysm; 7321/317,899 for thoracic aortic aneurysm; 3201/317,899 for abdominal aortic aneurysm; 1788/317,899 for cerebral aneurysm; and 246,363/561,190 for major depressive disorder), we estimated the genetic correlation between MDD and each of four aneurysm subtypes via LD Score Regression and tested the causality via various estimators under the bi-directional Mendelian randomization (MR) framework. ResultsPositive genetic correlation of statistical significance, ranging between 0.15 (with thoracic aortic aneurysm, P = 0.005) and 0.25 (with abdominal aortic aneurysm, P = 0.001), was consistently observed for MDD with each aneurysm subtype. In the MR analysis of MDD as an exposure, genetic liability to MDD causally increased the risk of cerebral (odds ratio: 1.71; 95 % confidence interval: 1.26–2.34) but not aortic aneurysm. Replication analysis of an independent dataset (Ncase/Ncontrol = 6242/59,418) corroborated this signal. In contrast, causal effect was not evident for any neurysm subtype on susceptibility to MDD. LimitationsAneurysm could have been underdiagnosed if asymptomatic, leading to an underestimated causal impact on MDD. Non-linearity of the causal effect was not tested due to the lack of individual-level data. ConclusionsDepression and aneurysm may share common pathomechanisms. Screening depressed population and improving the clinical management for depression may benefit the primary prevention of cerebral aneurysm.

Identification of the shared genetic architecture underlying seven autoimmune diseases with GWAS summary statistics.

The common clinical symptoms and immunopathological mechanisms have been observed among multiple autoimmune diseases (ADs), but the shared genetic etiology remains unclear. GWAS summary statistics of seven ADs were downloaded from Open Targets Genetics and Dryad. Linkage disequilibrium score regression (LDSC) was applied to estimate overall genetic correlations, bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap, and stratified-LDSC partitioned heritability to reveal tissue and cell type specific enrichments. Ultimately, we conducted a novel adaptive association test called MTaSPUsSet for identifying pleiotropic genes. The high heritability of seven ADs ranged from 0.1228 to 0.5972, and strong genetic correlations among certain phenotypes varied between 0.185 and 0.721. There was substantial polygenic overlap, with the number of shared SNPs approximately 0.03K to 0.21K. The specificity of SNP heritability was enriched in the immune/hematopoietic related tissue and cells. Furthermore, we identified 32 pleiotropic genes associated with seven ADs, 23 genes were considered as novel genes. These genes were involved in several cell regulation pathways and immunologic signatures. We comprehensively explored the shared genetic architecture across seven ADs. The findings progress the exploration of common molecular mechanisms and biological processes involved, and facilitate understanding of disease etiology.

COSGAP: COntainerized Statistical Genetics Analysis Pipelines.

The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: "COntainerized Statistical Genetics Analysis Pipelines," consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements. COSGAP is freely available on GitHub (https://github.com/comorment/containers) under the GPLv3 license.

Causal association of genetically determined plasma metabolites with osteoarthritis: a two-sample Mendelian randomization study.

We aimed to elucidate the causal relationship between plasma metabolites and the vulnerability to Osteoarthritis (OA), encompassing both hip OA and knee OA. We conducted a two-way two-sample Mendelian randomization (MR) analysis to investigate the association of 1,400 plasma metabolites with OA. The Inverse Variance Weighted (IVW) model served as the primary two-sample MR Analysis method, with supplementary analysis using the Weighted Median (WM) and MR Egger methods. To ensure the robustness of our findings, sensitivity analyses were performed, incorporating Cochran's Q test, MR-Egger intercept test, MR-PRESSO, and Leave-One-Out analyses. To validate the identified metabolites, we utilized the Steiger test and linkage disequilibrium score regression. A total of 94 plasma metabolites were associated with osteoarthritis, with 60 associated with hip OA and 106 associated with knee OA. IVW analysis revealed that tryptophan levels showed the strongest positive association with hip OA (OR [95% CI]: 1.119 [1.024, 1.223]), while X-24757 levels exhibited the highest positive association with knee osteoarthritis (OR [95% CI]: 1.095 [1.032, 1.162]). Ethylparaben sulfate levels were found to have the greatest positive association with hip OA (OR [95% CI]: 1.118 [1.015, 1.231]). Notably, the plasma metabolite X-2475 showed a strong robust random effect across all three types of osteoarthritis. Metabolic pathway analysis revealed that the pathogenesis of osteoarthritis in the hip was mediated by acetylarginine, specifically in four important metabolic pathways: ethanol degradation (p = 0.044), amino sugar metabolism (p = 0.090), fatty acid biosynthesis (p = 0.095), and aspartate metabolism (p = 0.097816). There is a significant association between tryptophan levels and the risk of hip OA, as well as X-24757 levels and the risk of knee osteoarthritis. Additionally, X-24757 levels are also linked to the risk of hip OA. Moreover, this study has identified four crucial metabolic pathways in hip osteoarthritis, which are all regulated by acetylarginine. These findings provide valuable insights into potential biomarkers for OA and highlight potential pathways for its prevention and clinical intervention.

