Integrating multi-omics data to reveal the effect of genetic variant rs6430538 on Alzheimer's disease risk.

Introduction: Growing evidence highlights a potential genetic overlap between Alzheimer's disease (AD) and Parkinson's disease (PD); however, the role of the PD risk variant rs6430538 in AD remains unclear. Methods: In Stage 1, we investigated the risk associated with the rs6430538 C allele in seven large-scale AD genome-wide association study (GWAS) cohorts. In Stage 2, we performed expression quantitative trait loci (eQTL) analysis to calculate the cis-regulated effect of rs6430538 on TMEM163 in both AD and neuropathologically normal samples. Stage 3 involved evaluating the differential expression of TMEM163 in 4 brain tissues from AD cases and controls. Finally, in Stage 4, we conducted a transcriptome-wide association study (TWAS) to identify any association between TMEM163 expression and AD. Results: The results showed that genetic variant rs6430538 C allele might increase the risk of AD. eQTL analysis revealed that rs6430538 up-regulated TMEM163 expression in AD brain tissue, but down-regulated its expression in normal samples. Interestingly, TMEM163 showed differential expression in entorhinal cortex (EC) and temporal cortex (TCX). Furthermore, the TWAS analysis indicated strong associations between TMEM163 and AD in various tissues. Discussion: In summary, our findings suggest that rs6430538 may influence AD by regulating TMEM163 expression. These discoveries may open up new opportunities for therapeutic strategies targeting AD.

Genome-wide analysis reveals extensive genetic overlap between childhood phenotypes and later-life type 2 diabetes.

Observational studies have indicated a potential influence of childhood phenotypes on the later development of type 2 diabetes (T2D). However, the underlying biological mechanisms remain unclear. In this study, we conducted a comprehensive genome-wide analysis to investigate the shared genetic architecture and genetic loci between nine childhood phenotypes (N = 4202-620,26) and later-life T2D (N = 80,154) using genetic correlation, mendelian randomization (MR), and conjunctional false discovery rate (conjFDR) statistical frameworks. Our findings demonstrated substantial genetic correlations and pleiotropic enrichment between childhood obesity, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and later-life T2D. Childhood obesity exhibited a significant association with increased later-life T2D risk through 10 mediators, 6 of which were adulthood obesity-related phenotypes. Additionally, we identified 69, 83, 3, 5, 10, 5, 3, and 7 loci shared between childhood obesity, BMI, SBP, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and T2D at conjFDR <0.05, with the majority of these loci being novel discoveries. Overall, our study reveals extensive genetic overlap between childhood obesity-related phenotypes and T2D with concordant effect directions, shedding new light on variants and phenotypes with lifelong effects.

Vitamin D levels and five cardiovascular diseases: A Mendelian randomization study.

Cardiovascular disease is the leading cause of death worldwide, whilst vitamin D levels have been found to be associated with cardiovascular disease. To investigate the causal relationship between vitamin D levels and five cardiovascular diseases, a genome-wide association study (GWAS) was carried out using data on vitamin D levels (sample size = 79366), angina pectoris (18168 cases and 187840 controls), coronary heart disease (21012 cases and 197780 controls), lacunar stroke (6030 cases and 248929 controls), heart attack (10693 cases and 451187 controls), and hypertension (55917 cases and 162837 controls), with a Mendelian randomization (MR) analysis being subsequently performed. Six single nucleotide polymorphisms were used as instrumental variables (IVs). In addition, sensitivity analysis was performed to verify the reliability of the MR results here. The results showed a causal relationship between vitamin D levels and angina pectoris (OR = 0.51, 95 % CI: 0.28-0.93, P = 0.03), coronary heart disease (OR = 0.53, 95 % CI: 0.34-0.81, P = 0.004), and lacunar stroke (OR = 0.41, 95 % CI: 0.20-0.86, P = 0.02), but no causal relationship with heart attacks (OR = 1.00, 95 % CI: 0.99-1.01, P = 0.76) or hypertension (OR = 0.99, 95 % CI: 0.73-1.34, P = 0.94). Additionally, our IVs data showed no heterogeneity or pleiotropy, whilst the results of the MR analysis were reliable. This study contributes to the prevention and treatment of these five cardiovascular diseases.

