Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Walking pace is a protective factor for rheumatoid arthritis: a mendelian randomization study

Walking pace is a simple and functional form of exercise and a strong predictor of health, but little is known about its causal association with rheumatoid arthritis. This study aimed to investigate the causal effect of WP on the developing RA using Mendelian randomization analysis. The genetic variation associated with WP was selected as an instrumental variable from the latest genome-wide association studies. Summary-level data for the outcomes were obtained from the corresponding GWAS. The inverse-variance weighted method was used as the primary MR analysis. The results were further tested using a multivariable MR approach based on Bayesian model averaging. Confounders (BMI, SMK, HBP, TD) with close associations with RA were included in the analysis. An observational study with individual data from UK Biobank was performed to reinforce our findings. The MR results indicated the significant inverse associations of WP with the risk of RA (odds ratio (OR), 0.31; 95% confidence interval (CI), 0.15, 0.62; p = 1.05 × 10 -3). After adjusting for the risk factors, the associations for WP and RA did not change substantially. Observational study results demonstrated the same effect of WP on reducing the risk of RA. The Mendelian randomization analysis and observational study provide evidence suggesting that walking pace is a protective factor for rheumatoid arthritis. Given its simple measurement, walking pace may be a pragmatic target for interventions.

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms.

RESULTS

We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1).

CONCLUSIONS

This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.

Causal links between circulatory inflammatory cytokines and risk of digestive polyps: a Mendelian randomization analysis

Background

There is a high morbidity of polyps in the digestive tract, and certain subtypes of polyps are thought to induce cancer progression and often recur, which may be associated with chronic inflammation. Mendelian randomization (MR) can help identify potential causative relationships and inform early treatment action.

Methods

We performed a bidirectional two-sample MR analysis implementing the results from genome-wide association studies for 41 serum cytokines from 8,293 Finnish individuals, and three types of polyps from European ancestry, respectively, including gastric polyp (6,155 cases vs. 341,871 controls), colonic polyp (22,049 cases vs. 332,368 controls) and gallbladder polyp (458 cases vs. 340,083 controls). Inverse-variance weighted (IVW), weight median (WM), and MR-Egger methods were used for calculating causal estimates. Furthermore, Bayesian model averaging MR (MR-BMA) method was employed to detect the dominant causal circulatory cytokines with adjustment for pleiotropy effects.

Results

Our univariable MR using inverse-variance weight method identified causal associations of IL-2ra (OR: 0.892, 95%CI: 0.828-0.961, p = 0.003), MIG (OR: 1.124, 95%CI: 1.046-1.207, p = 0.001) and IL-18 (OR: 0.912, 95%CI: 0.852-0.977, p = 0.008) with gastric polyp, MIP1b (OR: 0.956, 95%CI: 0.927-0.987, p = 0.005) and IL-6 (OR: 0.931, 95%CI: 0.870-0.995, p = 0.035) with colonic polyp and IL-9 (OR: 0.523, 95%CI: 0.345-0.794, p = 0.0007) with gallbladder polyp. Finally, our MR-BMA analysis prioritized MIG (MIP = 0.332, MACE = 0.022; PP: 0.264, MSCE = 0.059), IL-18 (MIP = 0.302, MACE = -0.020; PP: 0.243, MSCE = -0.059) and IL-2ra (MIP: 0.129; MACE: -0.005; PP: 0.112, MSCE: -0.031) for gastric polyp, and MIP1b (MIP = 0.752, MACE = -0.033; PP: 0.665, MSCE = -0.044) and IL-6 (MIP: 0.196; MACE: -0.012; PP: 0.140, MSCE: -0.064) for colonic polyp, and IL-9 (MIP = 0.936, MACE = -0.446; PP: 0.781, MSCE = -0.478) for gallbladder polyp as the top-ranked protective factors.

Conclusion

Our research advances the current understanding of the function of certain inflammatory biomarker pathways in the genesis and malignant mutation of polyps in the digestive tract. Deeper substantiation is necessary to assess the potential of these cytokines as pharmacological or lifestyle targets for digestive polyps prevention.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

A novel concern from two sample Mendelian randomization study: The effects of air pollution exposure on the cardiovascular, respiratory, and nervous system

BACKGROUND

Cardiovascular, respiratory, and nervous system diseases have high morbidity and mortality rates, but the causal relationship between air pollution and these diseases remains controversial.

METHODS

We conducted a large-scale genome-wide association (GWAS) study using Mendelian randomization (MR) to investigate the association between air pollution like Nitrogen dioxide (NO2), Nitrogen oxides (NOX), Particulate matter with diameter<2.5μm (PM2.5), Particulate matter with diameter<10μm (PM10) and cardiovascular, respiratory, and nervous system diseases, including acute myocardial infarction, heart failure, asthma, chronic obstructive pulmonary disease (COPD), pneumonia, stroke and Parkinson's disease. This study included 337,199 patients with acute myocardial infarction, 178,726 patients with heart failure, 463,010 patients with asthma, 462,933 patients with COPD, 486,484 patients with pneumonia, 484,598 patients with stroke, and 482,730 patients with Parkinson's disease. All genetic tools were identified from GWAS. The association effects of environmental pollution and these diseases were investigated using MR analysis, sensitivity analysis with heterogeneity, pleiotropy test, and leave-one-out test.

RESULTS

Our MR analysis showed the association between NOX and the development of COPD and stroke (Odds ratio (OR)=1.010, 95 % Confidence interval (CI): 1.000～1.020, P=0.046; OR=1.017, 95 %CI:1.003-1.031, P=0.019), the association between PM2.5 and the development of asthma, COPD and stroke (OR=1.013, 95 %CI:1.003-1.024, P=0.011; OR=1.010, 95 %CI:1.000-1.019, P=0.035; OR=1.019, 95 %CI:1.004-1.033, P=0.012). No significant associations were found between the rest of the air pollution exposures and diseases. Leave-one-out sensitivity analysis showed stable results.

CONCLUSIONS

The study clarifies the relationship between air pollution and cardiovascular, respiratory, and nervous system diseases, providing valuable evidence for environmental pollution prevention and population health monitoring, and provides a clear direction and evidence for the subsequent investigation of the association between air pollution and diseases.

Investigating Causal Associations between the Gut Microbiota and Dementia: A Mendelian Randomization Study

Background: The causal association of specific gut microbiota with dementia remains incompletely understood. We aimed to access the causal relationships in which one or more gut microbiota account for dementia. Method: Using data from the MiBioGen and FinnGen consortia, we employed multiple Mendelian randomization (MR) approaches including two-sample MR (TSMR), multivariable MR (MVMR), and Bayesian model averaging MR to comprehensively evaluate the causal associations between 119 genera and dementia, and to prioritize the predominant bacterium. Result: We identified 21 genera that had causal effects on dementia and suggested Barnesiella (OR = 0.827, 95%CI = 0.722-0.948, marginal inclusion probability [MIP] = 0.464; model-averaged causal estimate [MACE] = -0.068) and Allisonella (OR = 0.770, 95%CI = 0.693-0.855, MIP = 0.898, MACE = -0.204) as the predominant genera for AD and all-cause dementia. Conclusions: These findings confirm the causal relationships between specific gut microbiota and dementia, highlighting the necessity of multiple MR approaches in gut microbiota analysis, and provides promising genera as potential novel biomarkers for dementia risk.

Genetically predicted blood metabolites mediate relationships between gut microbiota and ovarian cancer: a Mendelian randomization study

Background and purpose

While there is evidence that gut microbiota (GM) and blood metabolites are associated with ovarian cancer (OC), the precise mechanisms underlying this relationship are still unclear. This study used Mendelian randomization (MR) to elucidate the causal connections between GM, blood metabolite biomarkers, and OC.

Methods

In this study, we leveraged summary data for GM (5,959 individuals with genotype-matched GM), blood metabolites (233 circulating metabolic traits with 136,016 participants), and OC (63,702 participants with 23,564 cases and 40,138 controls) from genome-wide association studies (GWASs). We performed MR analysis to explore the causal relationship between GM and OC. Further, we harnessed univariable MR (UVMR) analysis to evaluate the causal associations between GM and circulating metabolites. Finally, we employed a two-step approach based on multivariable MR (MVMR) to evaluate the total genetic prediction effect of metabolites mediating the GM on the risk of OC to discover a potential causal relationship.

