Genome-wide Association Studies Datasets Research Articles

Causal relationship between C-reactive protein and ischemic stroke caused by atherosclerosis: A Mendelian randomization study

ObjectivesThis study investigates the association between C-reactive protein (CRP) and ischemic stroke caused by large artery atherosclerosis (LAA). MethodsFive Mendelian Randomization (MR) methodologies were used for two-sample analyses: Inverse Variance Weighting (IVW), MR-Egger regression, Weighted Median (WM), Simple Mode, and Weighted Mode. CRP exposure data were obtained from aggregated summary statistics from a meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry (n = 343,524; UK Biobank). Stroke data were used as the outcome, with specific dataset details for relevant subtypes (cases = 40,585, controls = 406,111). ResultsIn the CRP GWAS dataset, selected single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) showed genome-wide significance and a causal relationship with CRP, particularly in relation to LAA stroke. IVW indicated a robust causal connection between CRP and LAA stroke (Beta = 0.151, SE = 0.055, P = 0.006). The WM approach supported this relationship (Beta = 0.176, SE = 0.082, P = 0.033). However, MR-Egger regression suggested a potential absence of a causal link (Beta = 0.098, SE = 0.077, P = 0.206), with minimal influence from horizontal pleiotropy (Intercept = 0.0029; P = 0.317). The Simple mode found no significant association (Beta = 0.046, SE = 0.217, P = 0.834), while the Weighted mode revealed a significant causal association (Beta = 0.138, SE = 0.059, P = 0.020) between CRP and LAA stroke. ConclusionsMR analysis provides evidence for a potential causal relationship between CRP and an increased risk of LAA stroke.

An atlas of causal association between micronutrients and osteoarthritis

ObjectiveThis study examines the causal relationships between serum micronutrients and site-specific osteoarthritis (OA) using Mendelian Randomization (MR). MethodsThis study performed a two-sample MR analysis to explore causal links between 21 micronutrients and 11 OA outcomes. These outcomes encompass overall OA, seven site-specific manifestations, and three joint replacement subtypes. Sensitivity analyses using MR methods, such as the weighted median, MR-Egger, and MR-PRESSO, assessed potential horizontal pleiotropy and heterogeneity. Genome-wide association summary statistical data were utilized for both exposure and outcome data, including up to 826,690 participants with 177,517 OA cases. All data was sourced from Genome-wide association studies datasets from 2009 to 2023. ResultsIn the analysis of associations between 21 micronutrients and 11 OA outcomes, 15 showed Bonferroni-corrected significance (P < 0.000216), without significant heterogeneity or horizontal pleiotropy. Key findings include strong links between gamma-tocopherol and spine OA (OR = 1.70), and folate with hand OA in finger joints (OR = 1.15). For joint replacements, calcium showed a notable association with a reduced likelihood of total knee replacement (TKR) (OR = 0.52) and total joint replacement (TJR) (OR = 0.56). Serum iron was significantly associated with an increased risk of total hip replacement (THR) (OR = 1.23), while folate indicated a protective effect (OR = 0.95). Various sex-specific associations were also uncovered. ConclusionThese findings underscore the critical role of micronutrients in osteoarthritis, providing valuable insights for preventive care and potential enhancement of treatment outcomes.

Identification of novel genetic loci associated with major depressive disorder and the hippocampus in a European population using the condFDR method

To identify additional loci associated with depression and the hippocampus (HIP) through genome-wide association study. The depression-related genome-wide association study (GWAS) meta summary data was downloaded from the official website of the Psychiatric Genomics Consortium, which had involved 170 756 cases and 329 443 controls. The left and right hippocampal volume GWAS data sets were downloaded from the UK Biobank, which involved 33 224 participants. The conditional false discovery rate (condFDR) was used to identify novel genetic loci for depression and left and right hippocampal volumes, and a conjunctional false discovery rate (conjFDR) was used to evaluate the enrichment of pleiotropic loci between depression and left and right hippocampal volumes. Respectively, 7, 13, and 12 new loci have been associated with depression, left hippocampal volume and right hippocampal volume, with a significant threshold of condFDR < 0.01. A site of rs1267073 locus was found to be shared by the depression and right hippocampal volume with a threshold of conjFDR < 0.01. Above findings have provided more insights into the genetic mechanisms underlying the volume of hippocampus and the risk for depression. The results may also provide evidence for future clinical trials for treating depression.

