Genome-wide Association Studies Datasets Research Articles

Is there a genetic relationship between blood glucose and osteoarthritis? A mendelian randomization study.

The relationship between blood glucose levels and osteoarthritis (OA) is unclear. This study aimed to investigate the genetic causal relationship between blood glucose-related traits and OA. We first performed univariate Mendelian randomization (UVMR) analyses using published genome-wide association study (GWAS) datasets with fasting glucose (FG), 2h-glucose post-challenge glucose (2hGlu), and glycosylated hemoglobin (HbA1c) as exposures, and hip osteoarthritis (HOA) and knee osteoarthritis (KOA) as outcomes; then, we performed inverse analyses of them. We used Inverse-variance weighted (IVW) analysis as the primary analysis, and sensitivity analyses were performed. Moreover, we performed multivariate Mendelian randomization (MVMR) to estimate the independent effect of exposure on outcome after adjusting for body mass index (BMI). Summarized data for blood glucose-related traits were obtained from the MAGIC Consortium study of the glucose trait genome and for OA from the UK Biobank and arcOGEN. Summarized data for BMI were obtained from the GIANT Consortium meta-analysis of individuals of European ancestry. A two-sided p value < 0.05 in UVMR was considered suggestive of significance when p < 0.0167 (Bonferroni correction p = 0.05/3 exposures) was considered statistically significant. We found significant negative genetic causality of FG for HOA and KOA, and these associations remained significant after we adjusted for the effect of BMI [odds ratios (ORs) of 0.829 (0.687-0.999, p = 0.049) and 0.741 (0.570-0.964, p = 0.025)]. HbA1c also had an independent negative genetic causal effect on HOA after adjustment for BMI [0.665 (0.463-0.954, p = 0.027)]. At the same time, there was no evidence of reverse genetic causality of OA on blood glucose-related traits. We further elucidated the relationship between blood glucose-related traits and OA by adjusting for the effect of BMI from a genetic causal perspective. This study provides new insights to further clarify the relationship between blood glucose levels and OA, as well as the pathogenesis, etiology and genetics of OA.

A comprehensive study of common and rare genetic variants in spermatogenesis-related loci identifies new risk factors for idiopathic severe spermatogenic failure