Results

In the MR analysis, 24 gut bacteria were causally associated with the pathogenesis of OC, including 10 gut bacteria (Dorea phocaeense, Succinivibrionaceae, Raoultella, Phascolarctobacterium sp003150755, Paenibacillus J, NK4A144, K10, UCG-010 sp003150215, Pseudomonas aeruginosa, and Planococcaceae) that were risk factors, and 14 gut bacteria (CAG-177 sp002438685, GCA-900066135 sp900066135, Enorma massiliensis, Odoribacter laneus, Ruminococcus E sp003521625, Streptococcus sanguinis, Turicibacter sp001543345, Bacillus velezensis, CAG-977, CyanobacteriaStaphylococcus A fleurettii, Caloranaerobacteraceae, RUG472 sp900319345, and CAG-269 sp001915995) that were protective factors. The UVMR analysis showed that these 24 positive gut bacteria were causally related to lipoproteins, lipids, and amino acids. According to the MVMR analysis, Enorma massiliensis could reduce the risk of OC by raising the total cholesterol to total lipids ratio in large low-density lipoprotein (LDL) and cholesteryl esters to total lipids ratio in intermediate-density lipoprotein (IDL). Turicibacter sp001543345, however, could reduce the risk of OC by lowering free cholesterol in small high-density lipoprotein (HDL) and increasing the ratios of saturated fatty acids to total fatty acids, total cholesterol to total lipids ratio in very small very-low-density lipoprotein (VLDL), and cholesteryl esters to total lipids ratio in very small VLDL.

Conclusion

The current MR study provides evidence that genetically predicted blood metabolites can mediate relationships between GM and OC.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics

Mendelian randomization (MR) utilizes genome-wide association study (GWAS) summary data to infer causal relationships between exposures and outcomes, offering a valuable tool for identifying disease risk factors. Multivariable MR (MVMR) estimates the direct effects of multiple exposures on an outcome. This study tackles the issue of highly correlated exposures commonly observed in metabolomic data, a situation where existing MVMR methods often face reduced statistical power due to multicollinearity. We propose a robust extension of the MVMR framework that leverages constrained maximum likelihood (cML) and employs a Bayesian approach for identifying independent clusters of exposure signals. Applying our method to the UK Biobank metabolomic data for the largest Alzheimer disease (AD) cohort through a two-sample MR approach, we identified two independent signal clusters for AD: glutamine and lipids, with posterior inclusion probabilities (PIPs) of 95.0% and 81.5%, respectively. Our findings corroborate the hypothesized roles of glutamate and lipids in AD, providing quantitative support for their potential involvement.

Inhalation of SP-101 Followed by Inhaled Doxorubicin Results in Robust and Durable hCFTRΔR Transgene Expression in the Airways of Wild-Type and Cystic Fibrosis Ferrets

Cystic fibrosis (CF) is a serious genetic disease caused by mutations in the CF transmembrane conductance regulator (CFTR) gene. Approved small molecule therapies benefit the majority of people with CF (pwCF), but unfortunately not all. Gene addition offers a mutation agnostic treatment option for all pwCF. SP-101 is an adeno-associated virus gene therapy vector (AAV2.5T) that has been optimized for efficient human airway cell transduction, and that contains a functional and regulated shortened human CFTR minigene (hCFTRΔR) with a small synthetic promoter/enhancer. To understand SP-101 airway distribution, activity, and the associated immune response, in vivo studies were performed in wild-type and CF ferrets. After single dose inhaled delivery of SP-101, followed by single dose inhaled doxorubicin (an AAV transduction augmenter) or saline, SP-101 vector genomes were detected throughout the respiratory tract. hCFTRΔR mRNA expression was highest in ferrets also receiving doxorubicin and persisted for the duration of the study (13 weeks). Pre-existing mucus in the CF ferrets did not present a barrier to effective transduction. Binding and neutralizing antibodies to the AAV2.5T capsid were observed regardless of doxorubicin exposure. Only a portion of ferrets exhibited a weak T-cell response to AAV2.5T and no T-cell response was seen against hCFTRΔR. These data strongly support the continued development of inhaled SP-101, followed by inhaled doxorubicin, for the treatment of CF.

From Lawn to Health: Understanding the Prostate Cancer Risk in Light DIY Activities

This study employs two-sample Mendelian Randomization (MR) analysis to investigate the causal relationship between light DIY activities and prostate cancer. We used single nucleotide polymorphisms (SNPs) associated with light DIY activities obtained from published genome-wide association studies (GWASs) and summary-level genetic data related to prostate cancer from published GWAS. The primary analysis was conducted using the inverse-variance weighted (IVW) method for two-sample MR analysis. Cochran's Q statistic was used to assess heterogeneity, MR-Egger was employed to detect horizontal pleiotropy, and "leave-one-out" analysis was performed for sensitivity analysis. Given the presence of heterogeneity, the random-effects IVW method was used for the primary analysis. The random-effects IVW results indicated a positive causal relationship between participation in light DIY activities and the risk of prostate cancer (odds ratio [OR] = 1.024, 95% confidence interval [CI]: 1.001-1.048; p = .039). The weighted median (WM) method results supported this finding (OR = 1.025, 95% CI: 1.003-1.048; p = .024). Participation in light DIY activities may slightly increase the risk of prostate cancer. This finding emphasizes the need to carefully consider the types and intensities of physical activities when making public health recommendations and personal lifestyle choices.

Integrative Metabolomics Differentiate Coronary Artery Disease, Peripheral Artery Disease, and Venous Thromboembolism Risks

BACKGROUND

Arterial and venous cardiovascular conditions, such as coronary artery disease (CAD), peripheral artery disease (PAD), and venous thromboembolism (VTE), are genetically correlated. Interrogating underlying mechanisms may shed light on disease mechanisms. In this study, we aimed to identify (1) epidemiological and (2) causal, genetic relationships between metabolites and CAD, PAD, and VTE.

METHODS

We used metabolomic data from 95 402 individuals in the UK Biobank, excluding individuals with prevalent cardiovascular disease. Cox proportional-hazards models estimated the associations of 249 metabolites with incident disease. Bidirectional 2-sample Mendelian randomization (MR) estimated the causal effects between metabolites and outcomes using genome-wide association summary statistics for metabolites (n=118 466 from the UK Biobank), CAD (n=184 305 from CARDIoGRAMplusC4D 2015), PAD (n=243 060 from the Million Veterans Project), and VTE (n=650 119 from the Million Veterans Project). Multivariable MR was performed in subsequent analyses.

RESULTS

We found that 196, 115, and 74 metabolites were associated (P<0.001) with CAD, PAD, and VTE, respectively. Further interrogation of these metabolites with MR revealed 94, 34, and 9 metabolites with potentially causal effects on CAD, PAD, and VTE, respectively. There were 21 metabolites common to CAD and PAD and 4 common to PAD and VTE. Many putatively causal metabolites included lipoprotein traits with heterogeneity across different sizes and lipid subfractions. Small VLDL (very-low-density lipoprotein) particles increased the risk for CAD while large VLDL particles decreased the risk for VTE. We identified opposing directions of CAD and PAD effects for cholesterol and triglyceride concentrations within HDLs (high-density lipoproteins). Subsequent sensitivity analyses including multivariable MR revealed several metabolites with robust, potentially causal effects of VLDL particles on CAD.

CONCLUSIONS

While common vascular conditions are associated with overlapping metabolomic profiles, MR prioritized the role of specific lipoprotein species for potential pharmacological targets to maximize benefits in both arterial and venous beds.

BMA-based Mendelian randomization identifies blood metabolites as causal candidates in pregnancy-induced hypertension

Pregnancy-induced hypertension (PIH), a prominent determinant of maternal mortality and morbidity worldwide, is hindered by the absence of efficacious biomarkers for early diagnosis, contributing to suboptimal outcomes. Here, we explored potential causal relationships between blood metabolites and the risk of PIH using Mendelian randomization (MR). We employed a two-sample univariable MR approach to empirically estimate the causal relationships between 249 circulating metabolites and PIH. Inverse variance weighted, MR-egger, weight median, simple mode, and weighted mode methods were used for causal estimates. The exposure-to-outcome directionality was confirmed with the MR Steiger test. The Bayesian model averaging MR (MR-BMA) method was applied to detect the predominant causal metabolic traits with alignment for pleiotropy effects. In the primary analysis, analyzing 249 metabolites, we identified 25 causally linked to PIH, including 11 lipid-related traits and 6 associated with fatty acid (un)saturation. Importantly, MR-BMA analyses corroborated the total concentration of branched-chain amino acids(total-BCAA) to be the highest rank causal metabolite, followed by leucine (Leu), phospholipids to total lipids ratio in medium LDL (M-LDL-PL-pct), and Val (all P < 0.05). The directionality of causality predicted by univariable MR and MR-BMA for these metabolites remained consistent. This study highlights the causal connection between metabolites and PIH risk. It highlighted BCAAs as the strongest causal candidates warranting further investigation. Since PIH typically occurs in the second and third trimesters, extending these findings could inform earlier strategies to reduce its risk. Directed acyclic graph of the MR framework investigating the causal relationship between metabolites and PIH. MR: Mendelian randomization; GIVs: genetic instrument variables; SNPs: single-nucleotide polymorphism; IVW: inverse variance weighted; WM: weighted median; PIH: pregnancy-induced hypertension; SM: significant metabolite; MR-BMA: Bayesian model averaging MR.

