GWAS Catalog Research Articles

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7000 publications for >15000 traits, from which more than 625000 lead associations have been curated. Additionally, 85 000 full genome-wide summary statistics datasets-containing association data for all variants in the analysis-are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

Causal impact of gut microbiota on five liver diseases: insights from mendelian randomization and single-cell RNA sequencing

BackgroundLiver disease is among the top ten causes of death globally. With studies suggesting a link between gut microbiota (GM) and liver disease.MethodWe selected summary statistics data from the largest available whole-genome association study (n = 13,266) of GM by the MiBioGen consortium as the exposure, and obtained liver disease-related data from IEU Open GWAS and The NHGRI-EBI GWAS Catalog. A two-sample Mendelian Randomization (MR) analysis employing various methods, to establish the causal relationship between GM and five liver diseases. Meanwhile, single-cell RNA sequencing data were used to examine Prevotella-related genes expression under healthy and disease liver.ResultsThe IVW analysis indicate a causal relationship between GM and liver diseases, with Prevotella exhibiting a protective effect in all five liver diseases: Alcoholic liver disease (OR:0.81,95% confidence interval:0.66-1.00,PIVW = 0.0494); Cirrhosis (OR: 0.85,95% confidence interval: 0.73-0.99,PIVW = 0.0397); Hepatic failure, not elsewhere classified (OR:0.60,95% confidence interval:0.37-0.95,PIVW = 0.0305); Benign neoplasm:Liver (OR:0.39,95% confidence interval:0.2-0.75,PIVW = 0.0046); Malignant neoplasm of liver, primary (OR:0.41, 95% confidence interval:0.18-0.93,PIVW = 0.0334). The single-cell results suggest differential expression of Prevotella-related genes between liver disease patients and healthy individuals.ConclusionOur MR results show a causal relationship between the GM and liver disease. Prevotella displays a notable protective effect. This finding may enhance the precision of GM-based therapies and offer new insights for clinical research.

Genome-wide DNA methylation profiling reveals a novel hypermethylated biomarker PRKCB in gastric cancer

Globally, gastric cancer (GC) ranks among the most prevalent forms of malignancy, posing a significant health burden. Epigenetic modifications, predominantly characterized by alterations in DNA methylation patterns, have been linked to a diverse array of neoplastic processes. Here, we undertake a comprehensive analysis of the DNA methylation signature in GC, with the aim to discover the potential diagnostic epigenetic biomarkers. Utilizing the Illumina 935 K BeadChip, we conducted a genome-wide exploration of DNA methylation patterns in four paired samples of GC tissues and adjacent non-cancerous counterparts. The bisulfite-pyrosequencing (n = 7) was employed to the quantification for methylated gene. The pubic databases including GWAS Catalog, TCGA and GEO were used. The immunohistochemistry and qRT-PCR analysis were performed. In contrast to adjacent tissues, GC tissues manifested pronounced hypermethylation patterns specifically within the promoter cytosine-phosphate-guanine (CpG) islands, indicating localized epigenetic alterations. DNA methylome analysis further revealed 4432 differentially-methylated probes (DMPs), with the gene PRKCB exhibited the most prominent average DNA methylation disparity (mean Δβ = 0.353). Pyrosequencing validation confirmed three DMPs within the PRKCB promoter (cg08406370, cg00735962, and cg18526361). Notably, the mean methylation levels of PRKCB were inversely correlated with mRNA expression levels in the GWAS Catalog. Furthermore, both mRNA and protein expression levels of PRKCB were significantly reduced in GCs when compared to their adjacent non-cancerous counterparts, verified by TCGA and GEO database. Our study reveals significant DNA methylation alterations in GC and emphasizes the pivotal role of PRKCB gene hypermethylation in conferring GC risk, which offers fresh perspectives for advancing diagnostic approaches and therapeutic strategies for GC.