Abstract STUDY QUESTION Can genome-wide genotyping data be analysed using a hypothesis-driven approach to enhance the understanding of the genetic basis of severe spermatogenic failure (SPGF) in male infertility? SUMMARY ANSWER Our findings revealed a significant association between SPGF and the SHOC1 gene, and identified three novel genes (PCSK4, AP3B1, and DLK1) along with 32 potentially pathogenic rare variants in 30 genes that contribute to this condition. WHAT IS KNOWN ALREADY SPGF is a major cause of male infertility, often with an unknown aetiology. SPGF can be due to either multifactorial causes, including both common genetic variants in multiple genes and environmental factors, or highly damaging rare variants. Next-generation sequencing (NGS) methods are useful for identifying rare mutations that explain monogenic forms of SPGF. Genome-wide association studies (GWAS) have become essential approaches for deciphering the intricate genetic landscape of complex diseases, offering a cost-effective and rapid means to genotype millions of genetic variants. Novel methods have demonstrated that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes. However, this approach has not been previously applied to characterize the genetic component of a whole case-control cohort. STUDY DESIGN, SIZE, DURATION We employed a hypothesis-driven approach focusing on all genetic variation identified, using a GWAS platform and subsequent genotype imputation, encompassing over 6 million polymorphisms and a total of 1571 SPGF patients and 2431 controls. Both common (minor allele frequency, MAF &amp;gt; 0.01) and rare (MAF &amp;lt; 0.01) variants were investigated within a total of 1797 loci with a reported role in spermatogenesis. This gene panel was meticulously assembled through comprehensive searches in the literature and various databases focused on male infertility genetics. PARTICIPANTS/MATERIALS, SETTING, METHODS This study involved a European cohort using previously and newly generated data. Our analysis consisted of three independent methods: (1) variant-wise association analyses using logistic regression models, (2) gene-wise association analyses using combined multivariate and collapsing (CMC) burden tests, and (3) identification and characterisation of highly damaging rare coding variants showing homozygosity only in SPGF patients. MAIN RESULTS AND THE ROLE OF CHANCE The variant-wise analyses revealed an association between SPGF and SHOC1-rs12347237 (p = 4.15E-06, OR = 2.66), which was likely explained by an altered binding affinity of key transcription factors in regulatory regions and the disruptive effect of coding variants within the gene. Three additional genes (PCSK4, AP3B1, and DLK1) were identified as novel relevant players in human male infertility using the gene-wise burden test approach (p &amp;lt; 5.56E-04). Furthermore, we linked a total of 32 potentially pathogenic and recessive coding variants of the selected genes to 35 different cases. LARGE SCALE DATA Publicly available via GWAS catalog (Accession number: GCST90239721). LIMITATIONS, REASONS FOR CAUTION The analysis of low-frequency variants presents challenges in achieving sufficient statistical power to detect genetic associations. Consequently, independent studies with larger sample sizes are essential to replicate our results. Additionally, the specific roles of the identified variants in the pathogenic mechanisms of SPGF should be assessed through functional experiments. WIDER IMPLICATIONS OF THE FINDINGS Our findings highlight the benefit of using GWAS genotyping to screen for both common and rare variants potentially implicated in idiopathic cases of SPGF, whether due to complex or monogenic causes. The discovery of novel genetic risk factors for SPGF and the elucidation of the underlying genetic causes provide new perspectives for personalized medicine and reproductive counselling. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the Spanish Ministry of Science and Innovation through the Spanish National Plan for Scientific and Technical Research and Innovation (PID2020-120157RB-I00) and the Andalusian Government through the research projects of “Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)” (ref. PY20_00212) and “Proyectos de Investigación aplicada FEDER-UGR 2023” (ref. C-CTS-273-UGR23). SGM was funded by the previously mentioned projects (ref. PY20_00212 and PID2020-120157RB-I00). AGJ was funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro” (grant ref. FPU20/02926). IPATIMUP integrates the i3S Research Unit, which is partially supported by the Portuguese Foundation for Science and Technology (FCT), financed by the European Social Funds (COMPETE-FEDER) and National Funds (projects PEstC/SAU/LA0003/2013 and POCI-01-0145-FEDER-007274). SS is supported by FCT funds (10.54499/DL57/2016/CP1363/CT0019), ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, and is also partially supported by the Portuguese Foundation for Science and Technology (UIDP/00009/2020 and UIDB/00009/2020). SLarriba received support from Instituto de Salud Carlos III (grant: DTS18/00101], co-funded by FEDER funds/European Regional Development Fund (ERDF)-a way to build Europe-), and from “Generalitat de Catalunya” (grant 2021SGR052). SLarriba is also sponsored by the “Researchers Consolidation Program” from the SNS-Dpt. Salut Generalitat de Catalunya (Exp. CES09/020). All authors declare no conflict of interest related to this study.

Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses.

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-variants to enhance model building for TF downstream target genes. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these prediction models to large GWAS datasets for breast, prostate, lung cancers and other diseases. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene expression prediction models and identifying disease-associated genes, as shown by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study shed new light on several genetically driven key TF regulators and their associated TF-gene regulatory networks underlying disease susceptibility.

Plasma Glutaminyl-Peptide Cyclotransferase Mediates Glucosamine-Metabolism-Driven Protection Against Hypertension: A Mendelian Randomization Study

Hypertension is one of the major risk factors for morbidity and mortality worldwide. In this study, Mendelian randomization was utilized to investigate how dietary supplement intake can impact hypertension based on circulating plasma metabolite genome-wide association study (GWAS) datasets, protein quantitative trait loci (pQTLs) of plasma proteins, and multiple public summary-level GWAS data. Pathway enrichment analysis combined with the results of inverse variance weighted Mendelian randomization revealed that a lower risk of hypertension was associated with the dietary intake of glucosamine, an anti-inflammatory supplement: odds ratio (OR) (95% CI): 0.888 (0.824–0.958). Additionally, glucosamine 6-phosphate N-acetyltransferase was identified as a protective factor against hypertension, OR (95% CI): 0.995 (0.992–0.998), shedding light on the potential protective mechanism of glucosamine. Mediation Mendelian randomization indicated that the protective effect of glucosamine metabolism was mediated by glutaminyl-peptide cyclotransferase, with a mediation proportion of 12.1% (5.9–18.2%), p &lt; 0.05. This study offers new insights into preventive strategies for individuals with hypertension risk.

Immune cells mediate the effects of gut microbiota on neuropathic pain: a Mendelian randomization study.