Causal Association between Circulating Metabolites and Dementia: A Mendelian Randomization Study

The causal association of circulating metabolites with dementia remains uncertain. We assessed the causal association of circulating metabolites with dementia utilizing Mendelian randomization (MR) methods. We performed univariable MR analysis to evaluate the associations of 486 metabolites with dementia, Alzheimer's disease (AD), and vascular dementia (VaD) risk. For secondary validation, we replicated the analyses using an additional dataset with 123 metabolites. We observed 118 metabolites relevant to the risk of dementia, 59 of which were lipids, supporting the crucial role of lipids in dementia pathogenesis. After Bonferroni adjustment, we identified nine traits of HDL particles as potential causal mediators of dementia. Regarding dementia subtypes, protective effects were observed for epiandrosterone sulfate on AD (OR = 0.60, 95% CI: 0.48-0.75) and glycoproteins on VaD (OR = 0.89, 95% CI: 0.83-0.95). Bayesian model averaging MR (MR-BMA) analysis was further conducted to prioritize the predominant metabolites for dementia risk, which highlighted the mean diameter of HDL particles and the concentration of very large HDL particles as the predominant protective factors against dementia. Moreover, pathway analysis identified 17 significant and 2 shared metabolic pathways. These findings provide support for the identification of promising predictive biomarkers and therapeutic targets for dementia.

Genetic prediction of blood metabolites mediating the relationship between gut microbiota and Alzheimer's disease: a Mendelian randomization study

Background

Observational studies have suggested an association between gut microbiota and Alzheimer's disease (AD); however, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive.

Purpose

To elucidate the causal relationship between gut microbiota and AD and to investigate whether blood metabolites serve as potential mediators.

Materials and methods

Univariable Mendelian randomization (UVMR) analysis was employed to assess the causal relationship between gut microbiota and AD, while multivariable MR (MVMR) was utilized to mitigate confounding factors. Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. We primarily utilized the inverse variance-weighted method to evaluate the causal relationship between exposure and outcome, and sensitivity analyses including Contamination mixture, Maximum-likelihood, Debiased inverse-variance weighted, MR-Egger, Bayesian Weighted Mendelian randomization, and MR pleiotropy residual sum and outlier were conducted to address pleiotropy.

Results

After adjustment for reverse causality and MVMR correction, class Actinobacteria (OR: 1.03, 95% CI: 1.01-1.06, p = 0.006), family Lactobacillaceae (OR: 1.03, 95% CI: 1.00-1.05, p = 0.017), genus Lachnoclostridium (OR: 1.03, 95% CI: 1.00-1.06, p = 0.019), genus Ruminiclostridium9 (OR: 0.97, 95% CI: 0.94-1.00, p = 0.027) and genus Ruminiclostridium6 (OR: 1.03, 95% CI: 1.01-1.05, p = 0.009) exhibited causal effects on AD. Moreover, 1-ribosyl-imidazoleacetate levels (-6.62%), Metabolonic lactone sulfate levels (2.90%), and Nonadecanoate (19:0) levels (-12.17%) mediated the total genetic predictive effects of class Actinobacteria on AD risk. Similarly, 2-stearoyl-GPE (18:0) levels (-9.87%), Octadecanedioylcarnitine (C18-DC) levels (4.44%), 1-(1-enyl-stearoyl)-2-oleoyl-GPE (p-18:0/18:1) levels (38.66%), and X-23639 levels (13.28%) respectively mediated the total genetic predictive effects of family Lactobacillaceae on AD risk. Furthermore, Hexadecanedioate (C16-DC) levels (5.45%) mediated the total genetic predictive effects of genus Ruminiclostridium 6 on AD risk; Indole-3-carboxylate levels (13.91%), X-13431 levels (7.08%), Alpha-ketoglutarate to succinate ratio (-13.91%), 3-phosphoglycerate to glycerate ratio (15.27%), and Succinate to proline ratio (-14.64%) respectively mediated the total genetic predictive effects of genus Ruminiclostridium 9 on AD risk.

Conclusion

Our mediation MR analysis provides genetic evidence suggesting the potential mediating role of blood metabolites in the causal relationship between gut microbiota and AD. Further large-scale randomized controlled trials are warranted to validate the role of blood metabolites in the specific mechanisms by which gut microbiota influence AD.

Genetic overlap and Mendelian randomization analysis highlighted the causal relationship between psoriatic disease and migraine

Despite observational studies suggesting a link between psoriatic disease (including psoriasis and psoriatic arthritis) and migraine, it is unclear whether there is a shared genetic etiology or a causal relationship between the two conditions. We aimed to reveal the genetic overlap and causality using the Mendelian randomization (MR) framework. The genetic analysis utilized summary data from the most extensive European genome-wide association study (GWAS) of migraine. Well-powered psoriatic disease GWAS data were obtained from two independent cohort studies, which served as discovery and validation datasets. Global and regional genetic correlations between psoriatic disease and migraine were assessed, and pleiotropic regions identified by pairwise GWAS analysis were further annotated. We further applied a two-sample MR multivariate MR to investigate the potential causal relationship between them. The global genetic correlation test indicated weak correlations between psoriatic disease and migraine, while regional correlation analyses delineated one significant shared locus between psoriasis and migraine. Pathway enrichment analysis revealed that shared genes were involved biological processes to the major histocompatibility and antigen processing and presentation. In terms of causality estimates, genetically predicted psoriasis (Pmeta = 0.003) and psoriatic arthritis (Pmeta = 0.028) were associated with an increased risk of migraine. Multivariate MR analysis indicated that psoriasis was an independent risk factor for migraine (P < 0.05). No significant associations were found in the reverse direction. Our study supported the causal role of psoriasis on migraine, and the central role for immunomodulatory etiology. These findings have significant implications for the management of migraine and clinical practice in patients with psoriasis.

Causal relationship between systemic lupus erythematosus and adverse pregnancy outcomes: A two-sample Mendelian randomized study

Background

Systemic lupus erythematosus (SLE) is associated with adverse pregnancy outcome (APO). However, the genetic causality of this association remains unclear. In this study, Mendelian randomization (MR) was used to explore the potential causal relationship between SLE and APO risk.

Methods

We selected 45 single nucleotide polymorphisms (SNPs) associated with SLE from published genome-wide association studies (GWAS). APO's statistics are obtained from the GWAS database. MR estimates were performed using the inverse variance-weighted (IVW) method, the MR-Egger method, and the weighted median (WM) method. Sensitivity analysis was performed using Cochran's Q test, MR-Egger intercept, MR-pleiotropic residual and outlier method, stay-one analysis and funnel plot.

Results

The results showed a causal relationship between SLE and pre-eclampsia (OR = 1.036, 95 % confidence interval 1.006 to 1.068, P = 0.019), and no significant causal relationship was found between SLE and other adverse pregnancy outcomes, including postpartum hemorrhage, placental abruption, spontaneous abortion, premature rupture of membranes, fetal distress, gestational diabetes mellitus. These findings were robust in several sensitivity analyses.

Conclusion

This MR study demonstrated the causal effect of SLE on preeclampsia. It provides important clues for identifying and early predicting risk factors for preeclampsia.

