Genome-wide Association Study Summary Data Research Articles

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.

Causal effects of glycemic traits and endometriosis: a bidirectional and multivariate mendelian randomization study

BackgroundObservational studies have suggested an association between endometriosis and glycemic traits, but causality remains unclear. We used bidirectional and multivariate Mendelian randomization (MR) to examine the causal effect of glycemic traits on endometriosis and vice versa.MethodsWe obtained genome-wide association studies summary data of endometriosis and glycemic traits in our study. Inverse variance weighted (IVW), Weighted median, MR-Egger and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) were applied in bidirectional two-sample MR analyses. MVMR was implemented to estimate the causal effect for fasting insulin (FI), fasting glucose (FG), and glycosylated hemoglobin A1c (HbA1c) on endometriosis. To test the validity of our findings, a number of sensitivity analyses were conducted.ResultsThe risk of endometriosis was significantly increased by genetically predicted T1DM (OR = 1.02, 95% CI 1.00-1.04, p = 0.0171, q = 0.0556) and GDM (OR = 1.01, 95% CI 1.01–1.02, p = 1.34 × 10− 8, q = 1.74 × 10− 7). Endometriosis had a suggestive association with HbA1c (Beta = 0.04, 95% CI 0.00-0.08, p = 0.0481, q = 0.1251). Using multivariate Mendelian randomization (MVMR), a significant causal effect of FI on genetically predicted endometriosis was found (OR = 2.18, 95% CI 1.16–4.09, p = 0.0154, q = 0.0547). Moreover, no causal associations between endometriosis and other glycemic traits were detected.ConclusionOur findings supported the significant causal associations of T1DM, GDM and FI with endometriosis, respectively. Additionally, a suggestive association was found of endometriosis on HbA1c. Importantly, our study may shed light on etiology studies and clinical management of endometriosis.

Alcohol drinking, DNA methylation and psychiatric disorders: A multi-omics Mendelian randomization study to investigate causal pathways.

Whether alcohol-related DNA methylation has a causal effect on psychiatric disorders has not been investigated. Furthermore, a comprehensive investigation into the causal relationship and underlying mechanisms linking alcohol consumption and psychiatric disorders has been lacking. This study aimed to evaluate the causal effect of general alcohol intake and pathological drinking behaviors on psychiatric disorders, alcohol-associated DNA methylation on gene expression and psychiatric disorders, and gene expression on psychiatric disorders. Two-sample design Mendelian randomization (MR) analysis. Various sensitivity and validation analyses, including colocalization analysis, were conducted to test the robustness of the results. Genome-wide association study (GWAS) data mainly from GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetics of DNA Methylation Consortium (GoDMC) and Psychiatric Genomics Consortium (PGC) with European ancestry. The GWAS summary data on general alcohol intake (drinks per week, n = 941 280), pathological drinking behaviors (including alcohol use disorder [AUD, n = 313 959] and problematic alcohol use [PAU, n = 435 563]) and psychiatric disorders (including schizophrenia, major depressive disorder and bipolar disorder, n = 51 710-500 199) were included. Alcohol-related DNA methylation CpG sites (n = 9643) and mQTL data from blood (n = 27 750) and brain (n = 1160), BrainMeta v2 and GTEx V8 eQTL summary data (n = 73-2865) were also included. Genetic variants were selected as instrumental variables for exposures, including drinks per week, AUD, PAU, alcohol-related DNA methylation CpG sites (mQTL) and genes selected (eQTL). Pathological drinking behaviors were associated with an increased risk of psychiatric disorders after removing outliers or controlling for alcohol consumption. MR analysis identified 10 alcohol-related CpG sites with colocalization evidence that were causally associated with psychiatric disorders (P = 1.65 × 10-4-7.52 × 10-22). Furthermore, the expression of genes (RERE, PTK6, GATAD2B, COG8, PDF and GAS5) mapped to these CpG sites in the brain, led by the cortex, were significantly associated with psychiatric disorders (P = 1.19 × 10-2-3.51 × 10-7). Pathological drinking behavior and alcohol-related DNA methylation appear to have a causal effect on psychiatric disorders. The expression of genes regulated by the alcohol-related DNA methylation sites may underpin this association.