Genetically predicted 1091 blood metabolites and 309 metabolite ratios in relation to risk of type 2 diabetes: a Mendelian randomization study.

Metabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM. Data encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran's Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0. The IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899-0.962, p = 2.16 × 10-5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917-0.968, p = 1.64 × 10-5), Mannose (IVW: OR:1.133, 95% CI: 1.072-1.197, p = 1.02 × 10-5), X-21829 (IVW: OR:1.036, 95% CI: 1.036-1.122, p = 9.44 × 10-5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818-0.926, p = 1.29 × 10-5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism. This study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located 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-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

PiRNA PROPER Suppresses DUSP1 Translation by Targeting N6-Methyladenosine-Mediated RNA Circularization to Promote Oncogenesis of Prostate Cancer.

Genetic and epigenetic alterations occur in many physiological and pathological processes. The existing knowledge regarding the association of PIWI-interacting RNAs (piRNAs) and their genetic variants on risk and progression of prostate cancer (PCa) is limited. In this study, three genome-wide association study datasets are combined, including 85,707 PCa cases and 166,247 controls, to uncover genetic variants in piRNAs. Functional investigations involved manipulating piRNA expression in cellular and mouse models to study its oncogenetic role in PCa. A specific genetic variant, rs17201241 is identified, associated with increased expression of PROPER (piRNA overexpressed in prostate cancer) in tumors and are located within the gene, conferring an increased risk and malignant progression of PCa. Mechanistically, PROPER coupled with YTHDF2 to recognize N6-methyladenosine (m6A) and facilitated RNA-binding protein interactions between EIF2S3 at 5'-untranslated region (UTR) and YTHDF2/YBX3 at 3'-UTR to promote DUSP1 circularization. This m6A-dependent mRNA-looping pattern enhanced DUSP1 degradation and inhibited DUSP1 translation, ultimately reducing DUSP1 expression and promoting PCa metastasis via the p38 mitogen-activated protein kinase (MAPK) signaling pathway. Inhibition of PROPER expression using antagoPROPER effectively suppressed xenograft growth, suggesting its potential as a therapeutic target. Thus, targeting piRNA PROPER-mediated genetic and epigenetic fine control is a promising strategy for the concurrent prevention and treatment of PCa.

O-136 A comprehensive evaluation of spermatogenesis-related genes identifies novel common and rare risk loci for idiopathic severe spermatogenic failure

Abstract Study question Can genome-wide genotyping data be analysed in a hypothesis-driven fashion to better understand the genetic component of male infertility due to severe spermatogenic failure (SPGF)? Summary answer We revealed that both common and rare genetic variants in genomic regions involved in spermatogenesis contribute to the development of idiopathic SPGF. What is known already Spermatogenesis is a tightly regulated process involving the controlled expression of over 2,000 genes. Mutations in spermatogenesis-related genes have been associated with SPGF development. Next-generation sequencing methods are useful for identifying rare mutations that explain monogenic forms of this condition. Additionally, genome-wide association studies (GWAS) have provided valuable insights into the role of common polymorphisms in complex forms of SPGF. Interestingly, novel methods have shown that GWAS datasets can be used to infer rare coding variants that are causal for male infertility phenotypes, but this approach has never been applied to characterise the genetic component of a whole case-control cohort. Study design, size, duration We conducted a thorough examination of both common (minor allele frequency, MAF &amp;gt; 0.01) and rare (MAF &amp;lt; 0.01) genetic variation within a set of 1,797 spermatogenesis genes. This gene panel was meticulously curated through an exhaustive search in the literature and in different databases of male infertility genetics. The genotype data were obtained from a previously published GWAS in Europeans, encompassing over 6 million polymorphisms. Participants/materials, setting, methods We analysed a cohort that included 1,274 SPGF patients and 1,951 unaffected controls with European ancestry. Three independent approaches were follow: (1) variant-wise analysis using logistic regression models followed by a meta-analysis of the study cohorts; (2) gene-wise analysis using a Combined Multivariate and Collapsing (CMC) burden test; and (3) identification and characterisation of highly damaging rare coding variants in homozygosity. Multiple testing correction was applied to determine statistical significance Main results and the role of chance Our results suggested a potential association between SPGF and the rs12347237*T variant of the SHOC1 gene in the GWAS population (P = 3.17E-05, OR = 2.94). This association was subsequently validated in an independent Iberian cohort (Pcombined = 6.992E-06, OR = 2.61). Furthermore, our gene-wise analysis showed putative associations through the cumulative effect of some rare and low-frequency variants in SHOC1 (P = 4.9E-03), PCSK4 (P = 1E-04), AP3B1 (P = 5E-04), and DLK1 (P = 5E-04). Remarkably, the rare and common variants identified in SHOC1 are part of the same haplotype block, impacting both coding and non-coding regions. Additionally, we identified 35 individuals carrying 32 very rare and potentially pathogenic variants in homozygosity within the coding regions of the selected genes. Limitations, reasons for caution The analysis of low-frequency variants presents challenges in achieving sufficient statistical power to detect genetic associations. Consequently, independent studies involving large sample sets are essential to replicate our findings. In addition, the specific role of the identified variants in the pathogenic mechanisms of SPGF should be assessed using functional experiments. Wider implications of the findings The discovery of novel genetic risk factors for SPGF and the elucidation of the genetic causes underlying both monogenic and complex forms of SPGF provide new perspectives for personalized medicine and reproductive counselling. Trial registration number not applicable