The gut microbiota may be involved in neuropathic pain. However, the causal association between gut microbiota and neuropathic pain remains unclear. Whether immune cells and inflammatory factors mediate the pathway from gut microbiota to neuropathic pain has not been elucidated. We obtained the summary data of 412 gut microbiota, 731 immune cells, 91 inflammatory factors, and five types of neuropathic pain (drug-induced neuropathy, postherpetic neuralgia, sciatica, trigeminal neuralgia, and unspecified neuralgia) from large-scale genome-wide association study (GWAS) datasets and the FinnGen database. We used bidirectional Mendelian randomization (MR) analysis to explore the causal association between gut microbiota and neuropathic pain. Additionally, we conducted a mediation analysis to identify whether immune cells and inflammatory factors act as mediators within these causal relationships. Our study revealed 30 causal relationships between 26 gut bacterial taxa and five types of neuropathic pain, including four associated with drug-induced neuropathy, six with postherpetic neuralgia, five with sciatica, eight with trigeminal neuralgia, and seven with unspecified neuralgia. Moreover, we identified 35 gut bacterial pathway abundances causally involved in neuropathic pain. The reverse MR analysis showed no evidence of reverse causality from gut microbiota to neuropathic pain. Mediation analysis demonstrated that the immune cell phenotype "HLA-DR++ monocyte % leukocyte" mediated the causal relationship between p_Proteobacteria and sciatica with a mediation proportion of 36.15% (P = 0.038), whereas "CD11c on CD62L+ myeloid dendritic cell" mediated the causal pathway from assimilatory sulfate reduction to trigeminal neuralgia with a mediation proportion of 27.90% (P = 0.041). This study identified the causal relationships between several specific gut microbiota and various neuropathic pain subtypes. Additionally, two immune cells may act as potential mediators in the pathways from gut microbiota to neuropathic pain.

EPID-16. GENOMIC ANCESTRY-RELATED RISK RATIOS FOR GLIOBLASTOMA INCIDENCE AMONG GLOBAL POPULATIONS AND RACIAL/ETHNIC GROUPS WITHIN THE UNITED STATES

Abstract Glioblastoma (GBM) incidence varies globally, elevated rates in Europe and lower rates in Asia, Latin America, and Africa. While partially attributable to national differences in cancer diagnosis and reporting practices, analogous patterns exist within diverse Western nations. Ancestry-associated differences in GBM risk allele frequencies (RAFs) could contribute, a hypothesis supported by our prior multiethnic genome-wide association studies (GWAS). Leveraging variant effect estimates from large GBM GWAS datasets and ancestry-stratified allele frequency data from diverse reference populations (ALFA, GNOMAD, 1000 Genomes Project), we estimate the extent of GBM incidence differences across populations that can be attributed to underlying variation in RAF at the seventeen known GBM risk loci. Aggregate differences in RAF between global populations (Europe, West-Africa, East Asia) were associated with a &amp;gt;20% reduction in the normalized GBM risk-ratio (RR) in Africans compared to Europeans (RR=0.78) and a &amp;gt;50% reduction in East Asians compared to Europeans (RR=0.47). The frequency of GBM risk alleles was generally highest in Europeans, but four alleles found most commonly in Africans are at loci regulating telomere maintenance (TERC, TERT, OBFC1/STN1, RTEL1). We repeated analyses using RAF data from U.S. populations (African-Americans, Latinos, and non-Hispanic Whites). Compared to non-Hispanic Whites, aggregate differences in RAF were associated with a 15% reduction in GBM risk among African-Americans (RR=0.85) and a 28% reduction among Latinos (RR=0.72). We conclude that a sizable proportion of the reduced GBM incidence in Latinos and Asians, relative to non-Hispanic Whites, can be accounted for by population-level differences in RAF at known loci. However, registry-based analyses consistently report a two-fold lower incidence of GBM in African Americans relative to non-Hispanic Whites which is poorly explained by RAF differences at known loci. Further work is needed to identify structural, environmental, and biological factors contributing to unresolved differences in GBM incidence.

The causal relationship between diabetes mellitus and the risk of sensorineural hearing loss: A Mendelian randomization study.

