Search Results (12)

Background: Obesity significantly impacts disease burden, with waist-to-hip ratio (WHR) as a key obesity indicator, but the genetic and biological pathways underlying WHR, particularly its sex-specific differences, remain poorly understood. Methods: This study explored WHR’s sexual dimorphism and its links to complex traits using cross-sectional surveys and genetic data from Giant and UK Biobank (UKB). We analyzed WHR heritability, performed tissue-specific transcriptome-wide association studies (TWAS) using FUSION, and conducted genetic correlation analyses with linkage disequilibrium score regression (LDSC) and Local Analysis of [co]Variant Association (LAVA). Polygenic scores (PGS) for WHR were constructed using the clumping and thresholding method (CT), and associations with complex traits were assessed via logistic or linear models. Results: The genetic analysis showed sex-specific heritability for WHR, with TWAS identifying female-specific (e.g., CCDC92) and male-specific (e.g., UQCC1) genes. Global genetic correlation analysis revealed sex-specific associations between WHR and 23 traits, while local analysis identified eight sex-specific loci across five diseases. Regression analysis highlighted sex-specific associations for 70 traits with WHR and 45 traits with WHR PGS, with stronger effects in females. Predictive models also performed better in females. Conclusions: This study underscores WHR’s sexual dimorphism and its distinct associations with complex traits, offering insights into sex-specific biological differences, health management, and clinical advancements. Full article

Background: Cholelithiasis is the most common disorder affecting the biliary system. Choline is an essential nutrient in the human diet and is crucial for the synthesis of neurotransmitters. Previous studies have suggested an association between choline metabolites and cholelithiasis. However, the underlying mechanisms remain unclear. This research aims to fill the knowledge gap regarding the role of choline metabolites in cholelithiasis. Methods: Genetic data related to choline metabolites and other covariates were retrieved from the U.K. Biobank and IEU OpenGWAS database. Two-sample (TSMR) and multivariate Mendelian randomization (MVMR) analyses, mediation analysis, linkage disequilibrium score regression (LDSC), colocalization analysis, and enrichment analysis were performed. Results: A significant causal relationship was identified between serum level of sphingomyelin and cholelithiasis (p-value = 0.0002). A protective causal effect was identified in MVMR analysis. The following mediated MR analysis indicated that only LDL mediated a large part of the causal relationship (59.18%). Seven genes, including GCKR, SNX17, ABCG8, MARCH8, FUT2, APOH, and HNF1A, were revealed to be colocalized with the causal signal between sphingomyelin and cholelithiasis. Conclusion: The present study has identified a protective effect between sphingomyelin and cholelithiasis. This effect is largely mediated by LDL. The findings of this study offer valuable information for further exploration of the molecular mechanisms of cholelithiasis. Full article

Introduction: Bell’s palsy is a common acute peripheral neurological disorder causing unilateral facial paralysis. Its exact etiology remains unknown, but it is linked to inflammation, immune responses, infections, and ischemia. This study explores the potential causal relationship between Bell’s palsy and peripheral blood inflammatory proteins, metabolites, and immune cell characteristics. Methods: Genetic data for Bell’s palsy were obtained from the Finnish database (version R10) and IEU OpenGWAS. A two-sample Mendelian randomization (MR) approach was applied, analyzing 4907 plasma proteins, 731 immune cell traits, 91 inflammatory proteins, and 1400 metabolites. The Finnish dataset served as the discovery cohort, while the IEU OpenGWAS dataset acted as the validation cohort. Bioinformatics analyses included protein–protein interaction (PPI) networks, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, colocalization, and Linkage Disequilibrium Score Regression (LDSC) to identify candidate proteins and explore potential therapeutic targets. Results: MR analysis identified 70 inflammatory proteins, 77 metabolites, and 26 immune cell traits as potentially causally associated with Bell’s palsy. After external validation, BLVRB, HMOX2, TNFRSF12A, DEFB128, ITM2A, VEGF-A, and DDX58 remained significantly associated (p < 0.05). PPI network analysis led to 31 candidate proteins, and six core proteins (JAK2, IL27RA, OSM, CCL19, SELL, VCAM-1) were identified. Conclusions: Our study identifies causal relationships between inflammatory proteins, metabolites, immune cells, and Bell’s palsy, highlighting that the JAK/STAT signaling pathway may be a potentially critical target for intervention in Bell’s palsy, and that its modulation may provide new directions and opportunities for therapeutic strategies and drug discovery for the disease. Full article

