Genome-wide Association Study Summary Data Research Articles

A Mendelian randomization analysis reveals the multifaceted role of the skin microbiota in liver cancer

BackgroundHepatocellular carcinoma (HCC, or hepatic cancer, HC) and cholangiocarcinoma (CCA, or hepatic bile duct cancer, HBDC) are two major types of primary liver cancer (PLC). Previous studies have suggested that microbiota can either act as risk factors or preventive factors in PLC. However, no study has reported the relationship between skin microbiota and PLC. Therefore, we conducted a two-sample Mendelian randomization (MR) study to assess the causality between skin microbiota and PLC.MethodsData from the genome-wide association study (GWAS) on skin microbiota were collected. The GWAS summary data of GCST90018803 (HBDC) and GCST90018858 (HC) were utilized in the discovery and verification phases, respectively. The inverse variance weighted (IVW) method was utilized as the principal method in our MR study. The MR-Egger intercept test, Cochran's Q-test, MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO), and leave-one-out analysis were conducted to identify the heterogeneity and pleiotropy.ResultsThe results showed that Veillonella (unc.) plays a protective role in HBDC, while the family Neisseriaceae has a positive association with HBDC risk. The class Betaproteobacteria, Veillonella (unc.), and the phylum Bacillota (Firmicutes) play a protective role in HC. Staphylococcus epidermidis, Corynebacterium (unc.), the family Neisseriaceae, and Pasteurellaceae sp. were associated with an increased risk of HC.ConclusionThis study provided new evidence regarding the association between skin microbiota and PLC, suggesting that skin microbiota plays a role in PLC progression. Skin microbiota could be a novel and effective way for PLC diagnosis and treatment.

Integrating Mendelian Randomization with Single-cell Sequencing Data Reveals the Causal Effect and Related Mechanisms of Smoking on Parkinson's Disease.

Smoking (nicotine) has been reported to possibly be neuroprotective and conducive to patients with early Parkinson's disease (PD). However, the causal effect of smoking on PD and the molecular mechanisms of smoking-related genes (SRGs) are vague. First, genome-wide association study summary data on smoking (ukb-b-6244) and PD (ieu-b-7) were retrieved from the Integrative Epidemiology Unit OpenGWAS database for Mendelian randomization (MR) analysis. Sensitivity analyses were performed to validate the results of the MR analyses. Subsequently, a differential analysis of PD patients and controls was performed to identify differentially expressed SRGs (DE-SRGs). Finally, the expression of DE-SRGs was analyzed in annotated cell types. The MR analysis revealed that smoking was a protective factor causally related to PD (P=0.008, odds ratio=0.288). Furthermore, a total of five DE-SRGs enriched in Toll-like receptor signaling pathways were identified in GSE7621 dataset. Regarding single-cell analysis of GSE184950 dataset, a total of nine cell types were annotated. The expression of LRRN1 in oligodendrocyte progenitor cells and oligodendrocytes, respectively, differed significantly between PD patients and controls. Our study supported a causal relationship between smoking and PD and found that five SRGs (MAPK8IP1, LRRN1, LINC00324, HIST1H2BK, and YOD1) enriched in Toll-like receptor signaling pathways might be beneficial in PD. In addition, single-cell sequencing indicated that four SRGs were differentially expressed in different cell types. All four genes except MAPK8IP1 were significantly correlated with the 10 genes calculated by scPagwas. Thus, this evidence provides a theoretical basis for further research on the effect of nicotine (smoking) on PD. In order to explore the potential etiology and pathogenesis of Parkinson's disease, this study combined Mendelian randomization, transcriptomics and single-cell sequencing analysis to explore the association between exposure factors and Parkinson's disease, observe and confirm the relationship and mechanism between the two from the perspective of genetics, and provide more reliable evidence for causal inference.

Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis.

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partiallydue to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.

Causality of metabolites and metabolic pathways on cholestatic liver diseases: a Mendelian randomization study.

Blood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Univariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination. A genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471-6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949-7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068-0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801-16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621-17.399, p = 7.64 × 10-5), X-11847 (OR = 0.478, 95%CI: 0.352-0.650, p = 2.28 × 10-6), and X-12405 (OR = 3.765, 95%CI: 1.771-8.005, p = 5.71 × 10-4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs. The findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.