Genetically predicted high serum sex hormone-binding globulin levels are associated with lower ischemic stroke risk: A sex-stratified Mendelian randomization study

ObjectiveCross-sectional and cohort studies have found insufficient evidence of a causal relationship between sex hormone-binding globulin and ischemic stroke, only associations. Here, we performed a sex-stratified, bidirectional, two-sample Mendelian randomization analysis to evaluate whether a causal relationship exists between sex hormone-binding globulin and ischemic stroke. MethodsSingle-nucleotide polymorphisms associated with sex hormone-binding globulin and ischemic stroke were screened from genome-wide association studies summary data as instrumental variables to enable a bidirectional, two-sample Mendelian randomization study design. Inverse-variance weighted analysis was used as the main method to evaluate potential causality, and additional methods, including the weighted median and MR-Egger tests, were used to validate the Mendelian randomization results. Cochran's Q statistic, MR-Egger intercept test, and Mendelian Randomization-Pleiotropy Residual Sum and Outlier global test were used as sensitivity analysis techniques to assure the reliability of the results. Multivariable analysis was used to show the robustness of the results with key theorized confounders. ResultsInverse-variance weighted analysis showed that genetically predicted higher serum sex hormone-binding globulin levels were associated with significantly decreased risk of ischemic stroke in males (odds radio = 0.934, 95 % confidence interval = 0.885-0.985, P = 0.012) and females (odds radio = 0.924, 95 % confidence interval = 0.868-0.983, P = 0.013). In an analysis of ischemic stroke subtypes, genetically predicted higher serum sex hormone-binding globulin levels were also associated with significantly decreased risk of small-vessel occlusion in both males (odds radio = 0.849, 95 % confidence interval = 0.759-0.949, P = 0.004) and females (odds radio = 0.829, 95 % confidence interval = 0.724-0.949, P = 0.006). The association remained in sensitivity analyses and multivariable analyses. The reverse analysis suggested an association between genetically predicted risk of cardioembolism and increased serum sex hormone-binding globulin in females (Beta = 0.029 nmol/L, Standard Error = 0.010, P = 0.003). ConclusionOur findings provide new insight into the etiology of ischemic stroke and suggest that modulating serum sex hormone-binding globulin may be a therapeutic strategy to protect against ischemic stroke.

A large-scale causal analysis of gut microbiota and endometriosis associated infertility: A Mendelian randomization study.

Endometriosis is a prevalent condition with notable impacts on fertility. Recent studies have implicated gut microbiota in the development of endometriosis associated infertility (EAI). This study employs Mendelian randomization (MR) to elucidate the causal relationship between specific gut microbes and EAI. Using MR, we selected single nucleotide polymorphisms associated with 211 gut microbiota taxa from large-scale genome-wide association studies summary data. We applied statistical methods including inverse variance weighting, weighted median, and MR-Egger for analysis. Outliers were identified through the leave-one-out method. MR-Egger intercept tests were conducted to address horizontal pleiotropy, while Cochran Q and P values assessed heterogeneity. The false discovery rate method was used for multiple testing correction. Sensitivity analysis and F statistics evaluated the reliability and potential biases of our results. The inverse variance weighting method indicated a significant association of the genus Actinomyces (OR = 1.657, 95% CI: 1.187-2.312, P = .00298) with an increased risk of EAI. Conversely, genera Holdemania (OR = 0.630, 95% CI: 0.444-0.894, P = .00969) and Ruminococcaceae NK4A214 group (OR = 0.689, 95% CI: 0.481-0.999, P = .0439) appeared as protective factors. MR-PRESSO global test and MR-Egger regression indicated no significant horizontal pleiotropy (P > .05). Leave-one-out analysis confirmed the robustness of these findings. Our study provides evidence of a causal relationship between specific gut microbiome taxa and EAI. These findings offer novel insights and may guide the development of new preventive and therapeutic strategies for managing EAI.

