Summary Datasets Research Articles

Bidirectional two-sample Mendelian randomization study of atrial fibrillation and breast cancer

BackgroundObservational studies have shown an association between Breast Cancer (BC) and Atrial Fibrillation (AF). However, due to confounding factors and reverse causality, the causal role between BC and AF remains unclear. In this study, bidirectional two-sample Mendelian randomization (MR) combined with meta-analysis was used to evaluate the causal association between BC and AF.MethodsBased on the Genome-Wide Association Studies (GWAS) summary data sets, the Inverse variance weighted (IVW) method was used as the main method, the weighted median method and MR-Egger method were used for Bidirectional Two-Sample Mendelian Randomization, and the Egger intercept test was used to detect horizontal pleiotropy. Heterogeneity was tested by Cochran's Q test, and sensitivity analysis was performed by “leave-one-out”. GWAS data for AF and BC were obtained from three separate databases (FinnGen, UKBiobank, GWAScatalog) for European individuals. Finally, meta-analysis was performed on the MR Analysis results from different databases.ResultsThe pooled IVW results showed no evidence of an effect of BC on the risk of AF (IVW: OR = 0.9994; 95% CI = 0.9967–1.0022). There was also no evidence of an effect of AF on BC risk (IVW: OR = 0.9970; 95% CI = 0.9154–1.0859).ConclusionThe results of the Bidirectional Two-Sample Mendelian Randomization study show that there is no causal relationship between BC and AF.

Association between human herpesviruses infections and childhood neurodevelopmental disorders: insights from two-sample mendelian randomization analyses and systematic review with meta-analysis

BackgroundThe potential roles of viral infections in neurodevelopmental disorders (NDDs) have been suggested based on previous studies. Given the high prevalence of human herpesviruses (HHVs), the associations between HHVs infection and the risk of NDDs warrant explored.MethodsOur study employs a two-sample Mendelian randomization (MR) analysis and systematic review with meta-analysis to investigate whether genetically predicted HHVs infection are linked to three main childhood NDDs—autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and Tourette syndrome (TS). We utilized genetic variants associated with HHV infections in genome-wide association study (GWAS) summary datasets of European populations to establish instrumental variables and statistics for three NDDs obtained from Psychiatric Genomics Consortium. MR analysis was performed using inverse-variance weighted, MR Egger, weighted median, simple median, weighted mode, and MR-PRESSO. In addition, publications associating HHVs infection with three NDDs were systematically searched using PubMed, Web of Science, and three Chinese databases for meta-analyses.ResultsThe MR results found no evidence to support a link between genetically predicted HHVs infection and the risk of NDDs based on existing datasets. Twenty-seven observational studies on children with HHVs infection and NDDs were considered eligible. Meta-analysis showed that cytomegalovirus and HHV-6 infection were related with ASD, while Epstein-Barr virus and cytomegalovirus infection were associated with TD in Chinese population. Conclusions: These results contribute to a comprehensive understanding of the possibilities underlying HHV infections in affecting childhood NDDs. Further research is necessary to include larger and more robust statistics of HHV infections and NDDs.Trial registrationThis systematic review was registered at PROSPERO as CRD42024554169. Retrospectively registered 26 July 2024.

Mendelian randomization analysis of causal and druggable circulating inflammatory proteins in schizophrenia.

Schizophrenia (SZ) is a severe mental disorder with complex origins. Observational studies suggested that inflammatory factors may play a role in the pathophysiology of SZ and we aim to investigate the potential genetic connection between them by examining the causal impact of circulating inflammatory proteins on SZ. We utilized Mendelian randomization (MR) analysis to assess the causal relationship between circulating inflammatory proteins and SZ and the GWAS summary datasets were sourced from public databases. The SZ dataset comprised 74,776 cases and 101,023 controls, while the summary results for 91 plasma proteins in 14,824 participants were obtained through the Olink Target platform. Moreover, to identify and evaluate potential drug targets, we searched the Drug-Gene Interaction Database (DGIdb). The results of the MR study confirmed that nine inflammatory proteins had a causal effect on SZ. Among these proteins, IL1A (OR: 0.93), TNFB (OR: 0.94), TNFSF14 (OR: 0.96), and CD40 (OR: 0.95) exhibited protective effects against SZ. Conversely, CCL23 (OR: 1.04), CCL19 (OR: 1.04), 4EBP1 (OR: 1.06), TWEAK (OR: 1.08), and DNER (OR: 1.10) were associated with an increased risk of SZ. The MR-Egger and weighted median methods also supported the direction of these effects. According to the Gene-Drug analysis, LTA, IL1A, CD40, and 4EBP1 can serve as drug targets. Our study established causal relationships between circulating inflammatory proteins and SZ. It may be beneficial to personalize the treatment of SZ by incorporating inflammation management into the treatment regimen.