An increasing body of evidence suggests that diabetes mellitus (DM) plays a role in sensorineural hearing loss (SNHL). However, the specific causal relationship between DM and SNHL remains partially uncertain. This study aimed to investigate the causal relationship between DM and the risk of SNHL using a Mendelian randomization (MR) study. Single nucleotide polymorphisms closely related to DM were selected as instrumental variables using open genome-wide association study datasets. Three methods based on inverse variance weighted were utilized to investigate the causal relationship between DM and SNHL. Subsequently, multivariable MR (MVMR) was executed to adjust for confounding genetic associations. In addition, a range of sensitivity analyses were performed to assess the stability and reliability of the MR results. The inverse variance weighted analysis indicated a potential genetic causality between DM and SNHL (odds ratio [OR]: 2.179; 95% confidence interval [CI]: 1.123-4.231; P = .021). The sensitivity analyses showed that the included single nucleotide polymorphisms had no heterogeneity, horizontal pleiotropy, and outliers (P > .05). Moreover, the leave-one-out method further verified the robustness of the MR analysis results. Finally, the results of the MVMR study predicted that there was a genetic causal relationship between type 1 DM and SNHL (OR: 1.032; 95%CI: 1.018-1.047; P = 5.45 × 10-6), while there was no causality between type 2 DM and SNHL (OR: 1.000; 95%CI: 0.958-1.036; P = .853). Our study suggested that DM and type 1 DM may be genetically responsible for SNHL. Although our study did not detect a genetic causal relationship between type 2 DM and SNHL, this does not rule out a relationship between them at other mechanistic levels. Further studies are required to confirm the findings and look into the physiological and pathological mechanism underlying these relationships.

Causal effects of Parkinson's disease on the risk of osteoporosis: A two-sample Mendelian randomization study.

Employing a two-sample Mendelian randomization (MR) analysis, we aimed to investigate the potential causal effect of Parkinson disease (PD) on osteoporosis. We conducted an in-depth MR analysis by leveraging extensive genome-wide association study datasets from the International Parkinson Disease Genomics Consortium and the Genetic Factors for Osteoporosis Consortium. We meticulously selected instrumental variables based on strict criteria, including significance thresholds, linkage disequilibrium, and the exclusion of confounding single-nucleotide polymorphisms. Our investigation utilized diverse MR methods, including inverse variance weighted, MR Egger regression, weighted median, and MR-PRESSO, to robustly evaluate the causal relationship. Our comprehensive analysis revealed noteworthy associations between PD and distinct measures of bone mineral density (BMD) (forearm BMD, femoral neck BMD, lumbar spine BMD). Specifically, the inverse variance weighted method underscored potential significant relationships between PD and forearm BMD (P = .037; odds ratio [OR], 1.04; 95% confidence interval [CI], 1.00-1.09), femoral neck BMD (P = .034; OR, 1.02; 95% CI, 1.00-1.05), and lumbar spine BMD (P = .043; OR, 1.03; 95% CI, 1.00-1.06). The consistency of results across various methods and sensitivity analyses indicated both robustness and minimal pleiotropy concerns. Through a two-sample MR approach, this study establishes a plausible causal relationship between PD and decreased BMD. The outcomes underscore the urgency of targeted interventions to mitigate bone loss and manage osteoporosis in individuals with PD.

Exploring the mediating role of immune cells in the pathogenesis of IgA nephropathy through the inflammatory axis of gut microbiota from a genomic perspective.

IgA nephropathy (IgAN) is a chronic glomerular disease characterized by the deposition of IgA antibodies in the kidney's mesangium. Its pathogenesis involves genetic, immune, and environmental factors, particularly within the mucosal immune system and gut microbiota. Immune cells play a central role in mediating these processes, which this study investigates using Mendelian Randomization (MR) to explore causal relationships among gut microbiota, inflammatory markers, blood cells, and immune cells in IgAN pathogenesis. We conducted a two-sample MR analysis using Genome-Wide Association Study (GWAS) summary data to assess the causal effects of gut microbiota, inflammatory markers, and blood cell traits on IgAN. Data sources included the FinnGen dataset for IgAN and relevant GWAS datasets for immune traits, blood cells, and inflammatory markers. Inverse variance weighting (IVW) was the primary MR method, supported by sensitivity analyses. We particularly examined the mediation effect of immune cells on these exposures' influence on IgAN. Significant associations were found between several factors and IgAN. Gut microbiota traits, such as Firmicutes E and Sporomusales, increased IgAN risk, while Citrobacter A and Herbinix reduced it. Inflammatory markers, including Interleukin-10 and Fibroblast Growth Factor 23, promoted IgAN onset. Blood cell traits like red blood cell perturbation response increased risk, while monocyte perturbation response was protective. Immune traits played a key mediating role, with Transitional %B cells reducing IgAN risk and CD28- CD25 + + CD8br %T cells increasing it. This study highlights the pivotal mediating role of immune cells in the interactions between gut microbiota, inflammatory markers, and IgAN risk. These findings identify potential biomarkers and therapeutic targets, providing new insights into the immune mechanisms underlying IgAN and opportunities for intervention.