Multi-omics insight into the metabolic and cellular characteristics in the pathogenesis of hypothyroidism

While circulating metabolites and immune system have been increasingly linked to hypothyroidism risk, the causality underlying these associations remains largely uninterrogated. We used Mendelian randomization to identified putative causal traits for hypothyroidism via integrating omics data. Briefly, we utilized 1180 plasma metabolites and 731 immune cells traits as exposures to identify putatively causal traits for hypothyroidism in the discovery (40,926 cases) and replication cohorts (14,871 cases). By combining MR results from two large-scale cohorts, we ultimately identified 21 putatively causal traits, including five plasma metabolites and 16 immune cell traits. CD3 on CD28+ CD4+ T cell and 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (p-16:0/18:1) demonstrated the most pronounced positive and negative associations with hypothyroidism risk, respectively. The odds ratio and 95% confidence interval were 1.09 (1.07, 1.12) and 0.81 (0.75, 0.87), respectively. No evidence of horizontal pleiotropy, heterogeneity among instrumental variables or reverse causation were found for these 21 significant associations. Our study elucidates key metabolites and immune cell traits associated with hypothyroidism. These findings provide new insights into the etiology and potential therapeutic targets for hypothyroidism.

Relationship between circulating white blood cell count and inflammatory skin disease: a bidirectional mendelian randomization study

Observational studies have shown a strong association between circulating white blood cell counts (WBC) and inflammatory skin diseases such as acne and psoriasis. However, the causal nature of this relationship is unclear. We performed a two-way two-sample Mendelian randomization (MR) analysis to investigate potential causal relationships between leukocytes and inflammatory skin diseases. The circulating white blood cell count, basophil cell count, leukocyte cell count, lymphocyte cell count, eosinophil cell count, and neutrophil cell count data were obtained from the Blood Cell Consortium (BCX). The data for inflammatory skin disorders, including acne, atopic dermatitis (AD), hidradenitis suppurativa (HS), psoriasis, and seborrheic dermatitis (SD), were obtained from the FinnGen Consortium R10. The primary analysis utilized inverse variance weighting (IVW) along with additional methods such as MR-Egger, weighted mode, and weighted median estimator. To assess heterogeneity among instrument variables, Cochran's Q test was employed, while MR-Egger intercept and MR-PRESSO were used to test for horizontal pleiotropy. IVW demonstrated that an elevated monocyte count was significantly associated with a decreased risk of psoriasis (OR = 0.897, 95% CI: 0.841-0.957, P = 0.001, FDR = 0.016). Additionally, an increased eosinophil count was causally associated with a higher risk of AD (OR = 1.188, 95% CI: 1.093-1.293, P = 0.000, FDR = 0.002). No inverse causal relationship between inflammatory skin disease and circulating white blood cell count was found. In conclusion, this study provides evidence that increased monocyte count is associated with a reduced risk of psoriasis and that there is a causal relationship between increased eosinophil counts and an increased risk of AD. These findings help us understand the potential causal role of specific white blood cell counts in the development of inflammatory skin diseases.

Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach

Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR) = 1.24, p = 3.88 × 10-12; SZ OR = 1.09, p = 2.44 × 10-20). However, while the effect of mental distress (OR = 1.17, p = 1.02 × 10-4) and risk-taking (OR = 1.52, p = 0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.

Investigating the Bidirectional Association of Rheumatoid Arthritis and Thyroid Function: A Methodologic Assessment of Mendelian Randomization

OBJECTIVE

Rheumatoid arthritis (RA) and thyroid dysfunction are frequently observed in the same patient. However, whether they co-occur or exhibit a causal relationship remains uncertain. We aimed to systematically investigate the causal relationship between RA and thyroid function using a large sample and advanced methods.

METHODS

Bidirectional two-sample Mendelian randomization (MR) analysis was performed based on RA and six thyroid function trait data sets from the European population. The robustness of the results was demonstrated using multiple MR methods and a series of sensitivity analyses. Multivariable MR using Bayesian model averaging (MR-BMA) was performed to adjust for possible competing risk factors. A sensitivity data set, which included data from patients with seropositive RA and controls, was used to repeat the analyses. Furthermore, enrichment analysis was employed to discover the underlying mechanism between RA and thyroid functions.

RESULTS

A significantly positive causal effect was identified for RA on autoimmune thyroid disease (AITD) as well as for AITD on RA (P < 0.001). Further sensitivity analyses showed consistent causal estimates from a variety of MR methods. After removing the outliers, MR-BMA results showed that RA and AITD were independent risk factors in their bidirectional causality, even in the presence of other competing risk factors (adjusted P < 0.05). Enrichment analysis showed immune cell activation and immune response play crucial roles in them.

CONCLUSION

Our results illustrate the significant bidirectional causal effect of RA and AITD, which holds even in multiple competing risk factors. Clinical screening for thyroid dysfunction in patients with RA deserves further attention, and vice versa.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

Casual relationships between circulating metabolites and rheumatoid arthritis: A mendelian randomization study

Background

Blood metabolites serve as pivotal indicators in identifying and predicting the course of rheumatoid arthritis (RA). However, empirical substantiation of a direct causal link between these serum biomarkers and the development of RA is still lacking comprehensive support.

Method

In pursuit of a thorough exploration of the causal links between circulating blood metabolites and RA, we deployed a two-sample Mendelian randomization (MR) approach during our initial investigative phase. This method was utilized to examine the potential connections between 249 distinct circulating metabolites and the prevalence of RA. In the validation phase, we conducted replication analyses with a new metabolic dataset consisting of 123 metabolites. Furthermore, we employed the Mendelian randomization based on Bayesian model averaging (MR-BMA) technique to pinpoint key metabolic characteristics that have significant causal implications.

Results

In our primary analysis, we found that acetate, acetoacetate and pyruvate exhibited a consistent protective causal association with rheumatoid arthritis, while lactate demonstrated a positive correlation with rheumatoid arthritis risk. It is also noteworthy that a substantial subset of traits related to both saturated and unsaturated fatty acids showed causal influences. Subsequent secondary analyses substantiated these observations, revealing that traits associated with the average number of methylene groups in a fatty acid chain exhibited protective effects. Ultimately, our MR-BMA analyses unveiled that the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids assumes a paramount role in increasing the susceptibility to rheumatoid arthritis.

Conclusions

By employing systemic MR analyses, our study has successfully generated an all-encompassing atlas elucidating the intricate connections between circulating metabolites and the susceptibility to rheumatoid arthritis. Our results indicate the high unsaturation degree is a dominant risk factors correlated with rheumatoid arthritis.

Mendelian Randomization Analysis of Systemic Iron Status and Risk of Different Types of Kidney Disease

With rapid increases in incidence, diverse subtypes, and complicated etiologies, kidney disease remains a global public health problem. Iron, as an essential trace element, has pleiotropic effects on renal function and the progression of kidney diseases. A two-sample Mendelian randomization (MR) analysis was implemented to determine the potential causal effects between systemic iron status on different kidney diseases. Systemic iron status was represented by four iron-related biomarkers: serum iron, ferritin, transferrin saturation (TfSat), and total iron binding capacity (TIBC). For systemic iron status, 163,511, 246,139, 131,471, and 135,430 individuals were included in the genome-wide association study (GWAS) of serum iron, ferritin, TfSat, and TIBC, respectively. For kidney diseases, 653,143 individuals (15,658 cases and 637,485 controls), 657,076 individuals (8160 cases and 648,916 controls), and 659,320 individuals (10,404 cases and 648,916 controls) were included for immunoglobulin A nephropathy (IgAN), acute kidney disease (AKD), and chronic kidney disease (CKD), respectively. Our MR results showed that increased serum iron [odds ratio (OR): 1.10; 95% confidence interval (95% CI): 1.04, 1.16; p < 0.0042], ferritin (OR: 1.30; 95% CI: 1.14, 1.48; p < 0.0042), and TfSat (OR: 1.07; 95% CI: 1.04, 1.11; p < 0.0042)] and decreased TIBC (OR: 0.92; 95% CI: 0.88, 0.97; p < 0.0042) were associated with elevated IgAN risk. However, no significant associations were found between systemic iron status and AKD or CKD. In our MR study, the genetic evidence supports elevated systemic iron status as a causal effect on IgAN, which suggests a potential protective effect of iron chelation on IgAN patients.

Mendelian randomization and Bayesian model averaging of autoimmune diseases and Long COVID

Background

Following COVID-19, reports suggest Long COVID and autoimmune diseases (AIDs) in infected individuals. However, bidirectional causal effects between Long COVID and AIDs, which may help to prevent diseases, have not been fully investigated.

Methods

Summary-level data from genome-wide association studies (GWAS) of Long COVID (N = 52615) and AIDs including inflammatory bowel disease (IBD) (N = 377277), Crohn's disease (CD) (N = 361508), ulcerative colitis (UC) (N = 376564), etc. were employed. Bidirectional causal effects were gauged between AIDs and Long COVID by exploiting Mendelian randomization (MR) and Bayesian model averaging (BMA).