Association between human blood metabolome and risk of myocarditis: a mendelian randomization study

Myocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant blood metabolites and the risk of myocarditis has not been well-established. To identify potential biometabolic markers associated with myocarditis, we conducted a two-sample Mendelian randomization (MR) study. We performed preliminary MR analysis using the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode methods to adjust for false discovery rate (FDR). Confounders were screened using the GWAS Catalog website. Sensitivity analyses included Cochrane Q-test, Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), scatterplots, funnel plots, and forest plots. For genetic and directional analysis, we employed co-localization analysis and the Steiger test. MR analysis was performed using the FinnGen database and meta-analysis was performed using the IEU database. MR analysis identified significant correlations for five metabolic biomarkers after FDR correction. These included four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, and 5-acetylamino-6-formylamino-3-methyluracil, as well as one unknown metabolite, X-25,422. Among these, kynurenine (OR = 1.441, 95%CI = 1.089–1.906, p-value = 0.018) and 1-stearoyl-GPE (18:0) (OR = 1.263, 95%CI = 1.029–1.550, p-value = 0.029) were identified as risk factors for myocarditis, while deoxycarnitine (OR = 0.813, 95%CI = 0.676–0.979, p-value = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95% CI = 0.775–0.962, p-value = 0.018), and X-25,422 (OR = 0.721, 95%CI = 0.587–0.886, p-value = 0.009) were found to be protective factors. No evidence of heterogeneity, horizontal pleiotropy, or sensitivity issues was observed, and no shared genetic factors between exposure and outcome were detected. The causality was in the correct direction. Meta-analysis further confirmed the causal relationship between the five metabolites and myocarditis. This study identifies a causal relationship between five circulating metabolites and myocarditis. Kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, X-25,422, and 5-acetylamino-6-formylamino-3-methyluracil may serve as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of the condition.

Causal associations of Sjögren's syndrome with cardiovascular disease: A two-sample Mendelian randomization study

Study objectivesObservational and cohort studies have associated Sjögren's syndrome (SS) with various types of cardiovascular disease (CVD), yet causal relationships have not been established. We employed Mendelian randomization (MR) to investigate potential causal links between SS and CVD in the general population. MethodsWe conducted a two-sample MR analysis using data from four distinct sources for 11 genome-wide significant single nucleotide polymorphisms (SNPs) associated with SS and data for 13 types of CVD sourced from FinnGen, IEU OpenGWAS, and GWAS catalog. The inverse variance weighted method was selected as the primary analytical approach, complemented by various sensitivity analyses. ResultsMR analyses provide evidence of a significantly increased risk of ischemic stroke associated with genetically predicted SS (odds ratio [OR], 1.0237; 95 % CI, 1.0096 to 1.0379; p = 0.0009), as well as suggestive evidence of a potential causal relationship between SS and an increased risk of chronic heart failure (OR, 1.0302; 95 % CI, 1.0020 to 1.0592; p = 0.0355). Sensitivity analyses reinforced these associations, demonstrating robustness and consistency across multiple statistical methods. The secondary analysis, conducted after outlier correction using MR-PRESSO and RadialMR methods, reaffirmed these associations and also indicated a suggestive causal link between SS and non-rheumatic valvular heart disease (OR, 1.0251; 95 % CI, 1.0021 to 1.0486; p = 0.0323). ConclusionsThis study demonstrates that genetically predicted SS is a potential causative risk factor for ischemic stroke, chronic heart failure, and non-rheumatic valvular heart disease on a large-scale population. However, further research incorporating ancestral diversity is required to confirm a causal relationship between SS and CVD.

Genetically predicted plasma metabolites mediate the causal relationship between gut microbiota and primary immune thrombocytopenia (ITP).