Genome-wide association studies of ischemic stroke based on interpretable machine learning

Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied. These methods were applied to real genotypic data of sick and healthy individuals of European ancestry obtained from the Database of Genotypes and Phenotypes (5,581 individuals, 883,749 single nucleotide polymorphisms). Finally, 131 genes were identified as candidates for association with the onset of IS. UBQLN1, TRPS1, and MUSK were previously described as associated with the course of IS in model animals. ACOT11 taking part in metabolism of fatty acids was shown for the first time to be associated with IS. The identified genes were compared with genes from the Illuminating Druggable Genome project. The product of GPR26 representing the G-coupled protein receptor can be considered as a therapeutic target for stroke prevention. The approaches presented in this research can be used to reprocess GWAS datasets from other diseases.

Regulatory T cells-related gene in primary sclerosing cholangitis: evidence from Mendelian randomization and transcriptome data.

The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) verified that compared to control, Akap10, Basp1, Dennd3, Plxnc1, and Tmco3 were significantly up-regulated in the PSC mice model yet the expression level of Klf13, and Scap was significantly lower. Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the hub Tregs-related gene with M2 macrophage, neutrophils, megakaryocyte-erythroid progenitor (MEP), natural killer T cell (NKT), and enrichment scores of the autophagic cell death, complement and coagulation cascades, metabolic disturbance, Fc gamma R-mediated phagocytosis, mitochondrial dysfunction, potentially mediating PSC onset. XGBoost algorithm and SHapley Additive exPlanations (SHAP) identified AKAP10 and KLF13 as optimal genes, which may be an important target for PSC.

Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.

Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.

Genetic associations of metabolic factors and therapeutic drug targets with polycystic ovary syndrome

IntroductionPolycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiology-based therapies are lacking. ObjectivesTo comprehensively identify the metabolic causal factors and potential drug targets for PCOS. MethodsThis genetic association study was conducted using bidirectional two-sample Mendelian Randomization (MR), multivariable MR (MVMR) and drug-target MR. Considering metabolic sexual dimorphism, female-specific genome-wide association studies (GWASs) for metabolic factors were obtained. To ensure the robustness of the findings, an additional independent PCOS GWAS dataset was utilized for replication. ResultsThe PCOS cohort included 10,074 PCOS cases (mean age 28 to 45 years) and 103,164 controls (mean age 27 to 60 years) of European ancestry. All participants were female. Employing two-sample MR analysis, we found that genetically proxied body mass index (BMI) (OR = 3.40 [95 % CI, 2.65–4.36]), triglyceride (TG) (OR = 1.54 [95 % CI, 1.17–2.04]), low-density lipoprotein cholesterol (LDL-c) (OR = 1.37 [95 % CI, 1.07–1.76]), and type 2 diabetes (T2D) (OR = 1.24 [95 % CI, 1.09–1.41]) were significantly associated with an increased risk of PCOS, whereas genetically predicted high-density lipoprotein cholesterol (HDL-c) (OR = 0.61 [95 % CI, 0.47–0.80]) decreased the odds of PCOS. Stepwise MVMR established a hierarchy of interactions among these metabolic factors, identifying BMI and HDL-c as the most prominent causal factors. Notably, drug-target MR analysis identified incretin-based therapeutics, PCSK9 inhibitors, LPL gene therapy, sulfonylureas, and thiazolidinediones as potential therapeutics for PCOS. All these findings were validated in an independent dataset. ConclusionThis study offered insights into the roles of obesity, diabetes, and dyslipidemia in PCOS etiology and therapeutics, underscoring the necessity for managing metabolic health in women and paving the way for tailored therapeutic strategies for PCOS based on its metabolic underpinnings.