Investigating the shared genetic architecture between breast and ovarian cancers.

High heritability and strong correlation have been observed in breast and ovarian cancers. However, their shared genetic architecture remained unclear. Linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) were applied to estimate heritability and genetic correlations. Bivariate causal mixture model (MiXeR) was used to qualify the polygenic overlap. Then, stratified-LDSC (S-LDSC) was used to identify tissue and cell type specificity. Meanwhile, the adaptive association test called MTaSPUsSet was performed to identify potential pleiotropic genes. The Single Nucleotide Polymorphisms (SNP) heritability was 13% for breast cancer and 5% for ovarian cancer. There was a significant genetic correlation between breast and ovarian cancers (rg=0.21). Breast and ovarian cancers exhibited polygenic overlap, sharing 0.4 K out 2.8 K of causal variants. Tissue and cell type specificity displayed significant enrichment in female breast mammary, uterus, kidney tissues, and adipose cell. Moreover, the 74 potential pleiotropic genes were identified between breast and ovarian cancers, which were related to the regulation of cell cycle and cell death. We quantified the shared genetic architecture between breast and ovarian cancers and shed light on the biological basis of the co-morbidity. Ultimately, these findings facilitated the understanding of disease etiology.

Genetic associations between gut microbiota and type 2 diabetes mediated by plasma metabolites: a Mendelian randomization study.

Numerous research studies have indicated a possible association between type 2 diabetes (T2DM) and gut microbiota. To explore specific metabolic pathways connecting gut microbiota and T2DM, we employed Mendelian randomization (MR) and linkage disequilibrium score regression (LDSC) techniques. This research utilized data from genome-wide association studies (GWAS) that are publicly accessible. We evaluated the genetic correlation between gut microbiota and T2DM using LDSC. Causality was primarily determined through the inverse variance weighted (IVW) method. To verify the robustness of our results, we conducted sensitivity analyses using several approaches, including the weighted median, MR-Egger, and MR-PRESSO. We integrated summary effect estimates from LDSC, along with forward and reverse MR, into a meta-analysis for T2DM using various data sources. Additionally, mediation analysis was performed to explore the impact of plasma metabolites on the relationship between gut microbiota and T2DM. Our study indicated a significant genetic correlation between genus RuminococcaceaeUCG005 (Rg = -0.26, Rg_P = 2.07×10-4) and T2DM. Moreover, the forward MR analysis identified genus RuminococcaceaeUCG010 (OR = 0.857, 95% CI 0.795, 0.924; P = 6.33×10-5) and order Clostridiales (OR = 0.936, 95% CI 0.878, 0.997; P = 0.039) as being significantly associated with a decreased risk of T2DM. The analysis also highlighted several plasma metabolites as significant mediators in these relationships, with metabolites like octadecadienedioate (C18:2-DC) and branched chain 14:0 dicarboxylic acid being notably involved. The findings demonstrate a significant impact of gut microbiota on T2DM via plasma metabolites, suggesting potential metabolic pathways for therapeutic targeting. This study enhances our understanding of the microbiota's role in T2DM pathogenesis and supports the development of microbiota-based interventions.

Cardiometabolic traits mediating the effect of education on the risk of DKD and CKD: a Mendelian randomization study.