Mendelian randomization study supports the causal effects of air pollution on longevity via multiple age-related diseases.

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may reduce life expectancy; however, the causal pathways of PM2.5 exposure affecting life expectancy remain unknown. Here, we assess the causal effects of genetically predicted PM2.5 concentration on common chronic diseases and longevity using a Mendelian randomization (MR) statistical framework based on large-scale genome-wide association studies (GWAS) (>400,000 participants). After adjusting for other types of air pollution and smoking, we find significant causal relationships between PM2.5 concentration and angina pectoris, hypercholesterolaemia and hypothyroidism, but no causal relationship with longevity. Mediation analysis shows that although the association between PM2.5 concentration and longevity is not significant, PM2.5 exposure indirectly affects longevity via diastolic blood pressure (DBP), hypertension, angina pectoris, hypercholesterolaemia and Alzheimer's disease, with a mediated proportion of 31.5, 70.9, 2.5, 100, and 24.7%, respectively. Our findings indicate that public health policies to control air pollution may help improve life expectancy.

Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer's disease.

Growing evidence suggests the effect of educational attainment (EA) on Alzheimer's disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (rg = -0.22, p = 5.07E-05), cognitive performance (rg = -0.27, p = 2.44E-05), and intelligence (rg = -0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58-0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD.

HNetGO: protein function prediction via heterogeneous network transformer.

Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of protein function prediction models. However, the heavy reliance on complex feature engineering and model integration methods limits the development of existing methods. Besides, models based on deep learning only use labeled data in a certain dataset to extract sequence features, thus ignoring a large amount of existing unlabeled sequence data. Here, we propose an end-to-end protein function annotation model named HNetGO, which innovatively uses heterogeneous network to integrate protein sequence similarity and protein-protein interaction network information and combines the pretraining model to extract the semantic features of the protein sequence. In addition, we design an attention-based graph neural network model, which can effectively extract node-level features from heterogeneous networks and predict protein function by measuring the similarity between protein nodes and gene ontology term nodes. Comparative experiments on the human dataset show that HNetGO achieves state-of-the-art performance on cellular component and molecular function branches.

Genetic correlation, causal relationship, and shared loci between vitamin D and COVID-19: A genome-wide cross-trait analysis.

Observational studies have shown that vitamin D supplementation reduces the risk of COVID-19 infection, yet little is known about the shared genomic architectures between them. Leveraging large-scale genome-wide association study (GWAS) summary statistics, we investigated the genetic correlation and causal relationship between genetically determined vitamin D and COVID-19 using linkage disequilibrium score regression and Mendelian randomization (MR) analyses, and conducted a cross-trait GWAS meta-analysis to identify the overlapping susceptibility loci of them. We observed a significant genetic correlation between genetically predicted vitamin D and COVID-19 (rg = -0.143, p = 0.011), and the risk of COVID-19 infection would decrease by 6% for every 0.76 nmol L-1 increase of serum 25 hydroxyvitamin D (25OHD) concentrations in generalized MR (OR = 0.94, 95% CI: 0.89-0.99, p = 0.019). We identified rs4971066 (EFNA1) as a risk locus for the joint phenotype of vitamin D and COVID-19. In conclusion, genetically determined vitamin D is associated with COVID-19. Increased levels of serum 25OHD concentration may benefit the prevention and treatment of COVID-19.

Repurposing Antihypertensive Drugs for the Prevention of Glaucoma: A Mendelian Randomization Study.