Genetically predicted inflammatory cytokine levels and risk of retinitis pigmentosa

ABSTRACT Purpose This study aims to estimate the potential causal relationship between genetically predicted levels of inflammatory cytokine and retinitis pigmentosa (RP) by performing Mendelian randomization (MR). Methods Single nucleotide polymorphisms (SNPs) were identified as instrumental variables (IVs) from publicly available genome-wide association study datasets. Inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode methods were applied in this MR analysis. IVW and MR-Egger were used to confirm heterogeneity and pleiotropy of identified IVs. Leave-one-SNP-out analysis was used to identify SNPs with potential impact. Results IVW results revealed that elevated levels of Tumor Necrosis Factor Alpha (TNF-α), Macrophage Inflammatory Protein-1a (MIP1a), and Monokine Induced by Gamma Interferon (MIG) were associated with higher RP risk (OR = 2.358, p = 0.050; OR = 2.583, p = 0.013; OR = 1.851, p = 0.015), while elevated levels of Interleukin-16 (IL-16) were associated with reduced RP risk (OR = 0.723, p = 0.019). The results of heterogeneity and pleiotropy (p > 0.05) confirmed there was no pleiotropy and heterogeneity in our IVW analysis. The association of TNF-α, MIP1a, MIG and IL-16 with RP from sensitivity analyses using these two sets of restricted IVs remained stable. Conclusion Our study provides evidence of potential causal relationships between several circulating cytokine levels and RP. Elevated levels of TNF-α, MIP1a, and MIG are associated with a higher risk of RP, while elevated levels of IL-16 are associated with a lower risk of RP. These cytokines may be novel biomarkers and therapeutic targets for RP.

Causal linkage of Graves’ disease with aging: Mendelian randomization analysis of telomere length and age-related phenotypes

BackgroundAging is an irreversible progressive decline in physical function. Graves’ disease (GD) is a common cause of hyperthyroidism and is characterized by elevated levels of the thyroid hormone (TH). High TH levels are associated with aging and a shortened lifespan. The causal relationship between GD and aging has yet to be investigated.MethodsWe used genome-wide association study (GWAS) datasets and Mendelian randomization (MR) analysis to explore the causal link between GD and aging. To assess the statistical power of instrumental variables (IVs), F-statistics and R2 were used. MR analysis was conducted using inverse-variance weighting (IVW), MR-Egger, weighted median, and weighted mode. The odds ratio (OR) and 95% CI were calculated to estimate the relative risk of GD to the outcomes. The Cochran Q test, I2, MR-PRESSO test, and MR-Egger regression intercept were calculated using statistical and leave-one-out analyses to test the heterogeneity, horizontal pleiotropy, and stability of the IVs on the outcomes.ResultsF-statistics of the five IVs were greater than 10, and the R2 values ranged from 0.033 to 0.156 (R2 > 0.01). According to the results of the IVW analysis, GD had no causal effect on facial aging (p = 0.189), age-related macular degeneration (p = 0.346), and Alzheimer’s disease (p = 0.479). There was a causal effect of GD on the remaining outcomes: telomere length (TL) (OR = 0.982; 95%CI:0.969–0.994; p = 0.004), senile cataract (OR = 1.031; 95%CI:1.002–1.060; p = 0.033), age-related hearing impairment (OR = 1.009; 95%CI:1.004–1.014; p = 0.001), chronic obstructive pulmonary disease (COPD) (OR = 1.055; 95%CI:1.008–1.103; p = 0.020), and sarcopenia (OR = 1.027; 95%CI:1.009–1.046; p = 0.004).ConclusionsGD accelerates the occurrence of age-related phenotypes including TL, senile cataracts, age-related hearing impairment, COPD, and sarcopenia. In contrast, there are no causal linkages between GD and facial aging, age-related macular degeneration, or Alzheimer’s disease. Further experimental studies could be conducted to elucidate the mechanisms by which GD facilitates aging, which could help slow down the progress of aging.

Causal association of serum calcium, phosphate, vitamin D, parathyroid hormone, and FGF23 with the risk of aortic stenosis