Association between walking pace and heart failure: A Mendelian Randomization analysis

Background and AimThe relationship between walking pace and heart failure (HF) has been recognized, yet the directionality and underlying mediating risk factors remain unclear. Methods and ResultsThis study utilized bidirectional two-sample Mendelian randomization (MR) with genome-wide association studies (GWAS) summary statistics to assess the causal relationships between walking pace and HF. Additionally, we employed a two-step Multivariable Mendelian Randomization (MVMR) to explore potential mediating factors. We further validated our findings by conducting two-sample MR with another available GWAS summary data on heart failure. Results indicated that genetically predicted increases in walking pace were associated with a reduced risk of HF (odds ratio (OR), 0.589, 95% confidence interval (CI): 0.417-0.832). Among the considered mediators, the waist-to-hip ratio (WHR) accounts for the largest proportion of the effect (45.7%, 95% CI: 13.2%, 78.2%). This is followed by type 2 diabetes at 24.4% (95% CI: 6.7%, 42.0%) and triglycerides at 18.6% (95% CI: 4.5%, 32.7%). Furthermore, our findings reveal that genetically predicted HF risk (OR, 0.975, 95% CI: 0.960-0.991) is associated with a slower walking pace. Validated findings were consistent with the main results. ConclusionsIn conclusion, MR analysis demonstrates that a slow walking pace is a reliable indicator of an elevated risk of HF, and the causal relationship is bidirectional. Interventions focusing on waist-to-hip ratio, type 2 diabetes, and triglycerides may provide valuable strategies for HF prevention in individuals with a slow walking pace.

Hidradenitis suppurativa and cardiovascular diseases: A bidirectional Mendelian randomization study.

Prior investigation has indicated a link between Hidradenitis suppurativa (HS) and cardiovascular diseases (CVDs), yet the causal relationship (CR) between these conditions remains unresolved. This investigation utilized bidirectional Mendelian randomization (MR) analysis to determine the CR between HS and CVDs. Genetic instruments for both conditions were sourced from genome-wide association studies (GWAS). The GWAS summary data for CVD comprised coronary artery disease (CAD), myocardial infarction (MI), coronary atherosclerosis (CA), ischemic stroke (IS), and chronic heart failure (CHF). Four new approaches were added to the inverse variance weighted (IVW) method for the main analysis: weighted median, weighted MR-Egger, simple mode, and weighted mode. The validity of the causal conclusions was verified by sensitivity tests that included leave-one-out analysis, heterogeneity, and pleiotropy. HS and CAD (OR=1.024; 95%CI: 1.002-1.046, P=0.033), MI (OR=1.001; 95%CI: 1.000-1.002, P=0.033), and CA (OR=1.001; 95%CI: 1.000-1.002, P=0.022) were identified to have a positive CR, according to the IVW analysis. Conversely, no significant association was identified between HS and either IS or CHF. Furthermore, the bidirectional analysis indicated no reverse causation between these diseases. The findings of this study suggest a potential CR between HS and CAD, MI, and CA. Additional research is warranted to elucidate the biological mechanisms underlying these associations.

Genetically Predicted Sleep Traits and Sensorineural Hearing Loss: A Mendelian Randomization Study.

Observational studies suggest a potential association between sleep characteristics, sensorineural hearing loss (SNHL), and sudden SNHL (SSNHL), but causal evidence is scarce. We sought to clarify this issue using two-sample Mendelian randomization analysis. The inverse-variance weighted (IVW) method was performed as primary analysis to assess bidirectional causal associations between sleep traits (chronotype, sleep duration, insomnia, daytime sleepiness, and snoring) and SNHL/SSNHL using publicly available Genome-Wide Association Studies summary data from two large consortia (UK Biobank and FinnGen). Sensitivity analyses, including Mendelian randomization (MR)-Egger, Mendelian randomization pleiotropy residual sum and outlier, weight median, Cochran's Q test, leave-one-out analysis, and potential pleiotropy analysis, were conducted to ensure robustness. IVW analysis found suggestive associations of morning chronotype (odds ratio [OR] = 1.08, 95% confidence interval [CI] = 1.01-1.16, p = 0.031) and daytime sleepiness (OR = 1.88, 95% CI = 1.24-2.87, p = 0.003) with SNHL onset. Additionally, morning chronotype was nominally associated with SSNHL onset using IVW method (OR = 1.37, 95% CI = 1.10-1.71, p = 0.006). However, there was no evidence for the causal effect of SNHL and SSNHL on different sleep traits (all p > 0.05). Sensitivity analysis showed that the results were stable. Within the MR limitations, morning chronotype and daytime sleepiness were underlying causal contributors to the burden of SNHL, indicating that optimal sleep might facilitate the prevention and development of SNHL. 3 Laryngoscope, 2024.