Purpose: Several antihypertensive drugs have been used for the treatment of glaucoma. However, the effect of hypertension and antihypertensive drugs on glaucoma is still unclear. Methods: Leveraging large-scale genome-wide association study summary statistics for glaucoma (Ncase = 4737, Ncontrol = 458,196), blood pressure (BP) (N = 422,771), and intraocular pressure (IOP) (N = 31,269), the genetic correlation and causal relationship of genetically assessed IOP, systolic blood pressure (SBP), diastolic blood pressure (DBP), and 12 types of antihypertensive drugs with glaucoma were evaluated using linkage disequilibrium score (LDSC) regression, univariate mendelian randomization (MR), and multivariable MR. Results: LDSC results showed a suggestive association of glaucoma with SBP (Rg = 0.12, P = 0.0076) and DBP (Rg = 0.17, P = 0.02). In univariate MR, genetically elevated BP in participants was not identified to lead to an increased glaucoma risk (SBP: odds ratio [OR], 1.05 [95% confidence interval {CI}, 0.91-1.21]; P = 0.52; DBP: OR, 1.07 [95% CI, 0.93-1.23]; P = 0.34). The results of univariate MR were replicated in multivariable MR (SBP: OR, 0.95 [95% CI, 0.71-1.29]; P = 0.75; DBP: OR, 1.13 [95% CI, 0.85-1.51]; P = 0.41). Furthermore, there was insufficient evidence to suggest that antihypertensive drugs were associated with glaucoma. Conclusions: Together, controlling BP may not help prevent and treat glaucoma, and antihypertensive drugs may neither treat nor worsen glaucoma. Translational Relevance: Treating with antihypertensive drugs should not be used as an intervention for patients with glaucoma.

Genetic variant rs11136000 upregulates clusterin expression and reduces Alzheimer's disease risk.

Clusterin (CLU) is an extracellular chaperone involved in reducing amyloid beta (Aß) toxicity and aggregation. Although previous genome-wide association studies (GWAS) have reported a potential protective effect of CLU on Alzheimer's disease (AD) patients, how intron-located rs11136000 (CLU) affects AD risk by regulating CLU expression remains unknown. In this study, we integrated multiple omics data to construct the regulated pathway of rs11136000-CLU-AD. In step 1, we investigated the effects of variant rs11136000 on AD risk with different genders and diagnostic methods using GWAS summary statistics for AD from International Genomics of Alzheimer's Project (IGAP) and UK Biobank. In step 2, we assessed the regulation of rs11136000 on CLU expression in AD brain samples from Mayo clinic and controls from Genotype-Tissue Expression (GTEx). In step 3, we investigated the differential gene/protein expression of CLU in AD and controls from four large cohorts. The results showed that rs11136000 T allele reduced AD risk in either clinically diagnosed or proxy AD patients. By using expression quantitative trait loci (eQTL) analysis, rs11136000 variant downregulated CLU expression in 13 normal brain tissues, but upregulated CLU expression in cerebellum and temporal cortex of AD samples. Importantly, CLU was significantly differentially expressed in temporal cortex, dorsolateral prefrontal cortex and anterior prefrontal cortex of AD patients compared with normal controls. Together, rs11136000 may reduce AD risk by regulating CLU expression, which may provide important information about the biological mechanism of rs9848497 in AD progress.

MHCRoBERTa: pan-specific peptide-MHC class I binding prediction through transfer learning with label-agnostic protein sequences.

Predicting the binding of peptide and major histocompatibility complex (MHC) plays a vital role in immunotherapy for cancer. The success of Alphafold of applying natural language processing (NLP) algorithms in protein secondary struction prediction has inspired us to explore the possibility of NLP methods in predicting peptide-MHC class I binding. Based on the above motivations, we propose the MHCRoBERTa method, RoBERTa pre-training approach, for predicting the binding affinity between type I MHC and peptides. Analysis of the results on benchmark dataset demonstrates that MHCRoBERTa can outperform other state-of-art prediction methods with an increase of the Spearman rank correlation coefficient (SRCC) value. Notably, our model gave a significant improvement on IC50 value. Our method has achieved SRCC value and AUC value as 0.785 and 0.817, respectively. Our SRCC value is 14.3% higher than NetMHCpan3.0 (the second highest SRCC value on pan-specific) and is 3% higher than MHCflurry (the second highest SRCC value on all methods). The AUC value is also better than any other pan-specific methods. Moreover, we visualize the multi-head self-attention for the token representation across the layers and heads by this method. Through the analysis of the representation of each layer and head, we can show whether the model has learned the syntax and semantics necessary to perform the prediction task well. All these results demonstrate that our model can accurately predict the peptide-MHC class I binding affinity and that MHCRoBERTa is a powerful tool for screening potential neoantigens for cancer immunotherapy. MHCRoBERTa is available as an open source software at github (https://github.com/FuxuWang/MHCRoBERTa).

Rheumatoid Arthritis and Cardio-Cerebrovascular Disease: A Mendelian Randomization Study.