Primary immune thrombocytopenia (ITP) is an immune-mediated hematologic disorder characterized by a reduction in platelet count, increasing the risk of bleeding. Recent studies have indicated a close association between alterations in gut microbiota and the development of ITP. However, the mechanisms by which gut microbiota influence the occurrence and progression of ITP through plasma metabolites remain poorly understood. Evidence suggests extensive interactions between gut microbiota and plasma metabolites, implying a potential role for gut microbiota in influencing ITP through alterations in plasma metabolites, which requires further investigation. In this study, summarized GWAS data (including 211 gut microbiota taxa, 1,400 plasma metabolites or ratios, and an ITP patient cohort) were retrieved from the MiBioGen and GWAS Catalog databases. Using a two-sample Mendelian randomization (MR) approach, we screened gut microbiota and plasma metabolites potentially causally related to ITP. We further identified plasma metabolites serving as mediators through which gut microbiota affect ITP and calculated the strength of the mediation effect. To ensure result stability, we primarily used the inverse variance weighted (IVW) method as the main judgment index. We also utilized MR Egger and inverse variance weighted methods to detect heterogeneity in the results, and employed MR-Egger and MR-PRESSO methods to assess the presence of pleiotropy. Though two-sample MR analysis, 8 gut microbiota taxa were found to have causal relationships with ITP. After excluding six plasma metabolites with pleiotropy, 39 plasma metabolites were found to be causally related to ITP (P < 0.05). Eleven plasma metabolites were identified as having causal relationships between gut microbiota and plasma metabolites. Finally, using the delta method, it was calculated that Sphingomyelin levels (8.0%, 95%CI: 0.9% to 11.5%, P = 0.047) and Glucose-to-mannose ratio (6.5%, 95%CI: 0.7% to 9.5%, P = 0.039) are intermediates for Intestinimonas influencing ITP, while Bilirubin (Z,Z) to etiocholanolone glucuronide ratio (5.6%, 95%CI: 4.7% to 6.9%, P = 0.043) is an intermediate for Senegalimassilia influencing ITP. Gut microbiota can influence the development of ITP through changes in plasma metabolites. Sphingomyelin levels, Glucose-to-mannose ratio, and Bilirubin (Z,Z) to etiocholanolone glucuronide ratio are newly discovered intermediates through which gut microbiota influence ITP, providing potential indicators and targets for clinical diagnosis and treatment. This study highlights the intricate relationship between gut microbiota and plasma metabolites in the context of ITP, suggesting new avenues for clinical diagnosis and treatment.

Genetic Polymorphisms Associated with Fetal Hemoglobin (HbF) Levels and F-Cell Numbers: A Systematic Review of Genome-Wide Association Studies.

Elevated fetal hemoglobin (HbF), which is partly controlled by genetic modifiers, ameliorates disease severity in β hemoglobinopathies. Understanding the genetic basis of this trait holds great promise for personalized therapeutic approaches. PubMed, MedRxiv, and the GWAS Catalog were searched up to May 2024 to identify eligible GWAS studies following PRISMA guidelines. Four independent reviewers screened, extracted, and synthesized data using narrative and descriptive methods. Study quality was assessed using a modified version of the Q-Genie tool. Pathway enrichment analysis was conducted on gene lists derived from the selected GWAS studies. Out of 113 initially screened studies, 62 underwent full-text review, and 16 met the inclusion criteria for quality assessment and data synthesis. A total of 939 significant SNP-trait associations (p-value < 1 × 10-5) were identified, mapping to 133 genes (23 with overlapping variant positions) and 103 intergenic sequences. Most SNP-trait associations converged around BCL11A (chr.2), HBS1L-MYB, (chr.6), olfactory receptor and beta globin (HBB) gene clusters (chr.11), with less frequent loci including FHIT (chr.3), ALDH8A1, BACH2, RPS6KA2, SGK1 (chr.6), JAZF1 (chr.7), MMP26 (chr.11), COCH (chr.14), ABCC1 (chr.16), CTC1, PFAS (chr.17), GCDH, KLF1, NFIX, and ZBTB7A (chr.19). Pathway analysis highlighted Gene Ontology (GO) terms and pathways related to olfaction, hemoglobin and haptoglobin binding, and oxygen carrier activity. This systematic review confirms established genetic modifiers of HbF level, while highlighting less frequently associated loci as promising areas for further research. Expanding research across ethnic populations is essential for advancing personalized therapies and enhancing outcomes for individuals with sickle cell disease or β-thalassemia.

The association between dietary consumption habits and psoriasis: a two-sample Mendelian randomization study.

Psoriasis is a chronic inflammatory skin disease characterized primarily by erythema and scales, having a wide-ranging impact globally. Previous studies have suggested that dietary consumption habits may influence psoriasis. The objective of this study was to determine the causal relationship between dietary consumption habits and psoriasis using the Mendelian Randomization (MR) approach. SNP data for 29 dietary consumption habits and psoriasis were obtained from the GWAS catalog database and the FinnGen database, respectively. The Mendelian Randomization analysis was performed using R software, with the 29 dietary consumption habits as the exposure factors and psoriasis as the outcome. Three MR analysis methods-Inverse Variance Weighted (IVW), Weighted Median Estimator (WME), and MR-Egger regression-were employed to study the causal relationship between dietary consumption habits and psoriasis. The IVW analysis indicated an OR (95%CI) of 0.065 (0.008-0.555), p = 0.012, demonstrating a negative correlation between the consumption of dried fruit and psoriasis. Among the 29 dietary consumption habits analyzed, the intake of dried fruits is a protective factor against psoriasis. Therefore, it is clinically advisable to appropriately increase the intake of dried fruits among patients with psoriasis, serving as a nutritional therapy method in conjunction with pharmacological treatment.