Results

The evidence of causal effects of IBD (OR = 1.06, 95% CI = 1.00-1.11, p = 3.13E-02), CD (OR = 1.10, 95% CI = 1.01-1.19, p = 2.21E-02) and UC (OR = 1.08, 95% CI = 1.03-1.13, p = 2.35E-03) on Long COVID was found. In MR-BMA, UC was estimated as the highest-ranked causal factor (MIP = 0.488, MACE = 0.035), followed by IBD and CD.

Conclusion

This MR study found that IBD, CD and UC had causal effects on Long COVID, which suggests a necessity to screen high-risk populations.

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments

BACKGROUND

Symptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity.

METHODS

Using data from the UK Biobank (n = 41,948-109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms.

RESULTS

Corresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences.

CONCLUSIONS

MDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

Dietary factors and the risk of atopic dermatitis: a Mendelian randomisation study

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

Estimating causality between smoking and abdominal obesity by Mendelian randomization

BACKGROUND AND AIMS

Smokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether or not the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization (MR) studies have investigated this relationship by relying upon a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to estimate causality between smoking and abdominal obesity using multiple genetic instruments.

DESIGN

MR study using causal analysis using summary effect estimates (CAUSE) and latent heritable confounder MR (LHC-MR) methods that instrument smoking using genome-wide data, and also two-sample MR (2SMR) methods.

SETTING

Genome-wide association studies (GWAS) summary statistics from participants of European ancestry, obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetic Investigation of Anthropometric Traits (GIANT) Consortium and the UK Biobank.

PARTICIPANTS

We used GWAS results for smoking initiation (n = 1 232 091), life-time smoking (n = 462 690) and smoking heaviness (n = 337 334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697 734), with and without adjustment for body mass index (adjBMI), as outcome traits.

MEASUREMENTS

Smoking initiation, life-time smoking, smoking heaviness, WHR, WC, HC, WHRadjBMI, WCadjBMI and HCadjBMI.

FINDINGS

Both CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95% confidence interval (CI) = 0.10, 0.16 and 0.49 (0.41, 0.57), respectively] and WHRadjBMI (0.07 (0.03, 0.10) and 0.31 (0.26, 0.37). Similarly, they indicated a positive causal effect of life-time smoking on WHR [0.35 (0.29, 0.41) and 0.44 (0.38, 0.51)] and WHRadjBMI [0.18 (0.13, 0.24) and 0.26 (0.20, 0.31)]. In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones.

CONCLUSIONS

Smoking initiation and higher life-time smoking may lead to increased abdominal fat distribution. The increase in abdominal fat due to smoking is characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.

Causal association of blood cell traits with inflammatory bowel diseases: a Mendelian randomization study

Background

Observational studies have found associations between blood cell traits and inflammatory bowel diseases (IBDs), whereas the causality and dose-effect relationships are still undetermined.

Methods

Two-sample Mendelian randomization (MR) analyses using linear regression approaches, as well as Bayesian model averaging (MR-BMA), were conducted to identify and prioritize the causal blood cell traits for Crohn's disease (CD) and ulcerative colitis (UC). An observational study was also performed using restricted cubic spline (RCS) to explore the relationship between important blood cell traits and IBDs.

Results

Our uvMR analysis using the random effects inverse variance weighted (IVW) method identified eosinophil (EOS) as a causal factor for UC (OR = 1.36; 95% CI: 1.13, 1.63). Our MR-BMA analysis further prioritized that high level of lymphocyte (LYM) decreased CD risk (MIP = 0.307; θ ^ MACE = -0.059; PP = 0.189; θ ^ λ = -0.173), whereas high level of EOS increased UC risk (MIP = 0.824; θ ^ MACE = 0.198; PP = 0.627; θ ^ λ = 0.239). Furthermore, the observational study clearly depicts the nonlinear relationship between important blood cell traits and the risk of IBDs.

Conclusion

Using MR approaches, several blood cell traits were identified as risk factors of CD and UC, which could be used as potential targets for the management of IBDs. Stratified genome-wide association studies (GWASs) based on the concentration of traits would be helpful owing to the nonlinear relationships between blood cell traits and IBDs, as demonstrated in our clinical observational study. Together, these findings could shed light on the clinical strategies applied to the management of CD and UC.

Causal effect of gut microbiota and diabetic nephropathy: a Mendelian randomization study

BACKGROUND

The interaction of dysbiosis of gut microbiota (GM) with diabetic nephropathy (DN) drew our attention and a better understanding of GM on DN might provide potential therapeutic approaches. However, the exact causal effect of GM on DN remains unknown.

METHODS

We applied two-sample Mendelian Randomization (MR) analysis, including inverse variance weighted (IVW), MR-Egger methods, etc., to screen the significant bacterial taxa based on the GWAS data. Sensitivity analysis was conducted to assess the robustness of MR results. To identify the most critical factor on DN, Mendelian randomization-Bayesian model averaging (MR-BMA) method was utilized. Then, whether the reverse causality existed was verified by reverse MR analysis. Finally, transcriptome MR analysis was performed to investigate the possible mechanism of GM on DN.

RESULTS

At locus-wide significance levels, the results of IVW suggested that order Bacteroidales (odds ratio (OR) = 1.412, 95% confidence interval (CI): 1.025-1.945, P = 0.035), genus Akkermansia (OR = 1.449, 95% CI: 1.120-1.875, P = 0.005), genus Coprococcus 1 (OR = 1.328, 95% CI: 1.066-1.793, P = 0.015), genus Marvinbryantia (OR = 1.353, 95% CI: 1.037-1.777, P = 0.030) and genus Parasutterella (OR = 1.276, 95% CI: 1.022-1.593, P = 0.032) were risk factors for DN. Reversely, genus Eubacterium ventriosum (OR = 0.756, 95% CI: 0.594-0.963, P = 0.023), genus Ruminococcus gauvreauii (OR = 0.663, 95% CI: 0.506-0.870, P = 0.003) and genus Erysipelotrichaceae (UCG003) (OR = 0.801, 95% CI: 0.644-0.997, P = 0.047) were negatively associated with the risk of DN. Among these taxa, genus Ruminococcus gauvreauii played a crucial role in DN. No significant heterogeneity or pleiotropy in the MR result was found. Mapped genes (FDR < 0.05) related to GM had causal effects on DN, while FCGR2B and VNN2 might be potential therapeutic targets.

CONCLUSIONS

This work provided new evidence for the causal effect of GM on DN occurrence and potential biomarkers for DN. The significant bacterial taxa in our study provided new insights for the 'gut-kidney' axis, as well as unconventional prevention and treatment strategies for DN.

Blood lipids mediate the effects of gut microbiome on endometriosis: a mendelian randomization study

BACKGROUND

There is evidence for an association between the gut microbiome and endometriosis. However, their causal relationship and the mediating role of lipid metabolism remain unclear.

METHODS

Using genome-wide association study (GWAS) data, we conducted a bidirectional Mendelian randomization (MR) analysis to investigate the causal relationships between gut microbiome and endometriosis. The inverse variance weighted (IVW) method was used as the primary model, with other MR models used for comparison. Sensitivity analysis based on different statistical assumptions was used to evaluate whether the results were robust. A two-step MR analysis was further conducted to explore the mediating effects of lipids, by integrating univariable MR and the multivariate MR method based on the Bayesian model averaging method (MR-BMA).

RESULTS

We identified four possible intestinal bacteria genera associated with the risk of endometriosis through the IVW method, including Eubacterium ruminantium group (odds ratio [OR] = 0.881, 95% CI: 0.795-0.976, P = 0.015), Anaerotruncus (OR = 1.252, 95% CI: 1.028-1.525, P = 0.025), Olsenella (OR = 1.110, 95% CI: 1.007-1.223, P = 0.036), and Oscillospira (OR = 1.215, 95% CI: 1.014-1.456, P = 0.035). The further two-step MR analysis identified that the effect of Olsenella on endometriosis was mediated by triglycerides (proportion mediated: 3.3%; 95% CI = 1.5-5.1%).

CONCLUSION

This MR study found evidence for specific gut microbiomes associated with the risk of endometriosis, which might partially be mediated by triglycerides.

The impact of lipidome on breast cancer: a Mendelian randomization study

OBJECTIVE

This study aims to investigate the association between specific lipidomes and the risk of breast cancer (BC) using the Two-Sample Mendelian Randomization (TSMR) approach and Bayesian Model Averaging Mendelian Randomization (BMA-MR) method.

