Large-scale Genetic Data Research Articles

Exploring the link between sedentary behavior and cognitive decline: a comprehensive study combining Mendelian randomization and animal model experiments.

The causal link between detrimental behaviors and cognitive performance remains unclear. This research seeks to investigate the causal impact of adjustable lifestyle factors on cognitive deterioration, including frequency of alcohol intake, onset of smoking, and sedentary activities like prolonged television viewing. This research combines large-scale genetic data obtained from univariable and multivariable Mendelian randomization analyses with experimental findings obtained from animal models. Our findings reveal that the odds ratio (OR) for cognitive function deterioration was 0.445 (inverse variance weighted [IVW] 95% CI: 0.370 to 0.536, p < 0.001) for each standard deviation increase in television watching time. After adjustment for body mass index (BMI), number of days walked /moderate activity over 10+ min and education in Multivariable Mendelian Randomization (MVMR), only the genetic predisposition to increased television watching time remained significantly associated with worse cognitive function (OR 0.659, 95% CI: 0.452 to 0.960, p = 0.030). The other two habits had no significant effects. Sensitivity analyses have confirmed that genetic pleiotropy did not influence the results. To further explore the relationship between sedentary behavior and cognitive function, as well as the underlying mechanisms, we conducted a restricted cage housing experiment and a physical exercise training experiment in mice. The results showed that physical exercise significantly improved spatial memory, as assessed by the Morris water maze, and increased exploratory interest, as evaluated by the open field test (OFT) and the elevated plus-maze test, compared to the sedentary control group. These cognitive advantages may be mediated through mechanisms involving free radical scavenging and enhanced synaptic plasticity. Our research provides genetic evidence indicating that extended television viewing is linked to an elevated risk of cognitive decline. Additionally, experimental data from mouse models suggest that physical exercise can counteract cognitive decline and anxiety-like behaviors induced by sedentary behavior. This protective effect is likely mediated by reactive oxygen species (ROS)-dependent mechanisms that enhance synaptic plasticity within the hippocampus.

Proteome- and Transcriptome-Wide Genetic Analysis Identifies Biological Pathways and Candidate Drug Targets for Preeclampsia.

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited. In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry. We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1 [sulfotransferase 1A1], SH2B3 [SH2B adapter protein 3], SERPINE2 [serpin family E member 2], RGS18 [regulator of G-protein signaling 18], PZP [pregnancy zone protein], NOTUM [notum, palmitoleoyl-protein carboxylesterase], METAP1 [methionyl aminopeptidase 1], MANEA [mannosidase endo-alpha], jun-D [JunD proto-oncogene], GDF15 [growth differentiation factor 15], FGL1 [fibrinogen like 1], FGF5 [fibroblast growth factor 5], FES [FES proto-oncogene], APOBR [apolipoprotein B receptor], ANP [natriuretic peptide A], ALDH-E2 [aldehyde dehydrogenase 2 family member], ADAMTS13 [ADAM metallopeptidase with thrombospondin type 1 motif 13], and 3MG [N-methylpurine DNA glycosylase]), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation. This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia.

Causal effects of neuroticism on postpartum depression: a bidirectional mendelian randomization study.

Postpartum depression (PPD) brings adverse and serious consequences to both new parents and newborns. Neuroticism affects PPD, which remains controversial for confounding factors and reverse causality in cross-sectional research. Therefore, mendelian randomization (MR) study has been adopted to investigate their causal relationship. This study utilized large-scale genome-wide association study genetic pooled data from three major databases: the United Kingdom Biobank, the European Bioinformatics Institute, and the FinnGen databases. The causal analysis methods used inverse variance weighting (IVW). The weighted median, MR-Egger method, MR-PRESSO test, and the leave-one-out sensitivity test have been used to examine the results' robustness, heterogeneity, and horizontal pleiotropy. The fixed effect model yielded the results of meta-analysis. In the IVW model, a meta-analysis of the MR study showed that neuroticism increased the risk of PPD (OR, 1.17; 95% CI, 1.11-1.25, p < 0.01). Reverse analysis showed that PPD could not genetically predict neuroticism. There was no significant heterogeneity or horizontal pleiotropy bias in this result. Our study suggests neuroticism is the risk factor for PPD from a gene perspective and PPD is not the risk factor for neuroticism. This finding may provide new insights into prevention and intervention strategies for PPD according to early detection of neuroticism.