Blood Protein Ratios Reveal New Diagnostic Biomarkers for Prostate Cancer: A Study from the Perspective of Mendelian Randomization

Prostate cancer (PCa) is a leading malignancy affecting men globally, contributing significantly to cancer-related morbidity and mortality. Our study aims to explore causal relationships between blood protein ratios (BPR) and PCa using a Mendelian randomization (MR) approach, potentially identifying new diagnostic and therapeutic targets. Methods: A two-sample MR method was employed, utilizing genetic variants as instrumental variables (IVs) to infer causality between circulating BPR and PCa. Data on BPR were obtained from a proteomics study of the UK Biobank, while PCa data were sourced from FinnGen, the PRACTICLE Consortium, and the GWAS Catalog. Stringent criteria were applied for IV selection, and statistical analyses included the inverse-variance weighted (IVW) method with sensitivity analyses to address pleiotropy and heterogeneity. Results: Significant causal associations were identified between several BPR and PCa. Notably, the ratios of CEBPB/PXN, APBB1IP/NCF2, APP/EGF, and CRKL/ EGF were found to be protective against PCa, while the ratios of ARHGAP1/RAD23B, EGF/ TNFSF14, and GOLM2/STC1 were identified as risk factors. Reverse MR analysis suggested that PCa might act as a protective factor for the GOLM2/STC1 ratio. Sensitivity analyses confirmed the robustness of these findings. Conclusions: This study elucidates significant causal relationships between 7 BPR and PCa, offering new insights for diagnosis, treatment evaluation, and personalized therapeutic strategies. Future research should focus on validating these findings and exploring the underlying biological mechanisms to improve PCa management.

Efficient indexing and querying of annotations in a pangenome graph.

The current reference genome is the backbone of diverse and rich annotations. Simple text formats, like VCF or BED, have been widely adopted and helped the critical exchange of genomic information. There is a dire need for tools and formats enabling pangenomic annotation to facilitate such enrichment of pangenomic references. The Graph Alignment Format (GAF) is a text format, tab-delimited like BED/VCF files, which was proposed to represent alignments. GAF could also be used to store paths representing annotations in a pangenome graph, but there are no tools to index and query them efficiently. Here, we present extensions to vg and HTSlib that provide efficient sorting, indexing, and querying for GAF files. With this approach, annotations overlapping a subgraph can be extracted quickly. Paths are sorted based on the IDs of traversed nodes, compressed with BGZIP, and indexed with HTSlib/tabix via our extensions for the GAF format. Compared to the binary GAM format, GAF files are easier to edit or inspect because they are plain text, and we show that they are twice as fast to sort and half as large on disk. In addition, we updated vg annotate , which takes BED or GFF3 annotation files relative to linear sequences and projects them into the pangenome. It can now produce GAF files representing these annotations' paths through the pangenome. We showcase these new tools on several applications. We projected annotations for all Human Pangenome Reference Consortium Year 1 haplotypes, including genes, segmental duplications, tandem repeats and repeats annotations, into the Minigraph-Cactus pangenome (GRCh38-based v1.1). We also projected known variants from the GWAS Catalog and expression QTLs from the GTEx project into the pangenome. Finally, we reanalyzed ATAC-seq data from ENCODE to demonstrate what a coverage track could look like in a pangenome graph. These rich annotations can be quickly queried with vg and visualized using existing tools like the Sequence Tube Map or Bandage.

Validation of TYK2 and exploration of PRSS36 as drug targets for psoriasis using Mendelian randomization

Psoriasis is a chronic inflammatory skin disorder with multiple causes, including genetic and environmental factors. Despite advances in treatment, there remains a need to identify novel therapeutic targets. A Mendelian randomization (MR) analysis was conducted to identify therapeutic targets for psoriasis. Data on cis-expression quantitative trait loci were obtained from the eQTLGen Consortium (n = 31,684). Summary statistics for psoriasis (outcome) were sourced from the GWAS Catalog with a sample size of 484,598, including 5,427 cases and 479,171 controls. Colocalization analysis was used to assess whether psoriasis risk and gene expression were driven by shared single nucleotide polymorphisms. Drug prediction and molecular docking were utilized to validate the pharmacological value of the drug targets. The MR analysis found that 81 drug targets were significantly associated, and two (TYK2 and PRSS36) were supported by colocalization analysis (PP.H4 > 0.80). Phenome-wide association studies did not show any associations with other traits at the gene level. Biologically, these genes were closely related to immune function. Molecular docking revealed strong binding with drugs and proteins, as supported by available structural data. This study validated TYK2 as a drug target for psoriasis, in line with its existing clinical use, including the development of decucravacitinib. PRSS36 is a potential novel target requiring further investigation.