Both diabetic kidney disease (DKD) and chronic kidney disease (CKD) are more prevalent among individuals with lower levels of education in observational studies. To quantify the mediation effect of recognized cardiometabolic traits, we obtain causal estimates between education and DKD as well as CKD. We assessed the causal effect of education on DKD and CKD, separately estimated the causal effect of 26 cardiometabolic traits on DKD and CKD, and finally calculated the mediating effects and mediating proportions of each using two-step, two-sample multivariable Mendelian randomization (MVMR). Furthermore, the genetic association between exposure, mediators, and outcomes was investigated using linkage disequilibrium score (LDSC) regression analysis. Expression quantitative trait loci (eQTL) were retrieved from the Genotype-Tissue Expression Project (GTEx) v8 to serve as genetic instrumental variables. Transcriptome-wide association studies (TWAS), Bayesian colocalization analysis, and Summary-data-based Mendelian Randomization (SMR) analysis were performed to explore underlying susceptibility genes between education, mediators, and kidney diseases. Higher education with a genetically predicted 1-SD (4.2 years) was linked to a 48.64% decreased risk of DKD and a 29.08% decreased risk of CKD. After extensive evaluation of 26 cardiometabolic traits, 7 and 6 causal mediators were identified as mediating the effects of education on DKD and CKD, respectively. The largest mediating factor between education and DKD was BMI, which was followed by WHR, T2D, fasting insulin, SBP, fasting glucose, and DBP. In contrast, candidate mediators in the education-to-CKD pathway included BMI, followed by cigarettes smoked per day, WHR, SBP, T2D, and DBP. MR analysis revealed that TP53INP1 was found to be a shared susceptibility gene for cardiometabolic traits and DKD, while L3MBTL3 was found to be a shared susceptibility gene for cardiometabolic traits and CKD. Our findings provide solid evidence that education has a causally protective effect on the development of DKD and CKD. We additionally reveal significant directions for intervention on cardiometabolic traits that mitigate the negative effects of educational inequities on the onset of DKD and CKD. Our work demonstrates a shared genetic basis between education, cardiometabolic traits, and kidney diseases. Future research aiming at lowering kidney risk may benefit from these findings.

Causal relationships between lung cancer and sepsis: a genetic correlation and multivariate mendelian randomization analysis.

Former research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear. This study used univariate Mendelian Randomization (MR) to explore the causal relationship between LC, its subtypes, and sepsis. Linkage Disequilibrium Score (LDSC) regression was used to calculate genetic correlations. Multivariate MR was applied to investigate the role of seven confounding factors. The primary method utilized was inverse-variance-weighted (IVW), supplemented by sensitivity analyses to assess directionality, heterogeneity, and result robustness. LDSC analysis revealed a significant genetic correlation between LC and sepsis (genetic correlation = 0.325, p = 0.014). Following false discovery rate (FDR) correction, strong evidence suggested that genetically predicted LC (OR = 1.172, 95% CI 1.083-1.269, p = 8.29 × 10-5, P fdr = 2.49 × 10-4), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021-1.181, p = 0.012, P fdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024-1.178, p = 0.009, P fdr = 0.012) are linked to an increased incidence of sepsis. Suggestive evidence was also found for small cell lung carcinoma (Wald ratio: OR = 1.156, 95% CI 1.047-1.277, p = 0.004) in relation to sepsis. The multivariate MR suggested that the partial impact of all LC subtypes on sepsis might be mediated through body mass index. Reverse analysis did not find a causal relationship (p > 0.05 and P fdr > 0.05). The study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients.

Exploring the Common Genetic Underpinnings of Chronic Pulmonary Disease and Esophageal Carcinoma Susceptibility.

Background: Pulmonary diseases and esophageal cancer are highly prevalent conditions with rising incidence worldwide. Prior evidence supports shared environmental and behavioral factors, but less is known regarding potential genetic links underlying this comorbidity. This study aimed to elucidate the complex genetic relationship between chronic lung diseases and esophageal cancer risk. Methods: Linkage disequilibrium score regression assessed the genetic correlation between esophageal cancer and asthma, COPD, and idiopathic pulmonary fibrosis leveraging extensive GWAS datasets. Pleiotropic analysis, gene-set enrichment, eQTL mapping, and mendelian randomization causality analyses were then conducted to identify specific shared genetic variants, enriched pathways, causal relationships and gene regulatory mechanisms connecting lung disease and cancer susceptibility. Results: Significant genetic correlations were observed between esophageal cancer and both COPD and asthma, but not idiopathic pulmonary fibrosis. Further analyses identified 13 pleiotropic loci and 6 shared genes including CHRNA4, ERBB3, and SMAD3, as well as pathways related to immune function. eQTL integration highlighted 53 genes like SOCS1, FGF2, and CHRNA5 with tissue-specific regulatory effects on disease risk. Bidirectional relationships were noted, whereby genetic predisposition to asthma and COPD increased esophageal cancer risk, while cancer liability reciprocally raised pulmonary fibrosis risk. Conclusions: These genomic analyses provide initial evidence that shared genetic factors may underpin the comorbidity between lung conditions and esophageal malignancy. The genes and pathways identified offer insights into biological mechanisms linking both diseases, aiding future screening, prevention and therapeutic efforts to mitigate this growing comorbidity burden.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns.

Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown. Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases. LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases. This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.