Leveraging large-scale genetic data to assess the causal impact of COVID-19 on multisystemic diseases

BackgroundThe long-term impacts of COVID-19 on human health are a major concern, yet comprehensive evaluations of its effects on various health conditions are lacking.MethodsThis study aims to evaluate the role of various diseases in relation to COVID-19 by analyzing genetic data from a large-scale population over 2,000,000 individuals. A bidirectional two-sample Mendelian randomization approach was used, with exposures including COVID-19 susceptibility, hospitalization, and severity, and outcomes encompassing 86 different diseases or traits. A reverse Mendelian randomization analysis was performed to assess the impact of these diseases on COVID-19.ResultsOur analysis identified causal relationships between COVID-19 susceptibility and several conditions, including breast cancer (OR = 1.0073, 95% CI = 1.0032–1.0114, p = 5 × 10 − 4), ER + breast cancer (OR = 0.5252, 95% CI = 0.3589–0.7685, p = 9 × 10 − 4), and heart failure (OR = 1.0026, 95% CI = 1.001–1.0042, p = 0.002). COVID-19 hospitalization was causally linked to heart failure (OR = 1.0017, 95% CI = 1.0006–1.0028, p = 0.002) and Alzheimer’s disease (OR = 1.5092, 95% CI = 1.1942–1.9072, p = 0.0006). COVID-19 severity had causal effects on primary biliary cirrhosis (OR = 2.6333, 95% CI = 1.8274–3.7948, p = 2.059 × 10 − 7), celiac disease (OR = 0.0708, 95% CI = 0.0538–0.0932, p = 9.438 × 10–80), and Alzheimer’s disease (OR = 1.5092, 95% CI = 1.1942–1.9072, p = 0.0006). Reverse MR analysis indicated that rheumatoid arthritis, diabetic nephropathy, multiple sclerosis, and total testosterone (female) influence COVID-19 outcomes. We assessed heterogeneity and horizontal pleiotropy to ensure result reliability and employed the Steiger directionality test to confirm the direction of causality.ConclusionsThis study provides a comprehensive analysis of the causal relationships between COVID-19 and diverse health conditions. Our findings highlight the long-term impacts of COVID-19 on human health, emphasizing the need for continuous monitoring and targeted interventions for affected individuals. Future research should explore these relationships to develop comprehensive healthcare strategies.

Genome-Wide Identification of Cell Type-Specific Susceptibility Genes for SLE Through the Analysis of RNA Modification-Associated SNPs.

This study aimed to elucidate the functional genes associated with systemic lupus erythematosus (SLE) in various cell types through the utilization of RNAm-SNPs. Utilizing large-scale genetic data, we identified associations between RNAm-SNPs and SLE. The association between RNAm-SNPs and bulk and single-cell mRNA expression (eQTL) and protein levels (pQTL) were examined. Mendelian randomization and differential expression analyses were conducted to explore the links between gene expression, protein levels, and SLE. We identified 41 RNAm-SNPs that were significantly associated with SLE. The GWAS signals exhibited notable enrichment in m6A-SNPs and m7G-SNPs. These RNAm-SNPs showed both eQTL and pQTL effects. In our single-cell analysis, 16 RNAm-SNPs exhibited associations with gene expression levels across 13 distinct cell types, including HLA-A, HLA-B, HLA-C, HLA-DQA1, HLA-DQB1, HLA-DRB1 and IRF7. We identified 58 noteworthy associations between the expression levels of 20 genes and SLE across 12 distinct immune cell types. Notably, HLA-DQB1, HLA-DRB1 and IRF7 exhibited abnormalities in CD8+ T cells, IRF7 displayed abnormal expression in CD4+ T cells, while HLA-DRB1 and IRF7 were also distinctly perturbed in natural killer cells. This study advances our understanding of the genetic basis of SLE by highlighting the significance of RNAm-SNPs and immune cell gene expression in SLE.