Background/Objectives: Observational studies suggest a link between vascular calcification and dementia or cognitive decline, but the evidence is conflicting, and the underlying mechanisms are unclear. Here, we investigate the shared genetic and causal relationships of vascular calcification—coronary artery calcification (CAC) and abdominal aortic calcification (AAC)—with Alzheimer’s disease (AD), and five cognitive traits. Methods: We analyse large-scale genome-wide association studies (GWAS) summary statistics, using well-regarded methods, including linkage disequilibrium score regression (LDSC), Mendelian randomisation (MR), pairwise GWAS (GWAS-PW), and gene-based association analysis. Results: Our findings reveal a nominally significant positive genome-wide genetic correlation between CAC and AD, which becomes non-significant after excluding the APOE region. CAC and AAC demonstrate significant negative correlations with cognitive performance and educational attainment. MR found no causal association between CAC or AAC and AD or cognitive traits, except for a bidirectional borderline-significant association between AAC and fluid intelligence scores. Pairwise-GWAS analysis identifies no shared causal SNPs (posterior probability of association [PPA]3 < 0.5). However, we find pleiotropic loci (PPA4 > 0.9), particularly on chromosome 19, with gene association analyses revealing significant genes in shared regions, including APOE, TOMM40, NECTIN2, and APOC1. Moreover, we identify suggestively significant loci (PPA4 > 0.5) on chromosomes 1, 6, 7, 9 and 19, implicating pleiotropic genes, including NAV1, IPO9, PHACTR1, UFL1, FHL5, and FOCAD. Conclusions: Current findings reveal limited genetic correlation and no significant causal associations of CAC and AAC with AD or cognitive traits. However, significant pleiotropic loci, particularly at the APOE region, highlight the complex interplay between vascular calcification and neurodegenerative processes. Given APOE’s roles in lipid metabolism, neuroinflammation, and vascular integrity, its involvement may link vascular and neurodegenerative disorders, pointing to potential targets for further investigation. Full article

Background: Rheumatoid arthritis (RA) is a globally prevalent chronic inflammatory disease. Environmental exposures, such as air pollution and smoking, are considered potential risk factors. However, the causal relationships and underlying mechanisms between these factors and RA are not fully understood. Methods: This study utilized large-scale genome-wide association studies (GWASs) from European ethnic backgrounds and employed bidirectional two-sample Mendelian randomization (MR) to investigate the relationships between air pollution, smoking, and RA. Genetic correlations were assessed using linkage disequilibrium score regression (LDSC). Furthermore, mediation analysis was conducted to evaluate the potential mediating roles of iron metabolism and urinary biomarkers in these relationships. Results: The MR analysis revealed that genetically predicted lifetime smoking intensity was associated with an 85% increased risk of RA. Subgroup analysis differentiating between seropositive RA (SPRA) and seronegative RA (SNRA) showed a causal association with SPRA, but not with SNRA. C-reactive protein was identified as a mediator in the relationship between lifetime smoking and both RA and SPRA, mediating 18.23% and 32.45% of the effects, respectively. Genetic correlation analysis further confirmed a positive genetic association between smoking and both RA and SPRA. Conclusions: This study provides significant insights into the genetic and causal connections between air pollution, smoking, and the development of RA, highlighting the mediating role of C-reactive protein. These findings not only offer new perspectives on how smoking might enhance RA risk through inflammatory pathways but also underscore the importance of reducing smoking exposure in public health strategies. Full article