Causal association between gastrointestinal diseases and coronary artery disease: a bidirectional Mendelian randomization study.

Coronary artery disease (CAD) has been a dominating reason of mortality globally due to its complexity of etiology. A variety of gastrointestinal disorders (GDs) have been accounted to be related to CAD. Thus, this study aims to determine their causal relationship by two-sample Mendelian randomization (MR) analysis. Single-nucleotide polymorphisms (SNPs) relevant to 22 GDs were employed as instrumental variables from the genome-wide association summary (GWAS) datasets. Genetic associations with CAD and HF were acquired from UK Biobank, FinnGen, and other GWAS studies. We conducted a univariable MR (UVMR) analysis followed by a meta-analysis. A multivariable MR (MVMR) analysis was then performed with smoking and body mass index (BMI) as justifications. Also, a bi-directional MR analysis was leveraged to verify the reverse causal correlations. Generally, UVMR analyses separately observed the causal effects of GDs on CAD and HF. Genetic liability to gastroesophageal reflux disease displayed a positive association with both CAD (OR=1.19; 95%CI: 1.01-1.41) and HF (OR=1.22; 95%CI: 1.00-1.49) risk; genetic liability to celiac disease separately attributed to CAD (OR=1.02; 95%CI: 1.01-1.03) and HF (OR=1.01; 95%CI: 1.00-1.02), which also maintained after MVMR analysis. Besides, we observed mutually causal associations between CAD and celiac disease. Our work suggested that genetic susceptibility to some GDs might causally increase the risk of CAD and HF, emphasizing the importance of preventing CAD in patients with GDs.

Causal association between blood metabolites and vertigo: A Mendelian randomization study.

Metabolic disorders have been identified as an important factor causing nervous system diseases. However, due to the interference of confounding factors, the causal relationship between them has not been clearly elucidated, so it is necessary to study the causal relationship between them. To explore the causal relationship between blood metabolites and vertigo by Mendelian randomization. To assess causality, the inverse variance weighting method was employed as the primary analytical approach, complemented by additional sensitivity analyses. Metabolic pathway enrichment analysis and genetic correlation analysis were employed to further assess the metabolites. All statistical analyses were conducted using the R software. The study employed metabolite Genome Wide Association Study and vertigo diseases summary data sets to examine the causal relationship between 486 blood metabolites and 3 types of vertigo. A total of 55 potential metabolites associated with the 3 types of vertigo were identified, with 22, 16, and 13 candidate metabolites showing relatively reliable MR Evidence for Vestibular Dysfunction, Peripheral Vertigo, and Central Vertigo, respectively. Enrichment analysis was conducted to investigate the biological significance of these candidate metabolites, resulting in the identification of 7 key metabolic pathways across the 3 diseases, the metabolic pathway known as "Valine, leucine, and isoleucine biosynthesis" was found to be associated with all 3 types of vertigo, suggesting its potential influence on the vestibular system. Genetic correlation analysis revealed a genetic correlation between X-10510 and dodecanedioate with Vestibular Dysfunction. This study offers novel perspectives on the causal impact of blood metabolites on vertigo through the integration of genomics and metabolomics. Identifying metabolites that contribute to vertigo could serve as potential biomarkers and contribute to a better understanding of the underlying biological mechanisms associated with vertigo.

The causal effect of telomere length on the risk of malignant lymphoma: A Mendelian randomization study.