METHOD

The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran's Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study.

RESULTS

By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916-0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894-0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER- BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034-1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091-1.119, P < 0.05) was an important causal effect in ER-BC.

CONCLUSIONS

The findings reveal that phosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC's intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.

Association between body fat distribution and age at menarche: a two sample Mendelian randomization study

Background

Numerous studies have examined the association between obesity and age at menarche (AAM), with most focusing on traditional obesity indicators such as body mass index. However, there are limited studies that explored the connection between body fat distribution and AAM, as well as a scarcity of Mendelian randomization (MR) studies.

Methods

In this study, we conducted a two-sample MR study to evaluate the causal effects of eight body fat distribution indicators on AAM. Inverse variance weighted (IVW) method was used for primary analysis, while supplementary approaches such as MR-Egger and weighted median were also utilized. Considering that the eight exposures were highly correlated, we performed an MR Bayesian model averaging (MR-BMA) analysis to prioritize the effect of major exposure on AAM. A series of sensitivity analyses were also performed.

Results

From a range of 82-105 single nucleotide polymorphisms (SNPs) were utilized as genetic instrumental variables for each of the exposure factors. After Bonferroni correction, we found that whole body fat mass (β: -0.17; 95% CI: -0.24, -0.11), left leg fat percentage (β: -0.14; 95% CI: -0.21, -0.07), left leg fat mass (β: -0.20; 95% CI: -0.27, -0.12), left arm fat percentage (β: -0.18; 95% CI: -0.26, -0.11) and left arm fat mass (β: -0.18; 95%CI: -0.26, -0.10) were associated with decreased AAM using random effects IVW method. And the beta coefficients for all MR evaluation methods exhibited consistent trends. MR-BMA method validated that left arm fat percentage plays a dominant role in AAM.

Conclusions

Our MR study suggested that body fat has broad impacts on AAM. Obtaining more information on body measurements would greatly enhance our comprehension of pubertal development.

No genetic causality between obesity and benign paroxysmal vertigo: A two-sample Mendelian randomization study

Objective

We applied Mendelian randomization to explore the causal relationship between obesity and benign paroxysmal vertigo (BPV).

Methods

We chose two types of obesity diseases. Obesity due to excessive calories and other or unspecified obesity from the FinnGen database. We used genomic significance (p < 5 × 10-8) to obtain independent single nucleotide polymorphisms (SNPs) as instrumental variables. Similarly, genome-wide association study data for the disease BPV were selected from the FinnGen database. R was then used to test the data for multiplicity and heterogeneity, as well as to detect the effect of individual SNPs on the results. Random effects inverse variance weighting was used as the main statistical analysis.

Results

First, by analyzing, we found an outlier in obesity due to excessive calories (rs12956821). Outliers were then removed, and the statistical results were analyzed without heterogeneity (p > 0.05) and horizontal pleiotropy (p > 0.05), as well as individual SNPs having no effect on the results. Meanwhile, random-effects IVW results showed obesity due to excessive calories (p = 0.481; OR = 0.941), and other or unspecified obesity (p = 0.640; OR = 0.964).

Conclusions

The present study did not find a causal relationship between the above two obesity types and BPV at the genetic level.

Sodium-glucose cotransporter 2 inhibitors, inflammation, and heart failure: a two-sample Mendelian randomization study

BACKGROUND

Sodium-glucose cotransporter 2 (SGLT-2) inhibitors are increasingly recognized for their role in reducing the risk and improving the prognosis of heart failure (HF). However, the precise mechanisms involved remain to be fully delineated. Evidence points to their potential anti-inflammatory pathway in mitigating the risk of HF.

METHODS

A two-sample, two-step Mendelian Randomization (MR) approach was employed to assess the correlation between SGLT-2 inhibition and HF, along with the mediating effects of inflammatory biomarkers in this relationship. MR is an analytical methodology that leverages single nucleotide polymorphisms as instrumental variables to infer potential causal inferences between exposures and outcomes within observational data frameworks. Genetic variants correlated with the expression of the SLC5A2 gene and glycated hemoglobin levels (HbA1c) were selected using datasets from the Genotype-Tissue Expression project and the eQTLGen consortium. The Genome-wide association study (GWAS) data for 92 inflammatory biomarkers were obtained from two datasets, which included 14,824 and 575,531 individuals of European ancestry, respectively. GWAS data for HF was derived from a meta-analysis that combined 26 cohorts, including 47,309 HF cases and 930,014 controls. Odds ratios (ORs) and 95% confidence interval (CI) for HF were calculated per 1 unit change of HbA1c.

RESULTS

Genetically predicted SGLT-2 inhibition was associated with a reduced risk of HF (OR 0.42 [95% CI 0.30-0.59], P < 0.0001). Of the 92 inflammatory biomarkers studied, two inflammatory biomarkers (C-X-C motif chemokine ligand 10 [CXCL10] and leukemia inhibitory factor) were associated with both SGLT-2 inhibition and HF. Multivariable MR analysis revealed that CXCL10 was the primary inflammatory cytokine related to HF (MIP = 0.861, MACE = 0.224, FDR-adjusted P = 0.0844). The effect of SGLT-2 inhibition on HF was mediated by CXCL10 by 17.85% of the total effect (95% CI [3.03%-32.68%], P = 0.0183).

CONCLUSIONS

This study provides genetic evidence supporting the anti-inflammatory effects of SGLT-2 inhibitors and their beneficial impact in reducing the risk of HF. CXCL10 emerged as a potential mediator, offering a novel intervention pathway for HF treatment.

Causal associations between circulating cytokines and risk of sepsis and related outcomes: a two-sample Mendelian randomization study

Introduction

Sepsis represents a critical medical condition that arises due to an imbalanced host reaction to infection. Central to its pathophysiology are cytokines. However, observational investigations that explore the interrelationships between circulating cytokines and susceptibility to sepsis frequently encounter challenges pertaining to confounding variables and reverse causality.

Methods

To elucidate the potential causal impact of cytokines on the risk of sepsis, we conducted two-sample Mendelian randomization (MR) analyses. Genetic instruments tied to circulating cytokine concentrations were sourced from genome-wide association studies encompassing 8,293 Finnish participants. We then evaluated their links with sepsis and related outcomes using summary-level data acquired from the UK Biobank, a vast multicenter cohort study involving over 500,000 European participants. Specifically, our data spanned 11,643 sepsis cases and 474,841 controls, with subsets including specific age groups, 28-day mortality, and ICU-related outcomes.

Results and Discussion

MR insights intimated that reduced genetically-predicted interleukin-10 (IL-10) levels causally correlated with a heightened sepsis risk (odds ratio [OR] 0.68, 95% confidence interval [CI] 0.52-0.90, P=0.006). An inverse relationship emerged between monocyte chemoattractant protein-1 (MCP-1) and sepsis-induced mortality. Conversely, elevated macrophage inflammatory protein 1 beta (MIP1B) concentrations were positively linked with both sepsis incidence and associated mortality. These revelations underscore the causal impact of certain circulating cytokines on sepsis susceptibility and its prognosis, hinting at the therapeutic potential of modulating these cytokine levels. Additional research is essential to corroborate these connections.

simmrd: An open-source tool to perform simulations in Mendelian randomization

Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods.

Genetic evidence strengthens the bidirectional connection between gut microbiota and Shigella infection: insights from a two-sample Mendelian randomization study

Background

In recent investigations, substantial strides have been made in the precise modulation of the gut microbiota to prevent and treat a myriad of diseases. Simultaneously, the pressing issue of widespread antibiotic resistance and multidrug resistance resulting from Shigella infections demands urgent attention. Several studies suggest that the antagonistic influence of the gut microbiota could serve as a novel avenue for impeding the colonization of pathogenic microorganisms or treating Shigella infections. However, conventional research methodologies encounter inherent challenges in identifying antagonistic microbial agents against Shigella, necessitating a comprehensive and in-depth analysis of the causal relationship between Shigella infections and the gut microbiota.

Materials and methods

Utilizing the aggregated summary statistics from Genome-Wide Association Studies (GWAS), we conducted Mendelian Randomization (MR) analyses encompassing 18,340 participants to explore the interplay between the gut microbiota and Shigella infections. This investigation also involved 83 cases of Shigella infection patients and 336,396 control subjects. In the positive strand of our findings, we initially performed a preliminary analysis using the Inverse Variance Weighting (IVW) method. Subsequently, we undertook sensitivity analyses to assess the robustness of the results, addressing confounding factors' influence. This involved employing the Leave-One-Out method and scrutinizing funnel plots to ensure the reliability of the MR analysis outcomes. Conclusively, a reverse MR analysis was carried out, employing the Wald ratio method due to the exposure of individual Single Nucleotide Polymorphisms (SNPs). This was undertaken to explore the plausible associations between Shigella infections and genetically predicted compositions of the gut microbiota.