Association between biological ageing and periodontitis: Evidence from a cross-sectional survey and multi-omics Mendelian randomization analysis.

To investigate the relationship and potential causality between biological ageing and periodontitis. We obtained the National Health and Nutrition Examination Survey (NHANES) and genome-wide association study (GWAS) summary statistics as well as single-cell sequencing data. Multivariate regression analysis based on cross-sectional data, Mendelian randomization (MR) and multi-omics integration analysis were employed to explore the causal association and potential molecular mechanisms between biological ageing and periodontitis. Additionally, two-step MR mediation analysis explored the risk factors in biological ageing-mediated periodontitis. We analysed data from 3189 participants in the NHANES data and found that higher biological age was associated with increased risk of periodontitis. MR analyses revealed causal associations between biological age measures and periodontitis risk. Frailty (odds ratio [OR] = 2.08, 95% confidence interval [CI]: 1.04-4.18, p = .039) and GrimAge acceleration (OR = 1.16, 95% CI: 1.01-1.32, p = .033) were causally associated with periodontitis risk, and these results were validated in a large-scale meta-periodontitis GWAS dataset. Additionally, the risk effects of body mass index, waist circumference and lifetime smoking on periodontitis were partially mediated by frailty and GrimAge acceleration. Evidence from cross-sectional survey and MR analysis suggests that biological ageing increases the risk of periodontitis. Additionally, improving the associated risk factors can help prevent both ageing and periodontitis.

Mendelian randomization analysis of psoriasis and psoriatic arthritis associated with risks of ulcerative colitis.

This study is designed to explore the potential causal relationship between psoriasis and psoriatic arthritis (PsA) while investigating the genetic basis shared by these inflammatory diseases. Significant single nucleotide polymorphisms (SNPs) associated with UC, psoriasis, and PsA were selected as genetic instrumental variables using Genome-Wide Association Study (GWAS) datasets. Additionally, Mendelian randomization (MR) methods, including inverse-variance weighting (IVW), MR-Egger regression, and Weighted Median (WME), were utilized to evaluate the causal relationships between these diseases. Moreover, sensitivity analysis and heterogeneity testing were conducted to validate the stability of the results. A total of 123 significant SNPs associated with psoriasis, PsA, and UC were identified as genetic instrumental variables based on GWAS datasets. The analysis revealed a 36% increased risk of UC with psoriasis (odds ratio [OR]=1.350, 95% confidence interval [CI]=1.065-1.729, P=0.012) and a 32.9% increased risk of UC with PsA (OR=1.329, 95% CI=1.176-1.592, P<0.001). Further analysis showed a 43.5% increased risk of psoriasis with UC (OR=1.435, 95% CI=1.274-1.831, P<0.001) and a 45.8% increased risk of PsA with UC (OR=1.458, 95% CI=1.166-1.822, P=0.0013). In addition, sensitivity analysis and heterogeneity testing demonstrated the high stability of these results. Particularly, neither MR-Egger regression analysis nor leave-one-out analysis revealed significant heterogeneity or pleiotropy bias, indicating the reliability of these causal estimates. Moreover, the use of the MR-PRESSO further confirmed the positive correlation between psoriasis and UC, and the corrected estimates remained consistent with IVW analysis results after excluding potential outlier SNPs, enhancing the credibility of the analysis. This study strengthens the understanding of the genetic and causal relationships among UC, psoriasis, and PsA through GWAS and MR methods, revealing the genetic basis they may share. These findings not only provide a novel perspective on the comorbidity mechanisms of these diseases but also offer a valuable reference for the development of future treatment strategies and intervention measures.