Genotype Representation Graphs: Enabling Efficient Analysis of Biobank-Scale Data.

Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. Current practice is to store large-scale genetic polymorphism data using tabular data structures and file formats, where rows and columns represent samples and genetic variants. However, encoding genetic data in such formats has become unsustainable. For example, the UK Biobank polymorphism data of 200,000 phased whole genomes has exceeded 350 terabytes (TB) in Variant Call Format (VCF), cumbersome and inefficient to work with. To mitigate the computational burden, we introduce the Genotype Representation Graph (GRG), an extremely compact data structure to losslessly present phased whole-genome polymorphisms. A GRG is a fully connected hierarchical graph that exploits variant-sharing across samples, leveraging ideas inspired by Ancestral Recombination Graphs. Capturing variant-sharing in a multitree structure compresses biobank-scale human data to the point where it can fit in a typical server's RAM (5-26 gigabytes (GB) per chromosome), and enables graph-traversal algorithms to trivially reuse computed values, both of which can significantly reduce computation time. We have developed a command-line tool and a library usable via both C++ and Python for constructing and processing GRG files which scales to a million whole genomes. It takes 160GB disk space to encode the information in 200,000 UK Biobank phased whole genomes as a GRG, more than 13 times smaller than the size of compressed VCF. We show that summaries of genetic variants such as allele frequency and association effect can be computed on GRG via graph traversal that runs significantly faster than all tested alternatives, including vcf.gz, PLINK BED, tree sequence, XSI, and Savvy. Furthermore, GRG is particularly suitable for doing repeated calculations and interactive data analysis. We anticipate that GRG-based algorithms will improve the scalability of various types of computation and generally lower the cost of analyzing large genomic datasets.

Investigating Genetic Overlap between Alzheimer's Disease, Lipids, and Coronary Artery Disease: A Large-Scale Genome-Wide Cross Trait Analysis.

There is evidence to support a link between abnormal lipid metabolism and Alzheimer's disease (AD) risk. Similarly, observational studies suggest a comorbid relationship between AD and coronary artery disease (CAD). However, the intricate biological mechanisms of AD are poorly understood, and its relationship with lipids and CAD traits remains unresolved. Conflicting evidence further underscores the ongoing investigation into this research area. Here, we systematically assess the cross-trait genetic overlap of AD with 13 representative lipids (from eight classes) and seven CAD traits, leveraging robust analytical methods, well-powered large-scale genetic data, and rigorous replication testing. Our main analysis demonstrates a significant positive global genetic correlation of AD with triglycerides and all seven CAD traits assessed-angina pectoris, cardiac dysrhythmias, coronary arteriosclerosis, ischemic heart disease, myocardial infarction, non-specific chest pain, and coronary artery disease. Gene-level analyses largely reinforce these findings and highlight the genetic overlap between AD and three additional lipids: high-density lipoproteins (HDLs), low-density lipoproteins (LDLs), and total cholesterol. Moreover, we identify genome-wide significant genes (Fisher's combined p value [FCPgene] < 2.60 × 10-6) shared across AD, several lipids, and CAD traits, including WDR12, BAG6, HLA-DRA, PHB, ZNF652, APOE, APOC4, PVRL2, and TOMM40. Mendelian randomisation analysis found no evidence of a significant causal relationship between AD, lipids, and CAD traits. However, local genetic correlation analysis identifies several local pleiotropic hotspots contributing to the relationship of AD with lipids and CAD traits across chromosomes 6, 8, 17, and 19. Completing a three-way analysis, we confirm a strong genetic correlation between lipids and CAD traits-HDL and sphingomyelin demonstrate negative correlations, while LDL, triglycerides, and total cholesterol show positive correlations. These findings support genetic overlap between AD, specific lipids, and CAD traits, implicating shared but non-causal genetic susceptibility. The identified shared genes and pleiotropic hotspots are valuable targets for further investigation into AD and, potentially, its comorbidity with CAD traits.