HMG-CoA reductase is a potential therapeutic target for migraine: a mendelian randomization study

Statins are thought to have positive effects on migraine but existing data are inconclusive. We aimed to evaluate the causal effect of such drugs on migraines using Mendelian randomization. We used four types of genetic instruments as proxies for HMG-CoA reductase inhibition. We included the expression quantitative trait loci of the HMG-CoA reductase gene and genetic variation within or near the HMG-CoA reductase gene region. Variants were associated with low-density lipoprotein cholesterol, apolipoprotein B, and total cholesterol. Genome-wide association study summary data for the three lipids were obtained from the UK Biobank. Comparable data for migraine were obtained from the International Headache Genetic Consortium and the FinnGen Consortium. Inverse variance weighting method was used for the primary analysis. Additional analyses included pleiotropic robust methods, colocalization, and meta-analysis. Genetically determined high expression of HMG-CoA reductase was associated with an increased risk of migraines (OR = 1.55, 95% CI 1.30–1.84, P = 6.87 × 10−7). Similarly, three genetically determined HMG-CoA reductase-mediated lipids were associated with an increased risk of migraine. These conclusions were consistent across meta-analyses. We found no evidence of bias caused by pleiotropy or genetic confounding factors. These findings support the hypothesis that statins can be used to treat migraine.

Causal relationship between rheumatoid arthritis and epilepsy in a European population: a univariate and multivariate Mendelian randomization study.

Several previous studies have reported an association between rheumatoid arthritis (RA) and epilepsy, but the causal relationship is unclear. The aim of this study was to assess the connection between RA and epilepsy in a European population using Mendelian randomization (MR). Genome-wide association study summary data on RA and epilepsy from European populations were included. Univariate MR (UVMR) and multivariate MR were used to investigate the causal relationship between the two conditions. Three analysis methods were applied: inverse variance weight (IVW), MR-Egger, and weighted median, with IVW being the primary method. Cochran Q statistics, MR-PRESSO, MR-Egger intercept, leave-one-out test, and MR-Steiger test were combined for the sensitivity analysis. UVMR showed a positive association between RA and epilepsy risk (OR=1.038, 95% CI=1.007-1.038, p=0.017) that was supported by sensitivity analysis. Further MVMR after harmonizing the three covariates of hypertension, alcohol consumption, and smoking, confirmed the causal relationship between RA and epilepsy (OR=1.049, 95% CI=1.011-1.087, p=0.010). This study demonstrated that RA is associated with an increased risk of epilepsy. It has emphasized that the monitoring of epilepsy risk in patients diagnosed with RA should be strengthened in clinical practice, and further studies are needed in the future to explore the potential mechanism of action connecting the two conditions.

Causal relationship between inflammatory proteins and glioblastoma: a two-sample bi‑directional mendelian randomization study.