Causal relationship and shared genes between air pollutants and amyotrophic lateral sclerosis: A large-scale genetic analysis.

Air pollutants have been reported to have a potential relationship with amyotrophic lateral sclerosis (ALS). The causality and underlying mechanism remained unknown despite several existing observational studies. We aimed to investigate the potential causality between air pollutants (PM2.5, NOX, and NO2) and the risk of ALS and elucidate the underlying mechanisms associated with this relationship. The data utilized in our study were obtained from publicly available genome-wide association study data sets, in which single nucleotide polymorphisms (SNPs) were employed as the instrumental variantswith three principles. Two-sample Mendelian randomization and transcriptome-wide association (TWAS) analyses were conducted to evaluate the effects of air pollutants on ALS and identify genes associated with both pollutants and ALS, followed by regulatory network prediction. We observed that exposure to a high level of PM2.5 (OR: 2.40 [95% CI: 1.26-4.57], p = 7.46E-3) and NOx (OR: 2.35 [95% CI: 1.32-4.17], p = 3.65E-3) genetically increased the incidence of ALS in MR analysis, while the effects of NO2 showed a similar trend but without sufficient significance. In the TWAS analysis, TMEM175 and USP35 turned out to be the genes shared between PM2.5 and ALS in the same direction. Higher exposure to PM2.5 and NOX might causally increase the risk of ALS. Avoiding exposure to air pollutants and air cleaning might be necessary for ALS prevention.

Risk Factors for Pelvic Organ Prolapse: Wide-Angled Mendelian Randomization Analysis.

We hypothesized that some metabolic factors, lifestyle factors, and socioeconomic factors may have a causal effect on pelvic organ prolapse (POP). We selected instruments from corresponding genome-wide association studies (GWAS), which identified independent single nucleotide polymorphisms strongly associated with 12 potential risk factors. Summary statistics for POP were derived from two GWAS datasets, serving for discovery and replication stage. The primary analysis involved the use of the inverse-variance weighting mendelian randomization (MR) method, with additional sensitivity MR analyses conducted. The univariable mendelian randomization (UVMR) analysis in both the discovery and replication stage provided evidence for significant causal effects between higher waist-to-hip ratio adjusted for body mass index (WHRadjBMI) levels, lower high-density lipoprotein cholesterol (HDL-C) levels, and lower educational attainment and higher POP risk, as well as a suggestive positive causal effect between triglycerides and POP. The multivariable mendelian randomization (MVMR)analysis showed that only HDL-C among the three blood lipid fractions could reduce the risk of POP. Mediation analysis indicated that HDL-C may partially mediate the effect of WHRadjBMI on POP risk, and the causal effect between educational attainment and POP may be mediated through WHRadjBMI and HDL-C. Our study's evidence supported a causal relationship between WHRadjBMI, triglycerides, HDL-C, educational attainment, and POP risk. This highlights that clinicians may guide the general female population to control obesity and blood lipid levels to reduce the risk of POP.

Causal relationship between thyroid dysfunction and sepsis: a bidirectional two-sample Mendelian randomization