Background: Acute acalculous cholecystitis (AAC) is a type of cholecystitis with high mortality rate while its pathogenesis remains complex. Choline is one of the essential nutrients and is related to several diseases. This study aimed to explore the causal relationship between choline metabolites and AAC and its potential mechanisms. Methods: This research utilized the two-sample Mendelian randomization method to investigate the causal relationship between choline metabolites and AAC. Additionally, multivariable Mendelian randomization and mediated Mendelian randomization were used to explore potential confounding effects from low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TGs), and coronary artery disease (CAD). Linkage disequilibrium score regression (LDSC), co-localization analysis, and enrichment analysis were used to investigate relevant molecular mechanisms. Results: There is a negative causal relationship between total choline (OR [95%CI] = 0.9982 [0.9974, 0.9990], p = 0.0023), phosphatidylcholine (OR [95%CI] = 0.9983 [0.9976–0.9991], p = 0.0040), sphingomyelin (OR [95%CI] = 0.9980 [0.9971–0.9988], p = 0.0001), and AAC. The mediating effects of LDL were −0.0006 for total choline, −0.0006 for phosphatidylcholine, and −0.0008 for sphingomyelin, indicating a protective effect of total choline, phosphatidylcholine, and sphingomyelin on AAC. Colocalized SNP rs75331444, which is mapped to gene ABCG8, was identified for total choline (PPH4 = 0.8778) and sphingomyelin (PPH4 = 0.9344). Conclusions: There is a causal relationship between choline metabolites and cholecystitis, mediated through the protective action of LDL. Our results suggest that ABCG8 may play a role in the development of non-calculous cholecystitis. Full article

Purpose: 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. Methods: 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. 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. Conclusion: 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. Full article

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits/diseases, and a key question is how much heritability could be explained by all single nucleotide polymorphisms (SNPs) in GWAS. One widely used approach that relies on summary statistics only is linkage disequilibrium score regression (LDSC); however, this approach requires certain assumptions about the effects of SNPs (e.g., all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the “true” effect sizes from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of our results and the applicability of the approach. In this study, we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. The resampling procedures are performed at the SNP level as it is most common to estimate heritability from GWAS summary statistics alone. Simulations showed that the delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. In particular, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. We also explored various methods for constructing confidence intervals (CIs). In addition, we applied our method to estimate the SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg. In conclusion, SumVg may provide a useful alternative tool for calculating SNP heritability and estimating SE/CI, which does not rely on distributional assumptions of SNP effects. Full article

The aim was to study the genetic correlation and causal relationship between spondyloarthritis (SpA) and blood metabolites based on the large-scale genome-wide association study (GWAS) summary data. The GWAS summary data (3966 SpA and 448,298 control cases) of SpA were from the UK Biobank, and the GWAS summary data (486 blood metabolites) of human blood metabolites were from a published study. First, the genetic correlation between SpA and blood metabolites was analyzed by linkage disequilibrium score (LDSC) regression. Next, we used Mendelian randomization (MR) analysis to perform access causal relationship between SpA and blood metabolites. Random effects inverse variance weighted (IVW) was the main analysis method, and the MR Egger, weighted median, simple mode, and weighted mode were supplementary methods. The MR analysis results were dominated by the random effects IVW. The Cochran’s Q statistic (MR-IVW) and Rucker’s Q statistic (MR Egger) were used to check heterogeneity. MR Egger and MR pleiotropy residual sum and outlier (MR-PRESSO) were used to check horizontal pleiotropy. The MR-PRESSO was also used to check outliers. The “leave-one-out” analysis was used to assess whether the MR analysis results were affected by a single SNP and thus test the robustness of the MR results. Finally, we identified seven blood metabolites that are genetically related to SpA: X-10395 (correlation coefficient = −0.546, p = 0.025), pantothenate (correlation coefficient = −0.565, p = 0.038), caprylate (correlation coefficient = −0.333, p = 0.037), pelargonate (correlation coefficient = −0.339, p = 0.047), X-11317 (correlation coefficient = −0.350, p = 0.038), X-12510 (correlation coefficient = −0.399, p = 0.034), and X-13859 (Correlation coefficient = −0.458, p = 0.015). Among them, X-10395 had a positive genetic causal relationship with SpA (p = 0.014, OR = 1.011). The blood metabolites that have genetic correlation and causal relationship with SpA found in this study provide a new idea for the study of the pathogenesis of SpA and the determination of diagnostic indicators. Full article

Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D (r_g = 0.06, p = 1.37 × 10⁻⁵) and between headache and T2D (r_g = 0.07, p = 3.0 × 10⁻⁴). Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (P_meta < 5 × 10⁻⁸) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (P_gene-based < 3.85 × 10⁻⁶) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes (EHMT2, SLC44A4, PLEKHA1, CFDP1, TMEM170A, CHST6, and BCAR1) common between them. There was also a significant overlap of genes nominally associated (P_gene-based < 0.05) with both migraine and T2D (P_{binomial-test} = 2.83 × 10⁻⁴⁶) and headache and T2D (P_{binomial-test} = 4.08 × 10⁻²⁹). Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated (inverse-variance weighted, OR_IVW = 0.90, P_ivw = 7 × 10⁻³) with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D. Full article