Abstract Aim Disorders of mineral metabolism, including elevated levels of serum calcium, phosphate, 25-hydroxyvitamin D (25OH-VitD), parathyroid hormone (PTH), and fibroblast growth factor 23 (FGF23), have been reported in patients with calcific aortic valve stenosis (CAVS). However, evidence of the causal role of mineral metabolism in CAVS is still lacking. We aimed to investigate the causality between mineral metabolism and CAVS. Methods A systematic pipeline combining Mendelian randomization (MR), Steiger directionality test, colocalization analysis, protein-protein network, and enrichment analysis was applied to investigate the causal effect. Genome-wide association study (GWAS) and protein quantitative trait loci data for mineral metabolism markers were extracted from large-scale meta-analyses. Summary statistics for CAVS were obtained from two independent GWAS datasets as discovery and replication cohorts (n=374,277 and 653,867). Results In MR analysis, genetic mimicry of serum FGF23 elevation was associated with increased CAVS risk [ORdiscovery=3.081 (1.649-5.760), Pdiscovery=4.21×10-4; ORreplication=2.280 (1.461-3.558), Preplication=2.82×10-4] without evidence of reverse causation (Psteiger=7.21×10-98). Strong colocalisation association with CAVS was observed for FGF23 expression in the blood (PP.H4 = 0.96). Additionally, we identified some protein-protein interactions between FGF23 and known CAVS causative genes. Serum calcium, phosphate, 25OH-VitD, and PTH failed to show causal effects on CAVS at Bonferroni-corrected significance (all P&amp;gt;0.05/5=0.01). Conclusions Elevated serum FGF23 level is a causal risk factor for CAVS, and its mechanism of action in CAVS development may be independent of its function in regulating mineral metabolism. Hence, FGF23 may serve as a circulating marker and a promising preventive target for CAVS, warranting further investigation.

Genetic evidence of bidirectional mendelian randomization study on the causality between gut microbiome and respiratory diseases contributes to gut-lung axis

Observational studies and clinical trials have suggested the relationship between the gut microbiome and respiratory diseases, but the causality between them remains unclear. Firstly, we selected eight respiratory diseases Genome-wide association study (GWAS) datasets mainly from the FinnGen collaboration as outcomes. The exposure was based on GWAS statistics about the gut microbiome, sourced from the MiBioGen consortium, including gut microbial taxa. The causal link between the gut microbiome and respiratory illnesses was then estimated using a Two-sample Mendelian randomization (MR) analysis, including the inverse-variance weighted (IVW), weighted median, MR-Egger, simple mode, and weighted mode. To ensure reliability, F-statistics and sensitivity tests were conducted. Furthermore, we performed a reverse MR analysis of the pre-Mendelian positive findings to possible reverse causality. For the 196 gut microbe taxa, the IVW analysis suggested 88 potential associations with eight clinically prevalent respiratory diseases. Among them, 30 causal associations were found in more than one MR method. Multiple statistical corrections have confirmed three causal associations: genus Holdemanella was a risk factor for chronic obstructive pulmonary disease (COPD) (P = 1.3 × 10−4, OR = 1.18), family FamilyXIII was a protective factor for COPD (P = 1.3 × 10−3, OR = 0.75), and genus Oxalobacter was a risk factor for asthma (P = 2.1 × 10−4, OR = 1.09). Our MR analysis results indicate that there would be a causal relationship between the gut microbiome and respiratory diseases, contributing to the gut-lung axis. This finding offers new insights into the gut microbiome’s roles in respiratory diseases’ clinical prevention, pathogenesis, and improvement of clinical symptoms. Further randomized controlled trials are necessary to clarify the protective effect of probiotics and fecal microbial transplantation on respiratory health.

Cysteine cathepsins and autoimmune diseases: A bidirectional Mendelian randomization.

Cysteine cathepsins are proteolytic enzymes crucial in various physiological and pathological processes, primarily operating within lysosomes. Their functions include protein degradation, immune system regulation, and involvement in various diseases. While some cysteine cathepsins play important roles in the immune system, their connection to autoimmune diseases remains unclear. This study proposes using Mendelian randomization to explore the causal relationship between cysteine cathepsins and autoimmune diseases. Single nucleotide polymorphisms (SNPs) for cysteine cathepsins were obtained from a publicly available genome-wide association study (GWAS) dataset, while outcome SNP data were sourced from 10 separate GWAS datasets. Mendelian randomization (MR) analysis employed the Wald ratio (WR) and inverse variance weighted (IVW) approach as primary methods, supplemented by the weighted median and MR-Egger methods. Heterogeneity was assessed using Cochran Q test, and sensitivity analysis was conducted using the MR-PRESSO method. The association strength between exposure and outcome was evaluated using odds ratios (OR) with 95% confidence intervals (CI). The study identified a potential positive correlation between elevated cathepsin B and psoriasis (Wald ratio OR = 1.449, 95% CI: 1.053-1.993, P = .0227). Elevated cathepsin F was potentially linked to ulcerative colitis (WR OR = 1.073, 95% CI: 1.021-1.127, P = .0056), ankylosing spondylitis (WR OR = 1.258, 95% CI: 1.082-1.463, P = .0029), and primary biliary cholangitis(PBC) (WR OR = 1.958, 95% CI: 1.326-2.889, P = .0007). Conversely, cathepsin H appeared protective against celiac disease (WR OR = 0.881, 95% CI: 0.838-0.926, P = 6.5e-7), though elevated levels may increase the risk of type 1 diabetes (IVW OR = 1.121, 95% CI: 1.053-1.194, P = .0003) and PBC (WR OR = 1.792, 95% CI: 1.062-3.024, P = .0288). Cathepsin Z was also associated with an increased risk of type 1 diabetes (IVW OR = 1.090, 95% CI: 1.006-1.181, P = .0349). The MR analysis suggests potential risks of cathepsin B with psoriasis, cathepsin F with ulcerative colitis, ankylosing spondylitis, and PBC, and cathepsin Z with type 1 diabetes. Conversely, cathepsin H may protect against celiac disease but could increase the risk of type 1 diabetes and PBC.