Telomere length (TL) has been implicated in the risk assessment of numerous cancers in observational studies. Nevertheless, the relationship between TL and malignant lymphoma remains unclear, displaying inconsistent patterns across different studies. A summary dataset for genome-wide association study of TL and malignant lymphoma was acquired from the OpenGWAS website. An extensive 2-sample Mendelian randomization (MR) analysis was performed, encompassing various methodologies such as MR-Egger, weighted median, weighted mode, simple mode, and the primary method of inverse-variance weighting (IVW). Sensitivity evaluations were performed using the Cochran Q test, MR-Egger regression, and leave-one-out analysis. The main method IVW revealed that TL substantially increased the risk of Hodgkin lymphoma (HL; odds ratio [OR] = 2.135; 95% confidence interval [CI] = 1.181-3.859; P = .012). Both the IVW and weighted median methods indicated statistical associations between genetically predicted TL and other types of non-HL (OR = 1.671, 95% CI = 1.009-2.768, P = .045; OR = 2.310, 95% CI = 1.033-5.169, P = .042). However, there was no association between TL and diffuse large B-cell lymphoma, follicular lymphoma, or mature T/natural Killer-cell lymphoma, and sensitivity analysis revealed no heterogeneity or horizontal pleiotropy, indicating that the causal effect was robust. Our study shows that TL plays different roles in different types of lymphomas. A longer TL significantly increases the risk of HL and other types of non-HL.

Exploring the relationship between the interleukin family and lung adenocarcinoma through Mendelian randomization and RNA sequencing analysis

BackgroundLung adenocarcinoma (LUAD) is still one of the most prevalent malignancies. Interleukin factors are closely associated with the initiation and progression of cancer. However, the relationship between interleukin factors and LUAD has not been fully elucidated. This study aimed to use Mendelian randomization (MR) and RNA sequencing (RNA-seq) analyses to identify the interleukin factors associated with the onset and progression of LUAD.MethodsExposure-related instrumental variables were selected from interleukin factor summary datasets. The LUAD summary dataset from FINGENE served as the outcome. MR and sensitivity analyses were conducted to screen for interleukin factors associated with LUAD occurrence. Transcriptome analyses revealed the role of interleukin factors in lung tissues. The results were validated through Western blotting and further confirmed with driver gene-negative patients from multiple centers. Potential mechanisms influencing LUAD occurrence and development were explored using bulk RNA-seq and single-cell RNA-seq data.ResultsMR analysis indicated that elevated plasma levels of IL6RB, IL27RA, IL22RA1, and IL16 are causally associated with increased LUAD risk, while IL18R1 and IL11RA exhibit the opposite effect. Transcriptome analyses revealed that IL11RA, IL18R1, and IL16 were downregulated in tumor tissues compared with normal lung tissue, but only higher expression of IL11RA correlated with improved prognosis in patients with LUAD from different centers and persisted even in driver-gene negative patients. The IL11RA protein level was lower in various LUAD cell lines than in human bronchial epithelial cells. The genes co-expressed with IL11RA were enriched in the Ras signaling pathway and glycosylation processes. Fibroblasts were the primary IL11RA-expressing cell population, with IL11RA+fibroblasts exhibiting a more immature state. The genes differentially expressed between IL11RA+and IL11RA- fibroblasts were involved in the PI3K-Akt/TNF signaling pathway.ConclusionAccording to the MR and transcriptome analyses, the downregulation of IL11RA was closely related to the occurrence and development of LUAD.Graphical

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood

BackgroundOsteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes.MethodsA proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data.ResultsThe abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA.ConclusionOur study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Trait imputation enhances nonlinear genetic prediction for some traits.

The expansive collection of genetic and phenotypic data within biobanks offers an unprecedented opportunity for biomedical research. However, the frequent occurrence of missing phenotypes presents a significant barrier to fully leveraging this potential. In our target application, on one hand, we have only a small and complete dataset with both genotypes and phenotypes to build a genetic prediction model, commonly called a polygenic (risk) score (PGS or PRS); on the other hand, we have a large dataset of genotypes (e.g. from a biobank) without the phenotype of interest. Our goal is to leverage the large dataset of genotypes (but without the phenotype) and a separate genome-wide association studies summary dataset of the phenotype to impute the phenotypes, which are then used as an individual-level dataset, along with the small complete dataset, to build a nonlinear model as PGS. More specifically, we trained some nonlinear models to 7 imputed and observed phenotypes from the UK Biobank data. We then trained an ensemble model to integrate these models for each trait, resulting in higher R2 values in prediction than using only the small complete (observed) dataset. Additionally, for 2 of the 7 traits, we observed that the nonlinear model trained with the imputed traits had higher R2 than using the imputed traits directly as the PGS, while for the remaining 5 traits, no improvement was found. These findings demonstrate the potential of leveraging existing genetic data and accounting for nonlinear genetic relationships to improve prediction accuracy for some traits.