To explore the causal relationship between thyroid dysfunction and sepsis based on the bidirectional two-sample Mendelian randomization (MR) method. The genome-wide association study (GWAS) dataset were selected to screen single nucleotide polymorphisms (SNP) associated with thyroid dysfunction as instrumental variable (IV) for genetic variation, using hypothyroidism and hyperthyroidism as exposure factor and sepsis as outcome factor. Potential causal relationship between thyroid dysfunction and sepsis was analyzed using a bidirectional two-sample MR method primary analysis method of inverse-variance weighted (IVW). Potential pleiotropic analysis of SNP was performed using the MR Egger regression intercept test. Sensitivity analysis was performed using the "leave one out" test. Reverse MR method was used to prove the causal relationship. The GWAS data were screened based on the three main assumptions of MR, resulting in 101 SNP strongly associated with hypothyroidism and 10 SNP strongly associated with hyperthyroidism entering the MR analysis. The results of the MR using the IVW method showed that the risk of sepsis in individuals with hypothyroidism was 2.293 times higher than those without hypothyroidism [odds ratio (OR) = 2.293, 95% confidence interval (95%CI) was 1.199-4.382, P = 0.012]. There was no significant difference in the risk of sepsis between hyperthyroid and non-hyperthyroid populations (OR = 1.049, 95%CI was 0.999-1.100, P = 0.560). MR Egger regression intercept test showed that the included SNP did not have pleiotropy, and the MR-PRESSO test did not find outliers. Sensitivity analysis suggested that the results of MR were stable. The results of the reverse MR analysis showed that the reverse causal relationship between hyperthyroidism and sepsis was not proved (OR = 0.996, 95%CI was 0.988-1.004, P = 0.338), which further confirmed the robust MR analysis result. The results of the bidirectional two-sample MR analysis show that hypothyroidism can increase the risk of sepsis onset, while there is no causal relationship between hyperthyroidism and sepsis.

Asthma and risk of adverse pregnancy outcomes: A Mendelian randomization study

BackgroundMultiple empirical investigations have indicated a connection between asthma and adverse pregnancy outcomes (APOs). Nevertheless, the effects of asthma on APOs remain uncertain. MethodsWe performed bi-directional Univariable Mendelian randomization (UVMR) analyses using combined information obtained from genome-wide association studies (GWAS) data that is publicly accessible. The principal approach used to analyze the causal association between asthma or age when diagnosed and APOs was the inverse variance weighted (IVW) method. The two types of data regarding exposure originate from the IEU Open GWAS project, which includes 56,167 and 47,222 European asthma patients, respectively. The data of four APOs were acquired via the GWAS dataset of the FinnGen collaboration. In addition, we implemented multivariable Mendelian randomization (MVMR), controlling for confounding factors such as smoking status, frequent drinking, body mass index (BMI), and live birth quantity. Furthermore, we executed several meticulous sensitivity studies to ascertain the reliability of our MR results. ResultsFollowing the implementation of the Bonferroni adjustment, the UVMR assessment revealed that in the IVW model, asthma was significantly linked to an elevated risk of spontaneous abortion (SA) (odds ratio [OR]: 1.115; 95 % confidence interval [CI]: 1.031–1.206; P = 0.006) and gestational diabetes mellitus (GDM) (OR: 1.125; 95 % CI: 1.037–1.220; P = 0.005). However, there was no causal correlation between asthma and preterm birth (PTB) (OR: 0.979; 95 % CI: 0.897–1.068; P = 0.629) or preeclampsia (PE) (OR: 1.059; 95 % CI: 0.951–1.179; P = 0.297). After adjusting for confounding factors, including smoking status, frequent drinking, BMI, and live birth quantity, the MVMR analysis shows a statistically significant causal relationship between asthma and SA or GDM. Furthermore, our investigation's findings did not reveal a substantial correlation between the age of asthma onset based on genetics and the likelihood of SA or GDM. The inverse MR outcomes indicate a lack of causal connection linking APOs to the incidence of asthma. The validity of these findings were verified by sensitivity analyses. ConclusionsThe evidence provided by this study proves that genetically determined asthma is linked to a higher likelihood of SA and GDM. Further research is required to examine potential pathways. However, no conclusive evidence has been found to support the increased risk of SA and GDM in early asthma diagnosis or the interaction between asthma and PTB or PE, indicating that confounding factors may affect the results of previous observational studies.

Physical activity and the osteoarthritis of the knee: A Mendelian randomization study.

Previous studies have reported an association between physical activity and the occurrence and progression of knee osteoarthritis (KOA). However, the existing evidence remains limited and of low-quality. This study aimed to examine the causal relationship between different levels of physical activity and KOA. Instrumental variables, represented by single nucleotide polymorphisms (SNPs), were utilized to capture sedentary behavior, appropriate physical exercise, and excessive physical activity. Aggregated statistics from the UK Biobank genome-wide association study dataset were used to assess the impact of these SNPs on KOA. Causality was estimated using inverse variance weighting (IVW), MR Egger, simple model, weighted median, and weighted model approaches. The stability of the results was assessed through heterogeneity and sensitivity analyses. Mendelian randomization (MR) analysis revealed a strong association between sedentary behavior and KOA, with an odds ratio (OR) of 2.096 (95% CI: 1.506-2.917) and a P value of 1.14 × 10-5. Appropriate physical exercise behavior exhibited a strong negative association with KOA, with an OR of 0.147 (95% CI: 0.037-0.582) and a P value of 0.006. Conversely, excessive physical activity behavior showed a significant positive association with KOA, with an OR of 2.162 (95% CI: 1.327-3.521) and a P value of .002. Our findings indicate that sedentary behavior and excessive physical activity are identified as risk factors for KOA, whereas engaging in appropriate physical exercise emerges as a protective factor against the development of KOA.