Results

In this study, we employed 2,818 SNPs associated with 211 species of gut microbiota as instrumental variables (IVs). Through IVW analysis, our positive MR findings revealed a significant negative correlation between the occurrence of Shigella infections and the phylum Tenericutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), class Mollicutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), genus Intestinimonas (OR: 0.16, 95% CI: 0.04-0.63, p = 0.01), genus Gordonibacter (OR: 0.39, 95% CI: 0.16-0.93, p = 0.03), and genus Butyrivibrio (OR: 0.44, 95% CI: 0.23-0.87, p = 0.02). Conversely, a positive correlation was observed between the occurrence of Shigella infections and genus Sutterella (OR: 10.16, 95% CI: 1.87-55.13, p = 0.01) and genus Alistipes (OR: 12.24, 95% CI: 1.71-87.34, p = 0.01). In sensitivity analyses, utilizing MR-Egger regression analysis and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) detection, all outcomes demonstrated robust stability. Simultaneously, in the reverse MR analysis, Shigella infections resulted in an upregulation of four bacterial genera and a downregulation of three bacterial genera.

Conclusion

In summation, the MR analysis outcomes corroborate the presence of bidirectional causal relationships between the gut microbiota and Shigella infections. This study not only unveils novel perspectives for the prevention and treatment of Shigella infections but also furnishes fresh insights into the mechanistic underpinnings of how the gut microbiota contributes to the pathogenesis of Shigella infections. Consequently, the established dual causal association holds promise for advancing our understanding and addressing the complexities inherent in the interplay between the gut microbiota and Shigella infections, thereby paving the way for innovative therapeutic interventions and preventive strategies in the realm of Shigella-related diseases.

Translational bioinformatics approach to combat cardiovascular disease and cancers

Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.

Conditional transcriptome-wide association study for fine-mapping candidate causal genes

Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16-91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.

Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study

BACKGROUND

Quantitative nuclear magnetic resonance (NMR) metabolomics techniques provide detailed measurements of lipoprotein particle concentration. Metabolic dysfunction often represents a cluster of conditions, including dyslipidaemia, hypertension, and diabetes, that increase the risk of cardiovascular diseases (CVDs). However, the causal relationship between lipid profiles and blood pressure (BP) remains unclear. We performed a Mendelian Randomisation (MR) study to disentangle and prioritize the potential causal effects of major lipids, lipoprotein particles, and circulating metabolites on BP and pulse pressure (PP).

METHODS

We employed single-nucleotide polymorphisms (SNPs) associated with major lipids, lipoprotein particles, and other metabolites from the UK Biobank as instrumental variables. Summary-level data for BP and PP were obtained from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Two-sample MR and MR Bayesian model averaging approaches (MR-BMA) were conducted to analyse and rank causal associations.

FINDINGS

Genetically predicted TG was the most likely causal exposure among the major lipids to increase systolic blood pressure (SBP) and diastolic blood pressure (DBP), with marginal inclusion probabilities (MIPs) of 0.993 and 0.847, respectively. Among the majority of lipoproteins and their containing lipids, including major lipids, genetically elevated TG in small high-density lipoproteins (S_HDL_TG) had the strongest association with the increase of SBP and DBP, with MIPs of 0.416 and 0.397, respectively. HDL cholesterol (HDL_C) and low-density lipoprotein cholesterol (LDL_C) were potential causal factors for PP elevation among the major lipids (MIP = 0.927 for HDL_C and MIP = 0.718 for LDL_C). Within the sub-lipoproteins, genetically predicted atherogenic lipoprotein particles (i.e., sub-very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and LDL particles) had the most likely causal impact on increasing PP.

INTERPRETATION

This study provides genetic evidence for the causality of lipids on BP indicators. However, the effect size on SBP, DBP, and PP varies depending on the lipids' components and sizes. Understanding this potential relationship may inform the potential benefits of comprehensive management of lipid profiles for BP control.

FUNDING

Key Research and Development Program of Hubei Province, Science and Technology Innovation Project of Huanggang Central Hospital of Yangtze University, the Hubei Industrial Technology Research Institute of Heart-Brain Diseases, and the Hubei Provincial Engineering Research Centre of Comprehensive Care for Heart-Brain Diseases.

Gut microbiota in combination with blood metabolites reveals characteristics of the disease cluster of coronary artery disease and cognitive impairment: a Mendelian randomization study

Background

The coexistence of coronary artery disease (CAD) and cognitive impairment has become a common clinical phenomenon. However, there is currently limited research on the etiology of this disease cluster, discovery of biomarkers, and identification of precise intervention targets.

Methods

We explored the causal connections between gut microbiota, blood metabolites, and the disease cluster of CAD combined with cognitive impairment through two-sample Mendelian randomization (TSMR). Additionally, we determine the gut microbiota and blood metabolites with the strongest causal associations using Bayesian model averaging multivariate Mendelian randomization (MR-BMA) analysis. Furthermore, we will investigate the mediating role of blood metabolites through a two-step Mendelian randomization design.

Results

We identified gut microbiota that had significant causal associations with cognitive impairment. Additionally, we also discovered blood metabolites that exhibited significant causal associations with both CAD and cognitive impairment. According to the MR-BMA results, the free cholesterol to total lipids ratio in large very low density lipoprotein (VLDL) was identified as the key blood metabolite significantly associated with CAD. Similarly, the cholesteryl esters to total lipids ratio in small VLDL emerged as the primary blood metabolite with a significant causal association with dementia with lewy bodies (DLB). For the two-step Mendelian randomization analysis, we identified blood metabolites that could potentially mediate the association between genus Butyricicoccus and CAD in the potential causal links.

Conclusion

Our study utilized Mendelian randomization (MR) to identify the gut microbiota features and blood metabolites characteristics associated with the disease cluster of CAD combined with cognitive impairment. These findings will provide a meaningful reference for the identification of biomarkers for the disease cluster of CAD combined with cognitive impairment as well as the discovery of targets for intervention to address the problems in the clinic.

A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

Causal Effects of Blood Metabolites and Obstructive Sleep Apnea: A Mendelian Randomization Study

BACKGROUND

Obstructive sleep apnea (OSA) is one of the most common forms of sleep-disordered breathing. Studies have shown that certain changes in metabolism play an important role in the pathophysiology of OSA. However, the causal relationship between these metabolites and OSA remains unclear.

AIMS

We use a mendelian randomization (MR) approach to investigate the causal associations between the genetic liability to metabolites and OSA.

METHODS

We performed a 2-sample inverse-variance weighted mendelian randomization analysis to evaluate the causal effects of genetically determined 486 metabolites on OSA. Multiple sensitivity analyses were performed to assess pleiotropy. We used multivariate mendelian randomization analyses to assess confounding factors and mendelian randomization Bayesian model averaging to rank the significant biomarkers by their genetic evidence. We also conducted a metabolic pathway analysis to identify potential metabolic pathways.

RESULTS

We identified 14 known serum metabolites (8 risk factors and 6 protective factors) and 12 unknown serum metabolites associated with OSA. These 14 known metabolites included 8 lipids( 1-arachidonoylglycerophosphoethanolamine, Tetradecanedioate, Epiandrosteronesulfate, Acetylca Glycerol3-phosphate, 3-dehydrocarnitine, Margarate17:0, Docosapentaenoaten3;22:5n3), 3 Aminoacids (Isovalerylcarnitine,3-methyl-2-oxobutyrate,Methionine), 2 Cofactors and vitamins [Bilirubin(E,ZorZ,E),X-11593--O-methylascorbate], 1Carbohydrate(1,6-anhydroglucose). We also identified several metabolic pathways that involved in the pathogenesis of OSA.

CONCLUSION

MR (mendelian randomization) approach was performed to identify 6 protective factors and 12 risk factors for OSA in the present study. 3-Dehydrocarnitine was the most significant risk factors for OSA. Our study also confirmed several significant metabolic pathways that were involved in the pathogenesis of OSA. Valine, leucine and isoleucine biosynthesis metabolic pathways were the most significant metabolic pathways that were involved in the pathogenesis of OSA.

