The metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.
Aims
Previous studies investigated the associations between sleep traits and cardiac diseases, but the evidence for the causal inferences was unclear. This study aimed to explore the causal relationship between sleep and cardiac diseases by virtue of Mendelian randomization (MR).
Methods and results
Summary‐level data for exposure variables (sleep duration, chronotype, and insomnia) and outcome variables (ischaemic heart disease, atrial fibrillation, myocardial infarction, and heart failure) were derived from UK Biobank. Data from the FinnGen consortium was used as a robustness check. In MR analysis, the inverse variance weighted (IVW) method was applied to infer causality between exposure and outcome. MR‐Egger regression was used to identify pleiotropy, and MR‐PRESSO outlier test was used to remove the pleiotropy of the genetic instruments. Based on UK Biobank, MR analysis suggested that sleep duration was weakly associated with atrial fibrillation (OR = 0.9999, 95% CI: 0.9998–0.9999) and ischaemic heart disease (OR = 0.9997, 95% CI: 0.9995–0.9998). Insomnia was associated with ischaemic heart disease (OR = 1.0117, 95% CI: 1.0051–1.0183) and myocardial infarction (OR = 1.0049, 95% CI: 1.0019–1.0079). No associations were found between chronotype and cardiac diseases (P > 0.05). We did not find pleiotropy except for insomnia with ischaemic heart disease and myocardial infarction using MR‐Egger regression, and MR‐PRESSO analysis consistent with IVW. Finally, we obtained the same direction as with UK Biobank using the FinnGen data.
Conclusions
Sleep duration and insomnia might be the potential causal risk factors of cardiac diseases. As the OR was small, these associations are probably not clinically relevant. Further validation studies are needed.
Purposes:Observational studies indicate that birth weight and childhood obesity are associated with essential hypertension, but their causal effect on essential hypertension remains unclear. The aim of our study is to elucidate the causal relationship between birth weight, childhood obesity, and essential hypertension by Mendelian randomization (MR) with genetic variants as instrumental variables (IVs).Methods:We identified IVs based on single nucleotide polymorphisms (SNPs) associated with birth weight (n = 160 295) and childhood obesity (n = 6889, 1509 cases and 5380 controls) from the meta-analysis of a genome-wide association study. Summary level data from the UK Biobank essential hypertension consortium (n = 463 010, 54 358 cases and 408 652 controls) was used to analyze the relationship between IVs and essential hypertension. Two MR analysis methods, two threshold values of selecting IVs, and leave-one-out analysis were used to ensure the robustness of the results.Results:Genetic predisposition to higher birth weight did not increase the risk of essential hypertension. In contrast, per one standard deviation increase in childhood body mass index was significantly associated with an increased risk of essential hypertension (odds ratio = 1.0075, 95% confidence interval: 1.0035–1.0116) when using seven SNPs that achieved genome-wide significance (P < 5 × 10−8). Sensitivity analysis and MR-Egger regression indicated that the results were robust and not influenced by pleiotropy.Conclusions:No evidence of an association between birth weight and essential hypertension was found. Childhood obesity, however, showed a causal relationship with the risk of essential hypertension, which was helpful to understand the mechanisms of essential hypertension and develop strategies for its prevention.
Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes. Subsequently, we constructed a 5-gene prognostic signature in the training cohort based on the T-cell marker genes from the cancer genome atlas (TCGA) database, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5-years were 0.614, 0.713 and 0.702 in the training cohort, 0.669, 0.603 and 0.645 in the testing cohort, 0.661, 0.628 and 0.590 in the GEO cohort, respectively. Furthermore, we created a highly reliable nomogram to facilitate clinical application. Gene set enrichment analysis showed that immune-related pathways were mainly enriched in the high-risk group. Tumor immune microenvironment indicated that high-risk group exhibited higher immune score, stromal score, and immune cell infiltration levels. Moreover, genes of the immune checkpoints and human leukocyte antigen family were all overexpressed in high-risk group. Drug sensitivity revealed that low-risk group was sensitive to 8 chemotherapeutic drugs and high-risk group to 4 chemotherapeutic drugs. In short, our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for LUSC patients.
Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10 −6) and 1,044 pleotropic genes (P < 4.34 × 10 −6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.
Background: Elevated blood pressure (BP) was associated with higher risk of heart failure, but the relationship between BP-lowering via antihypertensive drugs and diminution of heart failure was inconclusive. This study aimed to estimate the causal association of BP with heart failure, and explore the effects of BP-lowering through different antihypertensive drug classes on heart failure risk using Mendelian randomization analysis with genetic variants as instrument variables.Methods: Genetic variants associated with BP were derived from UK Biobank (n ¼ 317 754) and the genomewide association study (GWAS) meta-analysis of UK Biobank and International Consortium of Blood Pressure (n ¼ 757 601). Heart failure summary association data were contributed by HERMES Consortium (47 309 heart failure cases and 930 014 controls). Inverse variance weighted (IVW) was performed to estimate causality between exposure and outcome, and weighted median was utilized as sensitivity analysis, and Mendelian randomization-Egger regression was used to identify pleiotropy of instrument variables. Multivariable Mendelian randomization (MVMR) was applied to control for the confounders.
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