Search for signals of positive selection of circadian rhythm genes PER1, PER2, PER3 in different human populations.

The diversity of geographically distributed human populations shows considerable variation in external and internal traits of individuals. Such differences are largely attributed to genetic adaptation to various environmental influences, which include changes in climatic conditions, variations in sleep and wakefulness, dietary variations, and others. Whole-genome data from individuals of different populations make it possible to determine the specific genetic sites responsible for adaptations and to further understand the genetic structure underlying human adaptive characteristics. In this article, we searched for signals of single nucleotide polymorphisms (SNPs) under selection pressure in people of different populations. To identify selection signals in different population groups, the PER1, PER2 and PER3 genes that are involved in the coordination of thermogenic functions and regulation of circadian rhythms, which is directly reflected in the adaptive abilities of the organism, were investigated. Data were analyzed using publicly available data from the 1000 Genomes Project for 23 populations. The Extended Haplotype Homozygosity Score statistical method was chosen to search for traces of selection. The comparative analysis performed identified points subject to selection pressure. The SNPs were annotated through the GWAS catalog and manually by analyzing Internet resources. This study suggests that living conditions, climate, and other external factors directly influence the genetic structure of populations and vary across races and geographic locations. In addition, many of the selection variants in the PER1, PER2, PER3 genes appear to regulate biological processes that are associated with major modern diseases, including obesity, cancer, metabolic syndrome, bipolar personality disorder, depression, rheumatoid arthritis, diabetes mellitus, lupus erythematosus, stroke and Alzheimer's disease, making them extremely interesting targets for further research aimed at identifying the genetic causes of human disease.

Proteome-wide mendelian randomization identifies novel therapeutic targets for chronic kidney disease.

There is an urgent need to pinpoint novel targets for drug discovery in the context of chronic kidney disease (CKD), and the proteome represents a significant pool of potential therapeutic targets. To address this, we performed proteome-wide analyses using Mendelian randomization (MR) and colocalization techniques to uncover potential targets for CKD. We extracted summary-level data from the ARIC study, focusing on 7213 European American (EA) individuals and 4657 plasma proteins. To broaden our analysis, we incorporated genetic association data from Icelandic cohorts, thereby enhancing our investigation into the correlations with chronic kidney disease (CKD), creatinine-based estimated glomerular filtration rate (eGFRcrea), and estimated glomerular filtration rate (eGFR). We utilized genetic association data from the GWAS Catalog, including CKD (765,348, 625,219 European ancestry and 140,129 non-European ancestry), eGFRcrea (1,004,040, European ancestry), and eGFR (567,460, European ancestry). Employing MR analysis, we estimated the associations between proteins and CKD risk. Additionally, we conducted colocalization analysis to evaluate the existence of shared causal variants between the identified proteins and CKD. We detected notable correlations between levels predicted based on genetics of three circulating proteins and CKD, eGFRcrea, and eGFR. Notably, our colocalization analysis provided robust evidence supporting these associations. Specifically, genetically predicted levels of Transcription elongation factor A protein 2 (TCEA2) and Neuregulin-4 (NRG4) exhibited an inverse relationship with CKD risk, while Glucokinase regulatory protein (GCKR) showed an increased risk of CKD. Furthermore, our colocalization analysis also supported the associations of TCEA2, NRG4, and GCKR with the risk of eGFRcrea and eGFR.

Sports-Related Genomic Predictors Are Associated with Athlete Status in Chinese Sprint/Power Athletes.