Causal effects of potential risk factors on postpartum depression: a Mendelian randomization study

Background

Postpartum depression (PPD) is a type of depressive episode related to parents after childbirth, which causes a variety of symptoms not only for parents but also affects the development of children. The causal relationship between potential risk factors and PPD remains comprehensively elucidated.

Methods

Linkage disequilibrium score regression (LDSC) analysis was conducted to screen the heritability of each instrumental variant (IV) and to calculate the genetic correlations between effective causal factors and PPD. To search for the causal effect of multiple potential risk factors on the incidence of PPD, random effects of the inverse variance weighted (IVW) method were applied. Sensitivity analyses, including weighted median, MR-Egger regression, Cochrane's Q test, and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO), were performed to detect potential Mendelian randomization (MR) assumption violations. Multivariable MR (MVMR) was conducted to control potential multicollinearity.

Results

A total of 40 potential risk factors were investigated in this study. LDSC regression analysis reported a significant genetic correlation of potential traits with PPD. MR analysis showed that higher body mass index (BMI) (Benjamini and Hochberg (BH) corrected p = 0.05), major depression (MD) (BH corrected p = 5.04E-19), and schizophrenia (SCZ) (BH corrected p = 1.64E-05) were associated with the increased risk of PPD, whereas increased age at first birth (BH corrected p = 2.11E-04), older age at first sexual intercourse (BH corrected p = 3.02E-15), increased average total household income before tax (BH corrected p = 4.57E-02), and increased years of schooling (BH corrected p = 1.47E-11) led to a decreased probability of PPD. MVMR analysis suggested that MD (p = 3.25E-08) and older age at first birth (p = 8.18E-04) were still associated with an increased risk of PPD.

Conclusion

In our MR study, we found multiple risk factors, including MD and younger age at first birth, to be deleterious causal risk factors for PPD.

An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset

Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.

Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target

Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor β signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model.

Integrating plasma proteomes with genome-wide association data for causal protein identification in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a severely debilitating and fatal B-cell neoplastic disease. The discovery of disease-associated proteins with causal genetic evidence offers a chance to uncover novel therapeutic targets.

METHODS

First, we comprehensively investigated the causal association between 2994 proteins and MM through two-sample mendelian randomization (MR) analysis using summary-level data from public genome-wide association studies of plasma proteome (N = 3301 healthy individuals) and MM (598 cases and 180,756 controls). Sensitivity analyses were performed for these identified causal proteins. Furthermore, we pursued the exploration of enriched biological pathways, prioritized the therapeutic proteins, and evaluated their druggability using the KEGG pathway analysis, MR-Bayesian model averaging analysis, and cross-reference with current databases, respectively.

RESULTS

We identified 13 proteins causally associated with MM risk (false discovery rate corrected P < 0.05). Six proteins were positively associated with the risk of MM, including nicotinamide phosphoribosyl transferase (NAMPT; OR [95% CI]: 1.35 [1.18, 1.55]), tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1; 1.14 [1.06, 1.22]), neutrophil cytosol factor 2 (NCF2; 1.27 [1.12, 1.44]), carbonyl reductase 1, cAMP-specific 3',5'-cyclic phosphodiesterase 4D (PDE4D), platelet-activating factor acetylhydrolase IB subunit beta (PAFAH1B2). Seven proteins were inversely associated with MM, which referred to suppressor of cytokine signaling 3 (SOCS3; 0.90 [0.86, 0.94]), Fc-gamma receptor III-B (FCGR3B; 0.75 [0.65,0.86]), glypican-1 (GPC1; 0.69 [0.58,0.83]), follistatin-related protein 1, protein tyrosine phosphatase non-receptor type 4 (PTPN4), granzyme B, complement C1q subcomponent subunit C (C1QC). Three of the causal proteins, SOCS3, FCGR3B, and NCF2, were enriched in the osteoclast differentiation pathway in KEGG enrichment analyses while GPC1 (marginal inclusion probability (MIP):0.993; model averaged causal effects (MACE): - 0.349), NAMPT (MIP:0.433; MACE: - 0.113), and NCF2 (MIP:0.324; MACE:0.066) ranked among the top three MM-associated proteins according to MR-BMA analyses. Furthermore, therapeutics targeting four proteins are currently under evaluation, five are druggable and four are future breakthrough points.

CONCLUSIONS

Our analysis revealed a set of 13 novel proteins, including six risk and seven protective proteins, causally linked to MM risk. The discovery of these MM-associated proteins opens up the possibility for identifying novel therapeutic targets, further advancing the integration of genome and proteome data for drug development.

Genetic evidence strengthens the bidirectional connection between gut microbiota and periodontitis: insights from a two-sample Mendelian randomization study

BACKGROUND

Recent research has established the correlation between gut microbiota and periodontitis via oral-gut axis. Intestinal dysbiosis may play a pivotal bridging role in extra-oral inflammatory comorbidities caused by periodontitis. However, it is unclear whether the link is merely correlative or orchestrated by causative mechanistic interactions. This two-sample Mendelian randomization (MR) study was performed to evaluate the potential bidirectional causal relationships between gut microbiota and periodontitis.

MATERIALS AND METHODS

A two-sample MR analysis was performed using summary statistics from genome-wide association studies (GWAS) for gut microbiota (n = 18,340) and periodontitis (cases = 12,251; controls = 22,845). The inverse-variance weighted (IVW) method was used for the primary analysis, and we employed sensitivity analyses to assess the robustness of the main results. The PhenoScanner database was then searched for pleiotropy SNPs associated with potential confounders. In order to identify the possibly influential SNPs, we further conducted the leave-one-out analysis. Finally, a reverse MR analysis was performed to evaluate the possibility of links between periodontitis and genetically predicted gut microbiota alternation.

RESULTS

2,699 single nucleotide polymorphisms (SNPs) associated with 196 microbiota genera were selected as instrumental variables (IVs). IVW method suggested that order Enterobacteriales (OR: 1.35, 95% CI 1.10-1.66), family Bacteroidales S24.7group (OR: 1.22, 95% CI 1.05-1.41), genus Lachnospiraceae UCG008 (OR: 1.16, 95% CI 1.03-1.31), genus Prevotella 7 (OR: 1.11, 95% CI 1.01-1.23), and order Pasteurellales (OR: 1.12, 95% CI 1.00-1.26) may be associated with a higher risk of periodontitis, while genus Ruminiclostridium 6 may be linked to a lower risk (OR: 0.82, 95% CI 0.70-0.95). The sensitivity and heterogeneity analyses yielded no indication of horizontal pleiotropy or heterogeneity. Only the association between order Enterobacteriales and the likelihood of periodontitis remained consistent across all alternative MR approaches. In the reverse MR analysis, four microbiota genera were genetically predicted to be down-regulated in periodontitis, whereas two were predicted to be up-regulated.

CONCLUSIONS

The present MR analysis demonstrated the potential bidirectional causal relationships between gut microbiota and periodontitis. Our research provided fresh insights for the prevention and management of periodontitis. Future research is required to support the finding of our current study.

Causal relationships between the gut microbiome, blood lipids, and heart failure: a Mendelian randomization analysis

AIMS

Studies have linked gut microbiome and heart failure (HF). However, their causal relationships and potential mediating factors have not been well defined. To investigate the causal relationships between the gut microbiome and HF and the mediating effect of potential blood lipids by using genetics.

METHODS AND RESULTS

We performed a bidirectional and mediation Mendelian randomization (MR) study using summary statistics from the genome-wide association studies of gut microbial taxa (Dutch Microbiome Project, n = 7738), blood lipids (UK Biobank, n = 115 078), and a meta-analysis of HF (115 150 cases and 1550 331 controls). We applied the inverse-variance weighted estimation method as the primary method, with several other estimators as complementary methods. The multivariable MR approach based on Bayesian model averaging (MR-BMA) was used to prioritize the most likely causal lipids. Six microbial taxa are suggestively associated with HF causally. The most significant taxon was the species Bacteroides dorei [odds ratio = 1.059, 95% confidence interval (CI) = 1.022-1.097, P-value = 0.0017]. The MR-BMA analysis showed that apolipoprotein B (ApoB) was the most likely causal lipid for HF (the marginal inclusion probability = 0.717, P-value = 0.005). The mediation MR analysis showed that ApoB mediated the causal effects of species B. dorei on HF (proportion mediated = 10.1%, 95% CI = 0.2-21.6%, P-value = 0.031).

CONCLUSION

The study suggested a causal relationship between specific gut microbial taxa and HF and that ApoB might mediate this relationship as the primary lipid determinant of HF.