The causal relationship between CSF metabolites and GBM: a two-sample mendelian randomization analysis

BackgroundGlioblastoma multiforme (GBM) is a highly aggressive primary malignant brain tumor characterized by rapid progression, poor prognosis, and high mortality rates. Understanding the relationship between cerebrospinal fluid (CSF) metabolites and GBM is crucial for identifying potential biomarkers and pathways involved in the pathogenesis of this devastating disease.MethodsIn this study, Mendelian randomization (MR) analysis was employed to investigate the causal relationship between 338 CSF metabolites and GBM. The data for metabolites were obtained from a genome-wide association study summary dataset based on 291 individuals, and the GBM data was derived from FinnGen included 91 cases and 174,006 controls of European descent. The Inverse Variance Weighted method was utilized to estimate the causal effects. Supplementary comprehensive assessments of causal effects between CSF metabolites and GBM were conducted using MR-Egger regression, Weighted Median, Simple Mode, and Weighted Mode methods. Additionally, tests for heterogeneity and pleiotropy were performed.ResultsThrough MR analysis, a total of 12 identified metabolites and 2 with unknown chemical properties were found to have a causal relationship with GBM. 1-palmitoyl-2-stearoyl-gpc (16:0/18:0), 7-alpha-hydroxy-3-oxo-4-cholestenoate, Alpha-tocopherol, Behenoyl sphingomyelin (d18:1/22:0), Cysteinylglycine, Maleate, Uracil, Valine, X-12,101, X-12,104 and Butyrate (4:0) are associated with an increased risk of GBM. N1-methylinosine, Stachydrine and Succinylcarnitine (c4-dc) are associated with decreased GBM risk.ConclusionIn conclusion, this study sheds light on the intricate interplay between CSF metabolites and GBM, offering novel perspectives on disease mechanisms and potential treatment avenues. By elucidating the role of CSF metabolites in GBM pathogenesis, this research contributes to the advancement of diagnostic capabilities and targeted therapeutic interventions for this aggressive brain tumor. Further exploration of these findings may lead to improved management strategies and better outcomes for patients with GBM.

Association of serum uric acid with male sexual hormones and erectile dysfunction: a bidirectional 2-sample Mendelian randomization analysis.

Observational studies indicated that serum uric acid (SUA) was associated with male sexual hormones and erectile dysfunction (ED). However, their relationship was still heterogeneous. This study conducted 2-sample univariate mendelian randomization (UVMR) and multivariate mendelian randomization (MVMR) to explore the causal relationship between SUA and sexual hormones as well as ED. Genetic variants associated with SUA were derived from the UK Biobank database (N = 437 354). Outcomes from the IEU Open GWAS and summary data sets were sexual hormones (sex hormone-binding globulin [SHBG], testosterone, estradiol [E2], follicle-stimulating hormone, luteinizing hormone) and ED, with 3301 to 625 650 participants. UVMR analysis primarily utilized the inverse variance weighted method, complemented by MVMR analysis. Thorough sensitivity analyses were carried out to ensure the reliability of results. Moreover, mediation analysis was conducted to estimate the mediated effect between SUA and outcomes. The primary outcomes included results of UVMR and MVMR analysis and mediation analysis, along with sensitivity analyses involving the Cochran Q test, the MR Egger intercept test, leave-1-out analysis, and the MR-PRESSO method (mendelian randomization pleiotropy residual sum and outlier). UVMR analysis revealed that an elevated SUA level could decrease levels of SHBG (β = -0.10, P = 1.70 × 10-7) and testosterone (β = -0.10, P = 5.94 × 10-3) and had a positive causal effect on ED (odds ratio,1.10; P = .018). According to reverse mendelian randomization results, increased levels of SHBG (β = -0.06, P = 4.82 × 10-4) and E2 (β = -0.04, P = .037) could also reduce SUA levels. As shown by MVMR analysis, SUA had a negative effect on SHBG and testosterone levels (P <.05), while the significant causal relationship between SUA and ED disappeared. Furthermore, SHBG mediated 98.1% of the effect of SUA on testosterone levels. Results of other mendelian randomization analyses were not statistically significant. No pleiotropy was found by sensitivity analysis in this study. Given the causal relationship between SUA and sexual hormones, we must focus on SUA and E2 levels in men, especially patients with hypogonadism and ED. This study evaluated the causal effect of SUA on male sexual hormones and ED genetically for the first time, clarifying the common biases in observational studies and confirming the negative relationship between SUA and testosterone level. Limitations include a population based on European ancestry, some crossover of the samples, and unobserved confounding factors. Genetic studies provide evidence for the causal relationship between SUA and male sexual hormones (SHBG, testosterone, E2), while the relationship between SUA and ED should be further evaluated.