Objectives: The aim of this study was to assess the relationship between variant loci significantly associated with sports-related traits in the GWAS Catalog database and sprint/power athlete status, as well as to explore the polygenic profile of elite athletes. Methods: Next-generation sequencing and microarray technology were used to genotype samples from 211 elite athletes who had achieved success in national or international competitions in power-based sports and from 522 non-athletes, who were healthy university students with no history of professional sports training. Variant loci collected from databases were extracted after imputation. Subsequently, 80% of the samples were randomly selected as the training set, and the remaining 20% as the validation set. Results: Association analysis of variant loci was conducted in the training set, and individual Total Genotype Score (TGS) were calculated using genotype dosage and lnOR, followed by the establishment of a logistic model, with predictive performance evaluated in the validation set. Association analysis was performed on 2075 variant loci, and after removing linked loci (r2 > 0.2), 118 Tag SNPs (p ≤ 0.05) were identified. A logistic model built using 30 Tag SNPs (p ≤ 0.01) showed better performance in the validation set (AUC = 0.707). Conclusions: Our study identified 30 new genetic molecular markers and demonstrated that elite sprint/power athlete status is polygenic.

Linkage Disequilibrium-Informed Deep Learning Framework to Identify Genetic Loci for Alzheimer's Disease Using Whole Genome Sequencing Data.

The exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk. The Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7,416 non-Hispanic white participants (3,150 cognitively normal older adults (CN), 4,266 AD). First, 30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep-Block identified novel SNPs within the top 1,500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. The results were cross-validated against established AD-associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog. The Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving interactions between SNPs in performing the dimensionality reduction, which can potentially introduce bias or lead to information loss. The Deep-Block approach identified both known and novel genetic variation, enhancing our understanding of the genetic architecture of and demonstrating the framework's potential for application in large-scale sequencing studies.

Can alterations in cathepsin levels restrain the development of skin cancer?: A bidirectional multivariate Mendelian-randomization study.

Malignant skin tumors mainly include basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. There is currently observational research suggesting that changes in cathepsin (CTS) may be a factor in the development of malignant skin tumors, but no studies have yet demonstrated a causal relationship between tissue protease changes and the occurrence of malignant skin tumors. Current studies have shown that cathepsin is involved in tumor cell invasion and metastasis by regulating growth factors and cellular immune function in tumor microenvironment, decomposing extracellular matrix and basement membrane, and promoting angiogenesis. In this study, we conducted a bidirectional Mendelian-randomization study using publicly available genome-wide association study (GWAS; GWAS Catalog) data. This study applies a bidirectional multivariate Mendelian randomization (MR) approach to investigate the causal relationship between cathepsin, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. In cases where multiple cathepsins are implicated as etiological factors in certain diseases, a multivariable analysis is conducted to assess the direct and indirect causal effects of the exposure factors. In this study, we present a comprehensive MR analysis to investigate the relationship between 9 cathepsin and basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. Based on our MR analysis using the largest GWAS Catalog dataset available, we are able to draw relatively reliable conclusions. In the MR study, we found that tissue protease L2 can promote skin cancer, Cathepsin O, and Cathepsin F are associated with an increased risk of basal cell carcinoma. Cathepsin H can inhibit basal cell carcinoma and malignant melanoma. In the reverse MR study, it was found that squamous cell carcinoma may cause an increase in Cathepsin O expression. In the multivariate analysis, it was found that Cathepsin H is a direct factor in reducing the occurrence of skin cancer and melanoma, with no apparent causal relationship to non-melanoma skin cancer. Cathepsin has a dual impact on skin cancer cells, and the expression of different cathepsins at the edge of skin tumors may indicate different developmental tendencies of skin cancer. Cathepsin may serve as effective biomarkers for predicting tumors.

A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study.

The Long Life Family Study (LLFS) enrolled 4953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8 × 10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform ( https://nf-co.re/omicsgenetraitassociation ).

Causal relationship between Brugada syndrome and electrocardiogram traits: A bidirectional Mendelian randomization study