Abstract 6145: A trans-ethnic meta-analysis identified additional novel genomic loci associated with cervical cancer

Abstract Introduction: Cervical cancer is the second most common cause of cancer-related deaths among women worldwide. Genome-wide association studies (GWAS) have identified genetic variants associated with cervical cancer. However, these discoveries have been limited by the small sample sizes of the individual cohorts. To identify additional novel loci associated with cervical cancer, we performed a trans-ethnic meta-analysis. Methods: GWAS summary data for trans-ethic meta-analysis were identified in women of European ancestry from the UK Biobank, East Asian ancestry from Biobank Japan, and African ancestries from the pan-UK Biobank and Johannesburg cancer study. Meta-analyses were performed using fixed effect inverse variance weighted method implemented in GWAMA. To localize putative genomic loci associated with cervical cancer, we performed fine mapping using the Bayesian approach by calculating the marginal posterior probability of causality for each single nucleotide polymorphisms (SNPs) and 99% credible set size. Results: Our meta-analysis identified four additional novel genomic loci associated with cervical cancer. These loci include: rs147778467 (odds ratio [OR] = 1.12, 95% confidence interval [CI] = 1.08-1.16, P-value = 3.12 x 10-08) in RGS21, rs53150478 (OR =1.03, 95%CI = 1.01-1.05, P-value = 4.48 x 10-08) in MAN1C1, rs113842373 (OR = 0.68, 95%CI = 0.56-0.81, P-value = 2.31 x 10-09) in LINCO1850 and rs75372437 (OR = 0.80, 95%CI = 0.72-0.88, P-value = 3.32 x 10-08) in RNU6-356P. These loci are implicated in carcinogenic pathways. Moreover, our Bayesian fine mapping identified five loci with a marginal posterior probability of causality &amp;gt; 1 and reduced the 99% credible set sizes. Conclusion: We identified additional novel loci associated with cervical cancer and our fine mapping identified loci with a high posterior probability of being causal. Citation Format: Abram Bunya Kamiza, Jean-Tristan Brandenburg, Michele Ramsay, Christopher Mathew. A trans-ethnic meta-analysis identified additional novel genomic loci associated with cervical cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6145.

The causal relationship between gut microbiota and biliary tract cancer: comprehensive bidirectional Mendelian randomization analysis.

Growing evidence has shown that gut microbiome composition is associated with Biliary tract cancer (BTC), but the causality remains unknown. This study aimed to explore the causal relationship between gut microbiota and BTC, conduct an appraisal of the gut microbiome's utility in facilitating the early diagnosis of BTC. We acquired the summary data for Genome-wide Association Studies (GWAS) pertaining to BTC (418 cases and 159,201 controls) from the Biobank Japan (BBJ) database. Additionally, the GWAS summary data relevant to gut microbiota (N = 18,340) were sourced from the MiBioGen consortium. The primary methodology employed for the analysis consisted of Inverse Variance Weighting (IVW). Evaluations for sensitivity were carried out through the utilization of multiple statistical techniques, encompassing Cochrane's Q test, the MR-Egger intercept evaluation, the global test of MR-PRESSO, and a leave-one-out methodological analysis. Ultimately, a reverse Mendelian Randomization analysis was conducted to assess the potential for reciprocal causality. The outcomes derived from IVW substantiated that the presence of Family Streptococcaceae (OR = 0.44, P = 0.034), Family Veillonellaceae (OR = 0.46, P = 0.018), and Genus Dorea (OR = 0.29, P = 0.041) exerted a protective influence against BTC. Conversely, Class Lentisphaeria (OR = 2.21, P = 0.017), Genus Lachnospiraceae FCS020 Group (OR = 2.30, P = 0.013), and Order Victivallales (OR = 2.21, P = 0.017) were associated with an adverse impact. To assess any reverse causal effect, we used BTC as the exposure and the gut microbiota as the outcome, and this analysis revealed associations between BTC and five different types of gut microbiota. The sensitivity analysis disclosed an absence of empirical indicators for either heterogeneity or pleiotropy. This investigation represents the inaugural identification of indicative data supporting either beneficial or detrimental causal relationships between gut microbiota and the risk of BTC, as determined through the utilization of MR methodologies. These outcomes could hold significance for the formulation of individualized therapeutic strategies aimed at BTC prevention and survival enhancement.