Causality between migraine and cardiovascular disease: a bidirectional Mendelian randomization study

BackgroundWhile growing evidence suggests a relationship between migraine and cardiovascular disease, the genetic evidence for a causal relationship between migraine and cardiovascular disease is still scarce. Investigating the causal association between migraine and cardiovascular disease is vital.MethodsWe carried out a bidirectional Mendelian randomization (MR) study including discovery samples and replication samples using publicly available genome-wide association study (GWAS) summary datasets and stringent screening instrumental variables. Four different MR techniques—Inverse variance weighted (IVW), MR ‒Egger, weighted median, and weighted mode—as well as various sensitivity analyses—Cochran’s Q, IVW radial, leave-one-out (LOO), and MR-PRESSO—were utilized to investigate the causal relationship between cardiovascular disease and migraine.ResultsThe protective causal effects of genetically predicted migraine on coronary artery disease (OR, 0.881; 95% CI 0.790–0.982; p = 0.023) and ischemic stroke (OR, 0.912; 95% CI 0.854–0.974; p = 0.006) were detected in forward MR analysis but not in any other cardiovascular disease. Consistently, we also discovered protective causal effects of coronary atherosclerosis (OR, 0.865; 95% CI 0.797–0.940; p = 0.001) and myocardial infarction (OR, 0.798; 95% CI 0.668–0.952; p = 0.012) on migraine in reverse MR analysis.ConclusionWe found a potential protective effect of migraine on coronary artery disease and ischemic stroke and a potential protective effect of coronary atherosclerosis and myocardial infarction on migraine. We emphasised epidemiological and genetic differences and the need for long-term safety monitoring of migraine medications and future research to improve cardiovascular outcomes in migraine patients.

DMSeqNet-mBART: A state-of-the-art Adaptive-DropMessage enhanced mBART architecture for superior Chinese short news text summarization

Mandarin Chinese, a globally prevalent language, boasts an abundance of regularly refreshed short news texts accessible online. Consequently, devising concise summaries of these texts has emerged as a pivotal challenge for enhancing information dissemination and comprehension efficiency. To tackle this issue, we introduce DMSeqNet-mBART, an innovative model grounded in the mBART framework, positioning it as a state-of-the-art solution for Chinese short news summarization. DMSeqNet-mBART incorporates the Adaptive-DropMessage technique, an innovative methodology that intelligently discards or retains information contingent upon the attention mechanism’s output. Furthermore, the model integrates several enhanced technologies, such as dynamic convolutional layers, gated residual connections, customized feed-forward networks enhanced with batch normalization, self-attention, and cross-attention, all aimed at bolstering the performance and robustness of Chinese short news summarization. Rigorous comparative experiments, conducted across six recognized Chinese short news summary datasets, demonstrate that DMSeqNet-mBART significantly surpasses industry-leading models like T5, MLC, PLCC, and GPT-4 in terms of fluency, completeness, robustness, and accuracy. These results, as validated by benchmarks including BERTScore, BLEU, and ROUGE metrics, underscore the model’s superiority across diverse evaluation standards.