Cholecystitis may decrease the risk of sudden death: A 2-sample Mendelian randomization study.

Some observational studies have highlighted a significant association between cholecystitis and factors leading to sudden death; however, the specific relationship between the 2 has not been fully elucidated. The primary objective of this study was to elucidate the causal interplay between cholecystitis and augmented risk of sudden cardiac death. We used large-scale genetic summary data from genome-wide association study, genetic summary statistics were sourced from 3 eminent repositories: the UK Biobank (N = 463,010), the FinnGen consortium (N = 215,027), and the European Bioinformatics Institute (N = 471,251). By employing 2-sample Mendelian randomization (MR) to decipher the causal interplay between cholecystitis and sudden death etiologies, a meta-analytical approach was employed to amalgamate the findings derived from these disparate data sources. The primary MR methodologies used included inverse variance weighting with random effects, inverse variance weighting with fixed effects, maximum likelihood, MR-Egger, and weighted median. Subsequently, we performed heterogeneity testing, polyvalency examination, and sensitivity analysis to bolster the robustness of causal relationship assessments. Meta-analysis and amalgamating variegated data sources revealed a statistically significant inverse correlation between cholecystitis and ventricular arrhythmias (odds ratio, 0.896; 95% confidence interval: 0.826-0.971; P = .008). Similarly, an inverse association was observed between cholecystitis and aortic aneurysm (odds ratio, 0.899; 95% confidence interval: 0.851-0.951, P < .001). This study substantiates the absence of a direct causal link between cholecystitis and cerebrovascular accidents (P = .771), pulmonary embolism (P = .071), and acute myocardial infarction (P = .388). A direct causal correlation existed between cholecystitis and sudden death associated with ventricular arrhythmias and aortic aneurysms. The onset of cholecystitis may mitigate the risk of sudden death due to ventricular arrhythmias and aortic aneurysms.

Unveiling blood pressure-associated genes in aortic cells through integrative analysis of GWAS and RNA modification-associated variants.

Genome-wide association studies (GWAS) have identified more than a thousand loci for blood pressure (BP). Functional genes in these loci are cell-type specific. The aim of this study was to elucidate potentially functional genes associated with BP in the aorta through the utilization of RNA modification-associated single-nucleotide polymorphisms(RNAm-SNPs). Utilizing large-scale genetic data of 757,601 individuals from the UK Biobank and International Consortium of Blood Pressureconsortium, we identified associations between RNAm-SNPs and BP. The association between RNAm-SNPs, gene expression, and BP were examined. A total of 355 RNAm-SNPs related to m6A, m1A, m5C, m7G, and A-to-I modification were associated with BP. The related genes were enriched in the pancreatic secretion pathway and renin secretion pathway. The BP GWAS signals were significantly enriched with m6A-SNPs, highlighting the potential functional relevance of m6A in physiological processes influencing BP. Notably, m6A-SNPs in CYP11B1, PDE3B, HDAC7, ACE, SLC4A7, PDE1A, FRK, MTHFR, NPPA, CACNA1D, and HDAC9 were identified. Differential methylation and differential expression of the BP genes in FTO-overexpression and METTL14-knockdown vascular smooth muscle cells were detected. RNAm-SNPs were associated with ascending and descending aorta diameter and the genes showed differential methylation between aortic dissection (AD) cases and controls. In scRNA-seq study, we identified ARID5A, HLA-DPB1, HLA-DRA, IRF1, LINC01091, MCL1, MLF1, MLXIPL, NAA16, NADK, RERG, SRM, and USP53 as differential expression genes for AD in aortic cells. The present study identified RNAm-SNPs in BP loci and elucidated the associations between the RNAm-SNPs, gene expression, and BP. The identified BP-associated genes in aortic cells were associated with AD.