Background: Observational studies have indicated a potential correlation between glioblastoma and circulating inflammatory proteins. Further investigation is required to establish a causal relationship between these two factors. Methods: We performed a Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary of 91 circulating inflammation-related proteins (N = 14,824) to assess their causal impact on glioblastoma. The GWAS summary data for glioblastoma included 243 cases and 287,137 controls. The inverse variance weighted (IVW) method was used as the primary analytical method to assess causality. Four additional MR methods [simple mode, MR-Egger, weighted median, and weighted mode] were used to supplement the IVW results. Furthermore, several sensitivity analyses were performed to assess heterogeneity, horizontal pleiotropy, and stability. Reverse MR analysis was also performed. glioblastoma transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed to validate the findings obtained through MR, while pathway and functional enrichment analyses were conducted to predict the potential underlying mechanisms. Results: Our findings from employing the inverse variance weighted method in our forward MR analysis provide robust evidence supporting a potential association between glioblastoma and elevated levels of Cystatin D, as well as decreased levels of fibroblast growth factor 21 (FGF21) in the circulation. Moreover, our reverse MR analysis revealed that glioblastoma may contribute to increased concentrations of C-X-C motif chemokine 9 (CXCL9) and Interleukin-33 (IL-33) in the bloodstream. Transcriptomic analysis showed that FGF21 expression was inversely associated with the risk of developing glioblastoma, whereas an increased risk was linked to elevated levels of CXCL9 and IL-33. Pathway and functional enrichment analyses suggested that Cystatin D might exert its effects on glioblastoma through intracellular protein transport, whereas FGF21 might affect glioblastoma via glucose response mechanisms. Conclusion: These results indicate that FGF21 is a significant factor in glioblastoma susceptibility. Glioblastoma also affects the expression of inflammatory proteins such as C-X-C motif chemokine 9 and Interleukin-33, providing new insights into the mechanisms of glioblastoma genesis and clinical research.

Integration of multi-omics summary data reveals the role of N6-methyladenosine in neuropsychiatric disorders.

N6-methyladenosine (m6A) methylation regulates gene expression/protein by influencing numerous aspects of mRNA metabolism and contributes to neuropsychiatric diseases. Here, we integrated multi-omics data and genome-wide association study summary data of schizophrenia (SCZ), bipolar disorder (BP), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD) to reveal the role of m6A in neuropsychiatric disorders by using transcriptome-wide association study (TWAS) tool and Summary-data-based Mendelian randomization (SMR). Our investigation identified 86 m6A sites associated with seven neuropsychiatric diseases and then revealed 7881 associations between m6A sites and gene expressions. Based on these results, we discovered 916 significant m6A-gene associations involving 82 disease-related m6A sites and 606 genes. Further integrating the 58 disease-related genes from TWAS and SMR analysis, we obtained 61, 8, 7, 3, and 2 associations linking m6A-disease, m6A-gene, and gene-disease for SCZ, BP, AD, MDD, and PD separately. Functional analysis showed the m6A mapped genes were enriched in "response to stimulus" pathway. In addition, we also analyzed the effect of gene expression on m6A and the post-transcription effect of m6A on protein. Our study provided new insights into the genetic component of m6A in neuropsychiatric disorders and unveiled potential pathogenic mechanisms where m6A exerts influences on disease through gene expression/protein regulation.

Interactions between circulating inflammatory factors and autism spectrum disorder: a bidirectional Mendelian randomization study in European population.

Extensive observational studies have reported an association between inflammatory factors and autism spectrum disorder (ASD), but their causal relationships remain unclear. This study aims to offer deeper insight into causal relationships between circulating inflammatory factors and ASD. Two-sample bidirectional Mendelian randomization (MR) analysis method was used in this study. The genetic variation of 91 circulating inflammatory factors was obtained from the genome-wide association study (GWAS) database of European ancestry. The germline GWAS summary data for ASD were also obtained (18,381 ASD cases and 27,969 controls). Single nucleotide polymorphisms robustly associated with the 91 inflammatory factors were used as instrumental variables. The random-effects inverse-variance weighted method was used as the primary analysis, and the Bonferroni correction for multiple comparisons was applied. Sensitivity tests were carried out to assess the validity of the causal relationship. The forward MR analysis results suggest that levels of sulfotransferase 1A1, natural killer cell receptor 2B4, T-cell surface glycoprotein CD5, Fms-related tyrosine kinase 3 ligand, and tumor necrosis factor-related apoptosis-inducing ligand are positively associated with the occurrence of ASD, while levels of interleukin-7, interleukin-2 receptor subunit beta, and interleukin-2 are inversely associated with the occurrence of ASD. In addition, matrix metalloproteinase-10, caspase 8, tumor necrosis factor-related activation-induced cytokine, and C-C motif chemokine 19 were considered downstream consequences of ASD. This MR study identified additional inflammatory factors in patients with ASD relative to previous studies, and raised a possibility of ASD-caused immune abnormalities. These identified inflammatory factors may be potential biomarkers of immunologic dysfunction in ASD.

XWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BackgroundIntegrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.MethodsIn this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.ResultsOur findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.ConclusionsThe analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

Identifying the causal relationship between immune factors and osteonecrosis: a two-sample Mendelian randomization study

A wealth of evidence intimates a profound connection between the immune system and osteonecrosis, albeit the specific immune factors underlying this connection remain largely veiled. A bidirectional Mendelian randomization (MR) study was conducted based on genome-wide association study summary data to identify causal links between 731 immune factors and osteonecrosis including drug-induced osteonecrosis. Preliminary MR analysis was accomplished utilizing the inverse-variance weighted method under a multiplicative random effects model, and heterogeneity and potential horizontal pleiotropy were evaluated through Cochrane's Q-test, MR-Egger intercept test, MR-PRESSO global test, and leave-one-out analysis. Upon false discovery rate correction, the gene-predicted level of one immune factor (CD62L − monocyte %monocyte) exhibited a significant positive correlation with osteonecrosis, while eight immune traits associated with monocytes, dendritic cells, and NK cells demonstrated significant causal effects with drug-induced osteonecrosis. Reverse MR revealed no significant correlations. This MR research provides genetic evidence for the causal associations between a broad spectrum of immune factors and osteonecrosis. Such a study aids in unraveling the intricate interaction patterns between the immune and skeletal systems, elucidating the pathogenesis of osteonecrosis, and identifying potential novel therapeutic approaches.

Does androgenic alopecia aggravate the risk of prostate cancer? Evidence from Mendelian randomization

BackgroundEpidemiological reports indicate a potential association between androgenic alopecia (AGA) and increased prostate cancer (PC) prevalence, but conflicting reports also exist. This study aims to elucidate the causality of AGA on PC risk using Mendelian randomization (MR) analysis. Materials and methodsTwo-sample MR analyses utilized public genome-wide association studies summary data for single-nucleotide polymorphisms associated with AGA. Four statistical methods were used: inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode, with IVW as the preliminary estimation method. Additionally, sensitivity analyses were conducted to address pleiotropic bias. ResultsGenetically proxied AGA did not demonstrate a causal effect on PC risk (IVW P > 0.05). Consistently, complementary methods yielded results aligned with IVW. ConclusionsOur MR analysis indicates no causal relationship between genetically predicted AGA and PC risk, suggesting that observed associations in epidemiological studies may not be causal.

To analyze the relationship between gut microbiota, metabolites and migraine: a two-sample Mendelian randomization study.

It has been suggested in several observational studies that migraines are associated with the gut microbiota. It remains unclear, however, how the gut microbiota and migraines are causally related. We performed a bidirectional two-sample mendelian randomization study. Genome-wide association study (GWAS) summary statistics for the gut microbiota were obtained from the MiBioGen consortium (n = 18,340) and the Dutch Microbiota Project (n = 7,738). Pooled GWAS data for plasma metabolites were obtained from four different human metabolomics studies. GWAS summary data for migraine (cases = 48,975; controls = 450,381) were sourced from the International Headache Genetics Consortium. We used inverse-variance weighting as the primary analysis. Multiple sensitivity analyses were performed to ensure the robustness of the estimated results. We also conducted reverse mendelian randomization when a causal relationship between exposure and migraine was found. LachnospiraceaeUCG001 (OR = 1.12, 95% CI: 1.05-1.20) was a risk factor for migraine. Blautia (OR = 0.93, 95% CI: 0.88-0.99), Eubacterium (nodatum group; OR = 0.94, 95% CI: 0.90-0.98), and Bacteroides fragilis (OR = 0.97, 95% CI: 0.94-1.00) may have a suggestive association with a lower migraine risk. Functional pathways of methionine synthesis (OR = 0.89, 95% CI: 0.83-0.95) associated with microbiota abundance and plasma hydrocinnamate (OR = 0.85, 95% CI: 0.73-1.00), which are downstream metabolites of Blautia and Bacteroides fragilis, respectively, may also be associated with lower migraine risk. No causal association between migraine and the gut microbiota or metabolites was found in reverse mendelian randomization analysis. Both significant horizontal pleiotropy and significant heterogeneity were not clearly identified. This Mendelian randomization analysis showed that LachnospiraceaeUCG001 was associated with an increased risk of migraine, while some bacteria in the gut microbiota may reduce migraine risk. These findings provide a reference for a deeper comprehension of the role of the gut-brain axis in migraine as well as possible targets for treatment interventions.