Exploring the genetic and socioeconomic interplay between ADHD and anxiety disorders using Mendelian randomization.

ADHD and anxiety disorders often co-occur, sharing symptoms and dysfunctions, yet the underlying mechanisms remain elusive. To explore the shared and distinct genetic variations between ADHD and anxiety disorders, we applied Mendelian randomization (MR) analysis to ADHD, anxiety disorders, and three socioeconomic factors: income, educational attainment (EA), and intelligence. MR analysis utilized genome-wide association study summary datasets (anxiety disorder: 7,016 cases and 14,745 controls; ADHD: 38,691 cases and 275,986 controls; EA: 766,345 participants; intelligence: 146,808 participants; household income: 392,422 participants), with inverse-variance weighting as the primary method. Our MR analysis revealed no discernible genetic-level causal effect between ADHD and anxiety disorders (p > 0.77). Additionally, the independent variables for ADHD (25 SNPs) and anxiety disorders (18 SNPs) did not overlap, highlighting the genetic distinction between the two conditions. Higher income (p < 0.002) and EA (p < 0.005) were found to serve as protective factors for both ADHD and anxiety disorders. Genetic predisposition to higher income (86 SNPs) and EA (457 SNPs) were identified as a potential common protective factors for both conditions. Lastly, genetic predisposition to higher intelligence was found to potentially guard against ADHD (p < 0.001) but not against anxiety disorders (p > 0.55). Our findings indicate that the shared symptoms observed between ADHD and anxiety disorders are more likely influenced by genetic predispositions related to socioeconomic factors rather than by the genetic predispositions specific to the disorders themselves.

ADSumm: annotated ground-truth summary datasets for disaster tweet summarization

ADSumm: annotated ground-truth summary datasets for disaster tweet summarization

Genetic predisposition to milder forms of COVID-19 may provide some resilience to head and neck cancers.

The impact of the COVID-19 pandemic on head and neck cancer (HNC) has been suggested, but the causal relationship remains unclear. We explore this connection by utilizing the Mendelian randomization (MR) approach applied to publicly available genome-wide association study (GWAS) summary datasets for COVID-19 and HNC. The datasets included critical COVID-19 (13,769 cases, 1,072,442 controls), hospitalized COVID-19 (32,519 cases, 2,062,805 controls), SARS-CoV-2 infection (122,616 cases, 2,475,240 controls), and HNC (2,131 cases, 287,137 controls). Mechanistic underpinnings of the causal relationships identified by MR analysis were explored through functional annotation augmented by AI-based literature data mining. Surprisingly, a genetic predisposition to contracting a milder form of COVID-19 substantially reduced the risks of developing HNC (OR: 0.52, 95% CI: 0.35-0.78, p = 1.42E-03), with no significant association between genetic liability to severe COVID-19 and the risk of HNC detected. Additionally, our findings highlighted 14 genes linked to SARS-CoV-2 infection, potentially playing a protective role in the context of HNC. These genes include OAS1, LOC107985887, BCL11A, DPP9, LOC107984685, LINC02326, MUC4, NXPE3, IFNAR2, LZTFL1, LOC105372437, NAPSA, LOC105376622, LOC107986082, and SLC6A20. Our study emphasizes the protective role of the genetic liability to milder COVID-19 in reducing the risk of HNC while refuting a causal relationship between severe COVID-19 and HNC.

Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study.

Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis. Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results. We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings. In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA<PSNP & PMETA<PBMI) results for YPEL3 in nucleus accumbens and NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in liver. The identified genes help link the genetic variation at obesity risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.