OA30 Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel effector genes

Abstract Background/Aims Osteoarthritis is a complex disease with a huge public health burden. Genome-wide association studies (GWAS) have identified hundreds of osteoarthritis-associated sequence variants, but the effector genes underpinning these signals remain largely elusive. Understanding chromosome organisation in 3D space is essential for identifying long-range contacts between distant genomic features (e.g., between genes and regulatory elements), in a tissue-specific manner. Here, we generate the first whole genome chromosome conformation analysis (Hi-C) map of primary osteoarthritis chondrocytes and identify novel effector genes for the disease. Methods Primary chondrocytes collected from eight knee osteoarthritis patients underwent Hi-C analysis to link chromosomal structure to genomic sequence. The identified loops were then combined with osteoarthritis GWAS results to identify variants involved in gene regulation via enhancer-promoter interactions. Results We identified 36 genetic variants residing within chromatin loop anchors that are associated with 13 osteoarthritis GWAS signals. Five of these variants reside directly in enhancer regions of 3 enhancer-promoter loops newly described in chondrocytes, pointing to high-confidence effector genes: PAPPA, NSD2 and NCOA6. PAPPA is directly associated with the turnover of IGF-1 transport proteins, and IGF-1 is an important factor in the repair of damaged chondrocytes. Conclusion We have constructed the first Hi-C map of primary human chondrocytes and have made it available as a resource for the scientific community. By integrating 3D-genomics with large-scale genetic association data, we identify high-confidence osteoarthritis effector genes which enhance our understanding of disease and can serve as putative high-value novel targets. Disclosure N. Bittner: None. C. Shi: None. D. Zhao: None. J. Ding: None. L. Southam: None. D. Swift: None. P. Kreitmaier: None. M. Tutino: None. J. Hankinson: None. M. Wilkinson: None. G. Orozco: None. E. Zeggini: None.

The interplay between X-chromosome functional dosage and circadian regulation in females.

Biological factors and mechanisms that drive higher prevalence of insomnia in females are poorlyunderstood. This study focused on the neurological consequences of X-chromosome functional imbalancesbetween sexes. Benefited from publicly available large-scale genetic, transcriptional and epigenomic data, we curated andcontrasted different gene lists: (1) X-liked genes, including assignments for X-chromosome inactivation patterns anddisease associations; (2) sleep-associated genes; (3) gene expression markers for the suprachiasmatic nucleus. We show that X-linked markers for the suprachiasmatic nucleus are significantly enriched for clinicallyrelevant genes in the context of rare genetic syndromes and brain waves modulation. Considering female-specific patterns on brain transcriptional programs becomes essential whendesigning health care strategies for mental and sleep illnesses with sex bias in prevalence.

Botany and geogenomics: Constraining geological hypotheses in the neotropics with large-scale genetic data derived from plants.

Decades of empirical research have revealed how the geological history of our planet shaped plant evolution by establishing well-known patterns (e.g., how mountain uplift resulted in high rates of diversification and replicate radiations in montane plant taxa). This follows a traditional approach where botanical data are interpreted in light of geological events. In this synthesis, I instead describe how by integrating natural history, phylogenetics, and population genetics, botanical research can be applied alongside geology and paleontology to inform our understanding of past geological and climatic processes. This conceptual shift aligns with the goals of the emerging field of geogenomics. In the neotropics, plant geogenomics is a powerful tool for the reciprocal exploration of two long standing questions in biology and geology: how the dynamic landscape of the region came to be and how it shaped the evolution of the richest flora. Current challenges that are specific to analytical approaches for plant geogenomics are discussed. I describe the scale at which various geological questions can be addressed from biological data and what makes some groups of plants excellent model systems for geogenomics research. Although plant geogenomics is discussed with reference to the neotropics, the recommendations given here for approaches to plant geogenomics can and should be expanded to exploring long-standing questions on how the earth evolved with the use of plant DNA.