Type 1 diabetes mellitus and non-alcoholic fatty liver disease: a two-sample Mendelian randomization study.

NAFLD (Nonalcoholic fatty liver disease) is becoming an increasingly common cause of chronic liver disease. Metabolic dysfunction, overweight/obesity, and diabetes are thought to be closely associated with increased NAFLD risk. However, few studies have focused on the mechanisms of NAFLD occurrence in T1DM. We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal association between T1DM and NAFLD with/without complications, such as coma, renal complications, ketoacidosis, neurological complications, and ophthalmic complications. Multiple Mendelian randomization methods, such as the inverse variance weighted (IVW) method, weighted median method, and MR-Egger test were performed to evaluate the causal association of T1DM and NAFLD using genome-wide association study summary data from different consortia, such as Finngen and UK biobank. We selected 37 SNPs strongly associated with NAFLD/LFC (at a significance level of p < 5 × 10-8) as instrumental variables from the Finnish database based on the T1DM phenotype (8,967 cases and 308,373 controls). We also selected 14/16 SNPs based on with or without complications. The results suggest that the genetic susceptibility of T1DM does not increase the risk of NAFLD (OR=1.005 [0.99, 1.02], IVW p=0.516, MR Egger p=0.344, Weighted median p=0.959, Weighted mode p=0.791), regardless of whether complications are present. A slight causal effect of T1DM without complications on LFC was observed (OR=1.025 [1.00, 1.03], MR Egger p=0.045). However, none of the causal relationships were significant in the IVW (p=0.317), Weighted median (p=0.076), and Weighted mode (p=0.163) methods. Our study did not find conclusive evidence for a causal association between T1DM and NAFLD, although clinical observations indicate increasing abnormal transaminase prevalence and NAFLD progression in T1DM patients.

Whole-body water mass and kidney function: a Mendelian randomization study.

The morbidity and mortality of chronic kidney disease (CKD) are increasing worldwide, making it a serious public health problem. Although a potential correlation between body water content and CKD progression has been suggested, the presence of a causal association remains uncertain. This study aimed to determine the causal effect of body water content on kidney function. Genome-wide association study summary data sourced from UK Biobank were used to evaluate single-nucleotide polymorphisms (SNPs) associated with whole-body water mass (BWM). The summary statistics pertaining to kidney function were extracted from the CKDGen consortium. The primary kidney function outcome measures included estimated glomerular filtration rate (eGFR), albuminuria, CKD stages 3-5, and rapid progression to CKD (CKDi25). Two-sample Mendelian randomization (MR) analysis estimated a potential causal relationship between the BWM and kidney function. The inverse variance weighted MR method was used as the primary analysis, accompanied by several sensitive MR analyses. The increase of BWM exhibited a correlation with a reduction in eGFR (β = -0.02; P = 6.95 × 10-16). Excluding 13 SNPs responsible for pleiotropy (P = 0.05), the increase of BWM was also associated with the decrease of the ratio of urinary albumin to creatinine (β = -0.16; P = 5.91 × 10-36). For each standard deviation increase in BWM, the risk of CKD stages 3-5 increases by 32% (OR, 1.32; 95% CI, 1.19-1.47; P = 1.43 × 10-7), and the risk of CKDi25 increases by 22% (OR, 1.22; 95% CI, 1.07-1.38; P = 0.002). The increase of BWM is associated with impaired kidney function. Proactively managing body water content is of great significance in preventing the progression of CKD.

A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation

Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.

GAUSS: a summary-statistics-based R package for accurate estimation of linkage disequilibrium for variants, Gaussian imputation, and TWAS analysis of cosmopolitan cohorts.

As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g. fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers. To address these challenges, we present Genome Analysis Using Summary Statistics (GAUSS)-a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including (i) estimating ancestry proportion of study cohorts, (ii) calculating ancestry-informed linkage disequilibrium, (iii) imputing summary statistics of unobserved variants, (iv) conducting transcriptome-wide association studies, and (v) correcting for "Winner's Curse" biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information. The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/, along with a comprehensive user guide detailed in Supplementary Text S1.