Causal effect of immune cells on idiopathic pulmonary fibrosis: A mendelian randomization study

BackgroundA key component of idiopathic pulmonary fibrosis (IPF) is the involvement of immune cells. However, the causal interaction between different immune cell signatures and IPF remain inconclusive. ObjectivesBased on publicly accessible data, our study utilized the Mendelian randomization (MR) approach to determine the causative relevance of complex immune cell phenotypes in IPF. MethodsWe deployed a two-sample Mendelian randomization approach to evaluate the causal interaction between immune cell markers and IPF. All data regarding 731 immune signatures and IPF were acquired from two genome-wide association studies (GWAS) that are accessible to the public. The original study adopted the inverse variance weighted (IVW) method, followed by sensitivity analyses aimed at eliminating heterogeneity and pleiotropy. Additionally, Multivariate Mendelian randomization (MVMR) was utilized to identify the independent risk factors in our study. ResultsThe summary dataset for IPF was accessed from the Finnish Genetic Consortium R9, comprising 2018 patients and 373,064 controls. And the dataset for immune signatures was conducted in 3,757 Sardinian individuals. By conducting IVW and extensive sensitivity analyses, univariate Mendelian randomization (UVMR) identified one immunophenotype that remained causally associated with IPF after false discovery rate (FDR) correction: CD39 on CD39+ CD8+T cells (odd ratio [OR] = 0.850, 95 % confidence interval [CI] = 0.787–0.918, P = 3.68 × 10−5). The causal association with IPF was further validated using MVMR. ConclusionsBased on rigorous MR analysis methods and FDR correction, our study demonstrated that CD39 on CD39+ CD8+T cells showed a protective effect against IPF, providing effective insights for preventing and diagnosing pulmonary fibrosis.

Correlation and mechanism between chronic kidney disease and periodontitis based on cross-sectional data

Objective: To explore the correlation between periodontitis (PD) and chronic kidney disease (CKD) in adults, as well as the potential mechanisms involved. Methods: Data on PD and CKD from the National Health and Nutrition Examination Survey (NHANES) database between 1999 and 2014 were downloaded. Weighted univariate and multivariate logistic regression analyses were conducted to investigate the risk factors associated with PD and CKD, considering demographic and clinical indicators. Using publicly available genome-wide association study (GWAS) summary datasets for CKD and PD as outcome variables, as well as 731 immune cell phenotypes and 91 inflammatory proteins as exposure factors from the OPEN GWAS database, a two-sample Mendelian randomization (TSMR) analysis was performed using the inverse-variance weighted (IVW) method. Results: Seven demographic indicators including gender, age, race, education level, marital status, income, and health are related to the incidence of CKD and PD. Among them, the elderly (≥60 years old), poverty (poverty-income ratio <1.3), divorce or widowhood, and male ratio in the comorbidity group of CKD and PD [67.12% (833/1 241), 36.83% (457/1 241), 34.41% (427/1 241), and 57.78% (717/1 241) respectively] were significantly higher than those in the control group [23.71% (4 179/17 623), 29.17% (5 141/17 623), 18.16% (3 200/17 623), and 48.73% (8 587/17 623) respectively] (all P<0.001). Those with high educational level (university and above) and self-rated excellent health accounted for a relatively small proportion in the comorbidity group [14.10% (175/1 241) and 8.22% (102/1 241) respectively]. The prevalence of PD increased among individuals with abnormal renal function indices, including glomerular filtration rate, urine protein/creatinine ratio, serum creatinine, serum uric acid, and blood urea nitrogen. Univariate logistic regression analysis showed a positive correlation between the incidence of PD and CKD (OR=2.14, 95%CI: 1.90-2.42, P<0.001). Multivariate logistic regression analysis also indicated that PD and CKD were potential risk factors for each other (PD for CKD: OR=1.22, 95%CI: 1.07-1.40, P=0.004; CKD for PD: OR=1.19, 95%CI: 1.04-1.37, P=0.012). Furthermore, after adjusting the model based on demographic indicators, there was still a significant correlation between PD and CKD (P=0.010). Mechanistically, the results of the TSMR analysis support the existence of a common risk factor mediated by immune cells between CKD and PD, namely the expression of CD64 on multiple innate immune cells mediates the occurrence of CKD and PD. The absolute count of CD64+ monocytes is associated with an increased risk for both CKD (HR=1.11) and PD (HR=1.07), while same tendency showed in the absolute count of CD64+ neutrophils for CKD (HR=1.22) and PD (HR=1.23). Conclusions: There is a positive correlation between CKD and PD, particularly moderate to severe PD, and the shared pathogenesis involves CD64+ monocytes in the circulatory system. Targeted interventions focusing on CD64 molecules or monocyte subsets may be beneficial.