Association between body mass index and electrocardiogram indices: A Mendelian randomization study

IntroductionCardiovascular diseases (CVDs) remain a global health concern, and body mass index (BMI) is known to be associated with an increased risk of CVD, but the exact mechanisms underlying this relationship remain unclear. This study employs Mendelian randomization (MR) to investigate the causal association between BMI and electrocardiogram (ECG) indices, providing insights into potential pathways linking obesity to CVD. MethodsWe conducted a comprehensive MR study utilizing large-scale genetic and ECG data from diverse populations. Instrumental variables were selected from genome-wide association studies, ensuring their relevance to BMI. Causal relationships between BMI and ECG indices, including P wave duration, PR interval, QRS duration, and QT interval, were assessed using various MR methods, with inverse variance weighted (IVW) considered as the primary analysis. ResultsOur MR analysis revealed a significant positive causal association between higher BMI and P wave duration (β = 8.078, 95% CI: 5.322 to 10.833, p < 0.001), suggesting a potential mechanism through which higher BMI may contribute to arrhythmogenic risks. However, no significant causal associations were observed between BMI and PR interval, QRS duration, or QT interval (all p > 0.005). In addition, our study also found that there is no horizontal pleiotropy between BMI and P wave duration, PR interval, QRS duration, and QT interval, suggesting that the conclusions of this study are robust. ConclusionThis study supports a causal relationship between elevated BMI and prolonged P wave duration, a marker of increased atrial arrhythmogenic risk. Further investigations are still needed to elucidate the underlying mechanisms.

Causality-enriched epigenetic age uncouples damage and adaptation.

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

Evaluating the Causal Effect of Type 2 Diabetes on Alzheimer's Disease Using Large-Scale Genetic Data.

Alzheimer's disease (AD) has a high comorbidity with type 2 diabetes (T2D). However, there is still some controversy over whether T2D has a causal impact on AD at present. We aimed to reveal whether T2D has a causal effect on AD using large-scale genetic data. Firstly, we performed a primary two-sample Mendelian randomization (MR) analysis to assess the potential causal effects of T2D on AD. For this analysis, we used the largest available genome-wide association studies (GWAS) T2D (T2D1, including 80,154 cases and 853,816 controls) and AD (AD1, including 111,326 cases and 677,663 controls) datasets. Additionally, we performed a validation MR analysis using two largely overlapping-sample datasets from FinnGen, including T2D (T2D2, including 57,698 cases and 308,252 controls) and AD (AD2, including 13,393 cases and 363,884 controls). In all MR analyses, the inverse variance-weighted method was used as the primary analysis method, supplemented by the weighted-median and MR-Egger techniques. In the primary analysis, we found that T2D was not associated with the risk of AD (OR: 0.98, CI: 0.95-1.01, P=0.241). Similarly, no significant association was detected in the validation MR analysis (OR: 0.97, CI: 0.64-1.47, P=0.884). Our findings provide robust evidence that T2D does not have a causal impact on AD. Future studies need to further explore the effect of T2D on the non-AD components of the dementia phenotype.

Postpartum depression and autoimmune disease: a bidirectional Mendelian randomization study.