Causal association between plasma metabolites and neurodegenerative diseases

BackgroundEstablishing causal relationships between metabolic biomarkers and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is a challenge faced by observational studies. In this study, our aim was to investigate the causal associations between plasma metabolites and neurodegenerative diseases using Mendelian Randomization (MR) methods. MethodsWe utilized genetic associations with 1400 plasma metabolic traits as exposures. We used large-scale genome-wide association study (GWAS) summary statistics for AD and PD as our discovery datasets. For validation, we performed repeated analyses using different GWAS datasets. The main statistical method employed was inverse variance-weighted (IVW). We also conducted enrichment pathway analysis for IVW-identified metabolites. ResultsIn the discovered dataset, there are a total of 69 metabolites (36 negatively, 33 positively) potentially associated with AD, and 47 metabolites (24 negatively， 23 positively) potentially associated with PD. Among these, 4 significant metabolites overlap with significant metabolites (PIVW < 0.05)in the validation dataset for AD, and 1 metabolite overlaps with significant metabolites in the validation dataset for PD. Three metabolites serve as common potential metabolic markers for both AD and PD, including Tryptophan betaine, Palmitoleoylcarnitine (C16:1), and X-23655 levels. Further pathway enrichment analysis suggests that the SLC-mediated transmembrane transport pathway, involving tryptophan betaine and carnitine metabolites, may represent potential intervention targets for treating AD and PD. ConclusionThis study offers novel insights into the causal effects of plasma metabolites on degenerative diseases through the integration of genomics and metabolomics. The identification of metabolites and metabolic pathways linked to AD and PD enhances our comprehension of the underlying biological mechanisms and presents promising targets for future therapeutic interventions in AD and PD.

Unveiling new genetic insights in rheumatoid arthritis for drug discovery through Taxonomy3 analysis

Genetic support for a drug target has been shown to increase the probability of success in drug development, with the potential to reduce attrition in the pharmaceutical industry alongside discovering novel therapeutic targets. It is therefore important to maximise the detection of genetic associations that affect disease susceptibility. Conventional statistical methods such as genome-wide association studies (GWAS) only identify some of the genetic contribution to disease, so novel analytical approaches are required to extract additional insights. C4X Discovery has developed Taxonomy3, a unique method for analysing genetic datasets based on mathematics that is novel in drug discovery. When applied to a previously published rheumatoid arthritis GWAS dataset, Taxonomy3 identified many additional novel genetic signals associated with this autoimmune disease. Follow-up studies using tool compounds support the utility of the method in identifying novel biology and tractable drug targets with genetic support for further investigation.

Functional genetic variants of GEN1 predict overall survival of Chinese epithelial ovarian cancer patients

BackgroundInherited variations in DNA double-strand break (DSB) repair pathway are known to influence ovarian cancer occurrence, progression and treatment response. Despite its significance, survival-associated genetic variants within the DSB pathway remain underexplored.MethodsIn the present study, we performed a two-phase analysis of 19,290 single-nucleotide polymorphisms (SNPs) in 199 genes in the DSB repair pathway from a genome-wide association study (GWAS) dataset and explored their associations with overall survival (OS) in 1039 Han Chinese epithelial ovarian carcinoma (EOC) patients. After utilizing multivariate Cox regression analysis with bayesian false-discovery probability for multiple test correction, significant genetic variations were identified and subsequently underwent functional prediction and validation.ResultsWe discovered a significant association between poor overall survival and the functional variant GEN1 rs56070363 C > T (CT + TT vs. TT, adjusted hazard ratio (HR) = 2.50, P < 0.001). And the impact of GEN1 rs56070363 C > T on survival was attributed to its reduced binding affinity to hsa-miR-1287-5p and the resultant upregulation of GEN1 mRNA expression. Overexpression of GEN1 aggregated EOC cell proliferation, invasion and migration presumably by influencing the expression of immune inhibitory factors, thereby elevating the proportion of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and then constructing an immunosuppressive tumor microenvironment.ConclusionsIn conclusion, GEN1 rs56070363 variant could serve as a potential predictive biomarker and chemotherapeutic target for improving the survival of EOC patients.