IntroductionObservational studies have suggested associations between Brugada syndrome (BrS) and electrocardiograms traits. Nonetheless, the causal relationships remains uncertain in observational studies. This study aims to investigate the causal relationships between BrS phenotypic risk and electrocardiogram traits using Mendelian randomization (MR) analysis and colocalization analysis. MethodsMR analysis was performed to investigate the causal relationships between BrS phenotype risk and electrocardiogram traits (P wave duration, PR interval, QRS wave duration, ST segment duration, T wave duration, QT interval, heart rate (HR) and heart rate variability). The genetic instruments for BrS (number of cases = 12,821) were obtained from the latest GWAS. GWAS summary data of electrocardiogram traits were obtained from the MRC-IEU and GWAS catalog databases. The causal relationships were obtained through MR methods, and sensitivity analyses (e.g. Cochran's Q test, MR-PRESSO). Furthermore, the causal relationships were evaluated whether they were driven by one linkage disequilibrium using colocalization analysis. ResultsWe found that there are positive causal relationships between BrS phenotypic risk and P wave duration, PR interval, QRS wave duration and QT interval, respectively (IVWP: β = 1.238, 95 % CI = 0.857–1.619, P<0.001; IVWPR: β = 2.199, 95 % CI = 1.358–3.039, P<0.001; IVWQRS: β = 0.157, 95 % CI = 0.115–0.198, P<0.001; IVWQT: β = 0.593, 95 % CI = 0.391–0.796, P<0.001), and there is a negative causal relationship between BrS phenotypic risk and heart rate (IVWHR: β = −0.023, 95 % CI = −0.03 ∼ −0.015, P<0.001). Additionally, there are bidirectional causal relationships between BrS phenotypic risk and P wave duration and PR interval, respectively (IVWP: OR = 1.217, 95 % CI = 1.118–1.325, P<0.001; IVWPR: OR = 1.02, 95 % CI = 1.008–1.032, P = 0.001). Furthermore, colocalization analysis identified that the causal relationships between BrS phenotype risk and P wave duration, PR interval and QRS wave duration were driven by rs6790396, rs6801957 and rs6801957, respectively. ConclusionsBidirectional causal relationships were identified between BrS phenotypic risk and P wave duration and PR interval, respectively. There were positive causal relationships between BrS phenotypic risk and QRS wave duration and QT interval, respectively, and there is a negative causal relationship between BrS phenotypic risk and heart rate.

Exploring genetic associations between metabolites and atopic dermatitis: insights from bidirectional Mendelian randomization analysis in European population.

There is growing evidence indicating a complex interaction between blood metabolites and atopic dermatitis (AD). The objective of this study was to investigate and quantify the potential influence of plasma metabolites on AD through Mendelian randomization (MR) analysis. Our procedures followed these steps: instrument variable selection, primary analysis, replication analysis, Meta-analysis of results, reverse MR analysis, and multivariate MR (MVMR) analysis. In our study, the exposure factors were derived from the Canadian Longitudinal Study on Aging (CLSA), encompassing 8,299 individuals of European descent and identifying 1,091 plasma metabolites and 309 metabolite ratios. In primary analysis, AD data, was sourced from the GWAS catalog (Accession ID: GCST90244787), comprising 60,653 cases and 804,329 controls. For replication, AD data from the Finnish R10 database included 15,208 cases and 367,046 controls. We primarily utilized the inverse variance weighting method to assess the causal relationship between blood metabolites and AD. Our study identified significant causal relationships between nine genetically predicted blood metabolites and AD. Specifically, 1-palmitoyl-2-stearoyl-GPC (16:0/18:0) (OR = 0.92, 95% CI 0.89-0.94), 1-methylnicotinamide (OR = 0.93, 95% CI 0.89-0.98), linoleoyl-arachidonoyl-glycerol (18:2/20:4) [1] (OR = 0.94, 95% CI 0.92-0.96), and 1-arachidonoyl-GPC (20:4n6) (OR = 0.94, 95% CI 0.92-0.96) were associated with a reduced risk of AD. Conversely, phosphate / linoleoyl-arachidonoyl-glycerol (18:2/20:4) [2] (OR = 1.07, 95% CI 1.04-1.10), docosatrienoate (22:3n3) (OR = 1.07, 95% CI 1.04-1.10), retinol (Vitamin A) / linoleoyl-arachidonoyl-glycerol (18:2/20:4) [2] (OR = 1.08, 95% CI 1.05-1.11), retinol (Vitamin A) / linoleoyl-arachidonoylglycerol (18:2/20:4) [1] (OR = 1.08, 95% CI 1.05-1.12), and phosphate / linoleoyl-arachidonoyl-glycerol (18:2/20:4) [1] (OR = 1.09, 95% CI 1.07-1.12 were associated with an increased risk of AD. No evidence of reverse causality was found in the previously significant results. MVMR analysis further confirmed that 1-palmitoyl-2-stearoyl-GPC (16:0/18:0) and 1-methylnicotinamide are independent and dominant contributors to the development of AD. Our study revealed a causal relationship between genetically predicted blood metabolites and AD. This discovery offers specific targets for drug development in the treatment of AD patients and provides valuable insights for investigating the underlying mechanisms of AD in future research.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective.

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.