The rising prevalence of postpartum depression (PPD) is harmful to women and families. While there is a growing body of evidence suggesting an association between PPD and autoimmune diseases (ADs), the direction of causality remains uncertain. Therefore, Mendelian randomization (MR) study was employed to investigate the potential causal relationship between the two. This study utilized large-scale genome-wide association study genetic pooled data from two major databases: the IEU OpenGWAS project and the FinnGen databases. The causal analysis methods used inverse variance weighting (IVW). The weighted median, MR-Egger method, MR-PRESSO test, and the leave-one-out sensitivity test have been used to examine the results' robustness, heterogeneity, and horizontal pleiotropy. A total of 23 ADs were investigated in this study. In the IVW model, the MR study showed that PPD increased the risk of type 1 diabetes (OR , = 1.15 (1.05-1.26),p<0.01),Hashimoto's thyroiditis((OR) = 1.21 (1.09-1.34),p<0.0001),encephalitis((OR) = 1.66 (1.06-2.60),p<0.05). Reverse analysis showed that ADs could not genetically PPD. There was no significant heterogeneity or horizontal pleiotropy bias in this result. Our study suggests that PPD is a risk factor for type 1 diabetes, Hashimoto's thyroiditis, and encephalitis from a gene perspective, while ADs are not a risk factor for PPD. This finding may provide new insights into prevention and intervention strategies for ADs according to PPD patients.

Large-scale bidirectional Mendelian randomization study identifies new gut microbiome significantly associated with immune thrombocytopenic purpura.

A variety of studies have shown a link between the gut microbiota and autoimmune diseases, but the causal relationship with Henoch-Schönlein purpura (HSP) and immune thrombocytopenic purpura (ITP) is unknown. This study investigated the bidirectional causality between gut microbiota and HSP and ITP using Mendelian randomization (MR). Large-scale genetic data of gut microbiota at phylum to species level from the MiBioGen consortium and the Dutch Microbiome Project were utilized. Genome-wide association studies (GWAS) summary statistics for HSP and ITP came from FinnGen R10. Various MR methods were applied to infer causal relationships, including inverse variance weighted (IVW), maximum likelihood (ML), cML-MA, MR-Egger, weighted median, weighted model, and MR-PRESSO. Multiple sensitivity analyses and Bonferroni correction were conducted to enhance robustness and reliability. Based on the IVW estimates, 23 bacterial taxa were identified to have suggestive associations with HSP and ITP. Remarkably, after Bonferroni correction, family Alcaligenaceae (OR = 2.86, 95% CI = 1.52-5.37; IVW, p = 1.10 × 10-3, ML, p = 1.40 × 10-3) was significantly associated with ITP as a risk factor, while family Bacteroidales S24 7group (OR = 0.46, 95% CI = 0.29-0.74; IVW, p = 1.40 × 10-3) was significantly associated with ITP as a protective factor. No significant associations between HSP and ITP and gut microbiota were found in reverse analyses. Our study provides evidence of causal effects of gut microbiota on HSP and ITP, highlighting the importance of further research to clarify the underlying mechanisms and develop targeted therapeutic interventions for these autoimmune diseases.

Association Between Endometritis and Endometrial Polyp: A Mendelian Randomization Study.

Endometrial polyps (EPs) are one of the most common intrauterine benign tumors, and are an important cause of uterine bleeding and female infertility. Previous studies have suggested that endometritis may contribute to the onset of EPs. This study aims to reveal the causal effect of endometritis on EPs by a two-sample Mendelian randomization (MR) study. Utilizing summarized statistics from genome-wide association studies (GWAS) in the European population, we conducted a Mendelian randomization study. In order to select suitable instrumental variables (IVs) that were significantly related to the exposures, a number of quality control approaches were used. For endometritis, 2144 cases and 111,858 controls were included, while for EPs, 2252 cases and 460,758 controls. Utilizing the inverse variance weighted (IVW) as the primary analysis, the data were subjected to a two-sample MR analysis, and the weighted median (WM) technique and MR-Egger regression were carried out additionally. The sensitivity analysis revealed neither heterogeneity nor horizontal pleiotropy. Four independent single nucleotide polymorphisms (SNPs) from endometritis GWAS as IVs were selected. The IVW data did not agree to a causal association between endometritis and EPs (β=1.11e-04, standard error [SE] =4.88e-04, P = 0.82). Directional pleiotropy did not affect the outcome, according to the MR-Egger regression (intercept = 0.09, P = 0.10); Additionally, it showed no causation association between endometritis and EPs (β= -3.28e-03, SE = 3.54e-03, P = 0.45). Similar results were obtained using the weighted-median method (β=8.56e-05, SE=5.97e-04, P = 0.89). No proof of heterogeneity and horizontal pleiotropy between IV estimates was discovered. In conclusion, by large scale genetic data, the results of this MR analysis provided suggestive evidence that the presence of endometritis is not associated with higher EPs risk.