Significant genetic association exists between rheumatoid arthritis (RA) and cardiovascular disease. The associated mechanisms include common inflammatory mediators, changes in lipoprotein composition and function, immune responses, etc. However, the causality of RA and vascular/heart problems remains unknown. Herein, we performed Mendelian randomization (MR) analysis using a large-scale RA genome-wide association study (GWAS) dataset (462,933 cases and 457,732 controls) and six cardio-cerebrovascular disease GWAS datasets, including age angina (461,880 cases and 447,052 controls), hypertension (461,880 cases and 337,653 controls), age heart attack (10,693 cases and 451,187 controls), abnormalities of heartbeat (461,880 cases and 361,194 controls), stroke (7,055 cases and 454,825 controls), and coronary heart disease (361,194 cases and 351,037 controls) from United Kingdom biobank. We further carried out heterogeneity and sensitivity analyses. We confirmed the causality of RA with age angina (OR = 1.17, 95% CI: 1.04-1.33, p = 1.07E-02), hypertension (OR = 1.45, 95% CI: 1.20-1.75, p = 9.64E-05), age heart attack (OR = 1.15, 95% CI: 1.05-1.26, p = 3.56E-03), abnormalities of heartbeat (OR = 1.07, 95% CI: 1.01-1.12, p = 1.49E-02), stroke (OR = 1.06, 95% CI: 1.01-1.12, p = 2.79E-02), and coronary heart disease (OR = 1.19, 95% CI: 1.01-1.39, p = 3.33E-02), contributing to the understanding of the overlapping genetic mechanisms and therapeutic approaches between RA and cardiovascular disease.

Pan-cancer analysis reveals the expression, genetic alteration and prognosis of pyroptosis key gene GSDMD.

BACKGROUND: Gasdermins (GSDMs)-mediated pyroptosis is widely involved in activating anti-tumor immunity and suppressing tumor growth. However, whether gasdermin D (GSDMD)-mediated pyroptosis affects patient prognosis in pan-cancer remains unknown. METHODS: We performed analyses of the RNA expression, genetic alteration, prognosis and immune infiltration of GSDMD in pan-cancer. In order to explore the relationship between pyroptosis and tumors, we calculated the correlation between GSDMD and pyroptosis key genes in pan-cancer. We also investigated the enrichment pathway of GSDMD-related genes. RESULTS: GSDMD was differentially expressed in the vast majority of cancer, and could be used as a prognostic marker in adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), skin cutaneous melanoma (SKCM) and rectum adenocarcinoma (READ). Strong evidence indicated the significant correlation of GSDMD with almost all immune checkpoints and immune cells. Pyroptosis-related genes strongly associated with GSDMD in ACC, KIRC, LGG, LIHC and SKCM, suggesting that GSDMD-mediated pyroptosis might play a critical role in the five cancers. CONCLUSION: All the evidence supported the potential role of GSDMD-mediated pyroptosis in cancer. Our results provided new insights into GSDMD as a prognostic marker and potential therapeutic target for cancer.

Integration of Multiple-Omics Data to Analyze the Population-Specific Differences for Coronary Artery Disease.

Significant differences may exist among different descents, but the current studies are mainly based on European populations. In the present study, we analyzed the population-specific differences of coronary artery disease (CAD) between European and East Asian descents. In stage 1, we identified CAD susceptibility genes by gene-based tests in European and East Asian populations. We identified two novel susceptibility genes for CAD, namely, CUX2 and OAS3. In stage 2, we carried out meta-analyses for the population-specific variants. rs599839 (PSRC1) represented a protective variant for CAD in East Asian populations (ORASN = 0.72. 95% CI: 0.63-0.81) but a risk factor in European populations (OREUR = 1.13, 95% CI: 0.93-1.36). In stage 3, we enriched the risk genes and explored the population-specific differences in Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), regulatory element, tissues, and cell types. In stage 4, in order to predict genes that showed pleiotropic/potentially causal association with CAD, we integrated summary-level data from independent genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) by using summary data-based Mendelian randomization (SMR). The results showed that NBEAL1 and FGD6 were population-specific pleiotropic/causal genes. Although some potential mutations and risk genes of CAD are shared, it is still of great significance to elucidate the genetic differences among different populations. Our analysis provides a better understanding of the pathogenic mechanisms and potential therapeutic targets for CAD.