Objective: Osteoporosis (OP) is the most common bone disease. The genetic and metabolic factors play important roles in OP development. However, the genetic basis of OP is still elusive. The study aimed to explore the relationships between OP and dietary habits. Methods: This study used large-scale genome-wide association study (GWAS) summary statistics from the UK Biobank to explore potential associations between OP and 143 dietary habits. The GWAS summary data of OP included 9434 self-reported OP cases and 444,941 controls, and the GWAS summary data of the dietary habits included 455,146 participants of European ancestry. Linkage disequilibrium score regression (LDSC) was used to detect the genetic correlations between OP and each of the 143 dietary habits, followed by Mendelian randomization (MR) analysis to further assess the causal relationship between OP and candidate dietary habits identified by LDSC. Results: The LDSC analysis identified seven candidate dietary habits that showed genetic associations with OP including cereal type such as biscuit cereal (coefficient = −0.1693, p value = 0.0183), servings of raw vegetables per day (coefficient = 0.0837, p value = 0.0379), and spirits measured per month (coefficient = 0.115, p value = 0.0353). MR analysis found that OP and PC17 (butter) (odds ratio [OR] = 0.974, 95% confidence interval [CI] = (0.973, 0.976), p value = 0.000970), PC35 (decaffeinated coffee) (OR = 0.985, 95% CI = (0.983, 0.987), p value = 0.00126), PC36 (overall processed meat intake) (OR = 1.035, 95% CI = (1.033, 1.037), p value = 0.000976), PC39 (spirits measured per month) (OR = 1.014, 95% CI = (1.011, 1.015), p value = 0.00153), and servings of raw vegetables per day (OR = 0.978, 95% CI = (0.977, 0.979), p value = 0.000563) were clearly causal. Conclusions: Our findings provide new clues for understanding the genetic mechanisms of OP, which focus on the possible role of dietary habits in OP pathogenesis. Full article

Previous studies showed that diet and gut microbiota had a correlation with sleep. However, the potential interaction effects of diet and gut microbiota on sleep are still unclear. The phenotypic data of insomnia (including 374,505 subjects) and sleep duration (including 372,805 subjects) were obtained from the UK Biobank cohort. The Single Nucleotide Polymorphisms (SNPs) associated with 114 gut microbiota, 84 dietary habits, and 4 dietary compositions were derived from the published Genome-wide Association Study (GWAS). We used Linkage Disequilibrium Score Regression (LDSC) to estimate the genetic correlation and colocalization analysis to assess whether dietary habits and insomnia/sleep duration shared a causal variant in a region of the genome. Using UK Biobank genotype data, the polygenetic risk score of gut microbiota, dietary habits, and dietary compositions were calculated for each subject. Logistic regression and linear regression models were used to assess the potential effects of diet-gut microbiota interactions on sleep phenotypes, including insomnia and sleep duration. Insomnia and sleep duration were used as dependent variables, and sex, age, the Townsend Deprivation Index scores, and smoking and drinking habits were selected as covariates in the regression analysis. All statistical analyses were conducted using R-3.5.1 software. Significant genetic correlations were discovered between insomnia/sleep duration and dietary habits. Further, we found several significant dietary compositions-gut microbiota interactions associated with sleep, such as fat × G_Collinsella_RNT (p = 1.843 × 10⁻²) and protein × G_Collinsella_HB (p = 7.11 × 10⁻³). Besides, multiple dietary habits-gut microbiota interactions were identified for sleep, such as overall beef intake × G_Desulfovibrio_RNT (p = 3.26 × 10⁻⁴), cups of coffee per day × G_Escherichia_Shigella_RNT (p = 1.14 × 10⁻³), and pieces of dried fruit per day × G_Bifidobacterium_RNT (p = 5.80 × 10⁻³). This study reported multiple diet-gut microbiota interactions associated with sleep, which may provide insights into the biological mechanisms of diet and gut microbiota affecting sleep. Full article