Interplay Between Chronic Kidney Disease, Hypertension, and Stroke: Insights From a Multivariable Mendelian Randomization Analysis.

Chronic kidney disease (CKD) increases the risk of stroke, but the extent through which this association is mediated by hypertension is unknown. We leveraged large-scale genetic data to explore causal relationships between CKD, hypertension, and cerebrovascular disease phenotypes. We used data from genome-wide association studies of European ancestry to identify genetic proxies for kidney function (CKD diagnosis, estimated glomerular filtration rate [eGFR], and urinary albumin-to-creatinine ratio [UACR]), systolic blood pressure (SBP), and cerebrovascular disease (ischemic stroke and its subtypes and intracerebral hemorrhage). We then conducted univariable, multivariable, and mediation Mendelian randomization (MR) analyses to investigate the effect of kidney function on stroke risk and the proportion of this effect mediated through hypertension. Univariable MR revealed associations between genetically determined lower eGFR and risk of all stroke (odds ratio [OR] per 1-log decrement in eGFR, 1.77; 95% CI 1.31-2.40; p < 0.001), ischemic stroke (OR 1.81; 95% CI 1.31-2.51; p < 0.001), and most strongly with large artery stroke (LAS) (OR 3.00; 95% CI 1.33-6.75; p = 0.008). These associations remained significant in the multivariable MR analysis, controlling for SBP (OR 1.98; 95% CI 1.39-2.82; p < 0.001 for all stroke; OR 2.16; 95% CI 1.48-3.17; p < 0.001 for ischemic stroke; OR 4.35; 95% CI 1.84-10.27; p = 0.001 for LAS), with only a small proportion of the total effects mediated by SBP (6.5% [0.7%-16.8%], 6.6% [0.8%-18.3%], and 7.2% [0.5%-24.8%], respectively). Total, direct and indirect effect estimates were similar across a number of sensitivity analyses (weighted median, MR-Egger regression). Our results demonstrate an independent causal effect of impaired kidney function, as assessed by decreased eGFR, on stroke risk, particularly LAS, even when controlled for SBP. Targeted prevention of kidney disease could lower atherosclerotic stroke risk independent of hypertension.

Evaluating the effect of childhood sunburn on the risk of cutaneous melanoma through Mendelian randomization.

Despite numerous observational studies indicating an increased risk of cutaneous melanoma (CM) due to childhood sunburn, no studies have established a definitive cause-and-effect relationship. Therefore, our objective was to employ a Mendelian randomization (MR) design to explore a possible causal association between childhood sunburn and the risk of CM. To investigate the causal relationship between childhood sunburn and CM, we used large-scale genetic summary-level data from genome-wide association studies (GWAS), including childhood sunburn (n = 346,955) and CM (n = 262,288), building upon previous observational studies. In the analysis, we mainly used the inverse-variance weighted (IVW) method of the random effects model, supplemented by the weighted median method and MR-Egger method. The results of the IVW method demonstrated that genetically predicted childhood sunburn was significantly associated with higher odds of CM, with an odds ratio (OR) of 2.418 (95%CI, 1.426-4.099; p = .001). The weighted median method and MR-Egger regression also demonstrated directionally similar results (both p < .05). Furthermore, both the funnel plot and the MR-Egger intercepts showed the absence of directional pleiotropy between childhood sunburn and CM. Our study offers potential evidence linking genetically predicted childhood sunburn with CM, underscoring the need for individuals with a history of childhood sunburn to be extra vigilant regarding the occurrence of CM.