Integrative Multi-Omics Approach for Improving Causal Gene Identification.

Transcriptome-wide association studies (TWAS) have been widely used to identify thousands of likely causal genes for diseases and complex traits using predicted expression models. However, most existing TWAS methods rely on gene expression alone and overlook other regulatory mechanisms of gene expression, including DNA methylation and splicing, that contribute to the genetic basis of these complex traits and diseases. Here we introduce a multi-omics method that integrates gene expression, DNA methylation, and splicing data to improve the identification of associated genes with our traits of interest. Through simulations and by analyzing genome-wide association study (GWAS) summary statistics for 24 complex traits, we show that our integrated method, which leverages these complementary omics biomarkers, achieves higher statistical power, and improves the accuracy of likely causal gene identification in blood tissues over individual omics methods. Finally, we apply our integrated model to a lung cancer GWAS data set, demonstrating the integrated models improved identification of prioritized genes for lung cancer risk.

Association between dried fruit intake and kidney function: research from univariate and multivariate Mendelian randomized studies.

Observational studies have identified an association between dried fruit intake and kidney function. However, these studies have limitations such as vulnerability to confounders and reverse causality bias. Therefore, this study aimed to explore the potential causal relationship between dried fruit intake and kidney function. A two-sample Mendelian randomization (MR) study was conducted using a large-scale genome-wide association study dataset to investigate the causal relationship between dried fruit intake and kidney function markers (blood urea nitrogen (BUN), creatinine (CR), uric acid (UA), cystatin C (CyC), hematuria, microalbuminuria). The main analytical method was inverse variance weighting. In addition, we applied the MR Egger and weighted median to assess the robustness of the results. Finally, Multivariate Mendelian randomization (MVMR) was used to estimate the direct effect of dried fruit intake on kidney function markers. The univariate MR analysis showed that increased dried fruit intake was associated with lower kidney function markers, including BUN (β: -0.171, 95% confidence interval (CI): -0.239 to -0.102, p = 1.063 × 10-6), CR (β: -0.205, 95% CI: -0.311 to -0.099, p = 1.455 × 10-4), UA (β = -0.317, 95% CI: -0.384 to -0.249, p = 4.439 × 10-20), and CysC (β = -0.323, 95% CI: -0.384 to -0.249, p = 1.074 × 10-11); however, it was unrelated to hematuria and microalbuminuria. Causality persisted after performing MVMR analysis; however, with the addition of alcohol consumption and smoking as exposure factors, the causality for UA (β = -0.296, 95% CI: -0.523 to -0.068, p = 1.094 × 10-2) and CysC (β = -0.238, 95% CI: -0.465 to -0.011, p = 4.024× 10-2) weakened, while the causality for BUN (β = -0.038, 95% CI: -0.215 to 0.138, p = 6.698 × 10-1) and CR (β = -0.038, 95% CI: -0.431 to 0.046, p = 1.347 × 10-1) disappeared. Increased dried fruit intake was associated with lower kidney function markers (BUN, CR, UA, and CysC) in the absence of smoking and alcohol consumption; however, the causal relationship between dried fruit intake and BUN and CR disappeared in the presence of smoking and alcohol consumption. These results provide a promising avenue for delaying the course of chronic kidney disease.