Causal Relationships of Ligamentous Injuries in the Knee on Corticospinal Tract Structure: A Mendelian Randomization Analysis.

The association between ligamentous knee injuries and corticospinal tract (CST) structure has attracted attention; however, any causal relationship remains uncertain. We performed Mendelian randomization (MR) analysis to identify the causal effects of ligamentous knee injuries on the CST. Ligamentous knee injuries impair CST microstructure (ie, by reducing fractional anisotropy [FA] and increasing mean diffusivity [MD]). MR analysis. Level 2. MR uses genetic variants as instrumental variables to infer causal relationships between exposures and outcomes. Summary data for ligamentous injuries in knee and CST structure were obtained from genome-wide association study datasets. Significant and independent (5 × 10-6; r2 < 0.001; 10,000 kb) single-nucleotide polymorphisms were extracted for MR analysis. Three methods for MR analysis were used (hypothesis-driven 1-tailed inverse variance weighted, MR-Egger, and weighted median), and sensitivity analyses were conducted to test reliability and stability. Results from 3 MR methods consistently demonstrated that ligamentous knee injuries increased MD of the right CST (β, 0.063; 90% CI, 0.003-0.123; P = 0.04), and weak statistical significance suggested increased MD of the left CST (β, 0.060; 90% CI, -0.002 to -0.121; P = 0.05). However, no significant causal relationships were observed in CST FA, and no significant pleiotropy or heterogeneity was observed. Sensitivity analysis utilizing 2-tailed tests had no significant associations between ligamentous knee injuries and changes in CST structure. There is statistically weak genetic evidence that corticospinal pathway abnormalities may evolve after ligamentous knee injuries, which manifests as abnormally organized neurites. Ligamentous knee injuries require attention not only to damage to the structure of the knee joint itself but also to the process of maladaptive neuroplasticity that leads to structural and functional changes of the CST; novel interventions that target the corticospinal pathway may provide subsequent treatment of ligamentous knee injuries.

Blood metabolomic and postpartum depression: a mendelian randomization study

BackgroundPostpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).MethodsThis study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study’s clinical expert groups, which included leading experts in their respective medical fields.ResultsThe MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.ConclusionsThis study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Multi-omics analysis uncovered systemic lupus erythematosus and COVID-19 crosstalk

BackgroundStudies have highlighted a possible crosstalk between the pathogeneses of COVID-19 and systemic lupus erythematosus (SLE); however, the interactive mechanisms remain unclear. We aimed to elucidate the impact of COVID-19 on SLE using clinical information and the underlying mechanisms of both diseases.MethodsRNA-seq datasets were used to identify shared hub gene signatures between COVID-19 and SLE, while genome-wide association study datasets were used to delineate the interaction mechanisms of the key signaling pathways. Finally, single-cell RNA-seq datasets were used to determine the primary target cells expressing the shared hub genes and key signaling pathways.ResultsCOVID-19 may affect patients with SLE through hematologic involvement and exacerbated inflammatory responses. We identified 14 shared hub genes between COVID-19 and SLE that were significantly associated with interferon (IFN)-I/II. We also screened and obtained four core transcription factors related to these hub genes, confirming the regulatory role of the IFN-I/II-mediated Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway on these hub genes. Further, SLE and COVID-19 can interact via IFN-I/II and IFN-I/II receptors, promoting the levels of monokines, including interleukin (IL)-6/10, tumor necrosis factor-α, and IFN-γ, and elevating the incidence rate and risk of cytokine release syndrome. Therefore, in SLE and COVID-19, both hub genes and core TFs are enriched within monocytes/macrophages.ConclusionsThe interaction between SLE and COVID-19 promotes the activation of the IFN-I/II-triggered JAK-STAT signaling pathway in monocytes/macrophages. These findings provide a new direction and rationale for diagnosing and treating patients with SLE–COVID-19 comorbidity.