Table1.xlsx

<p>High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.</p

Table3.xlsx

<p>High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.</p

Table2.xlsx

<p>High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.</p

DataSheet1.docx

<p>High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.</p

Table5.xlsx

<p>High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.</p

Clinical presentation, disease course and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease – a cohort study across eighteen countries

AimsPatients with cardiac disease are considered high risk for poor outcomes following hospitalization with COVID-19. The primary aim of this study was to evaluate heterogeneity in associations between various heart disease subtypes and in-hospital mortality.Methods and resultsWe used data from the CAPACITY-COVID registry and LEOSS study. Multivariable Poisson regression models were fitted to assess the association between different types of pre-existing heart disease and in-hospital mortality. A total of 16 511 patients with COVID-19 were included (21.1% aged 66–75 years; 40.2% female) and 31.5% had a history of heart disease. Patients with heart disease were older, predominantly male, and often had other comorbid conditions when compared with those without. Mortality was higher in patients with cardiac disease (29.7%; n = 1545 vs. 15.9%; n = 1797). However, following multivariable adjustment, this difference was not significant [adjusted risk ratio (aRR) 1.08, 95% confidence interval (CI) 1.02–1.15; P = 0.12 (corrected for multiple testing)]. Associations with in-hospital mortality by heart disease subtypes differed considerably, with the strongest association for heart failure (aRR 1.19, 95% CI 1.10–1.30; P ConclusionConsiderable heterogeneity exists in the strength of association between heart disease subtypes and in-hospital mortality. Of all patients with heart disease, those with heart failure are at greatest risk of death when hospitalized with COVID-19. Serious cardiac complications are rare during hospitalization.</div

Schematic description of the association model used to quantify the overlap between regulatory elements (black and red DHS peaks) and disease-associated variants (SNP1, 2 and 3) in various cell types (cell type 1, 2 and 3).

<p>The model estimates the regulatory activity by counting the number of DHS peaks (red) located within the region of a 500kb with the associated tag SNP in the middle.</p

Tissues and cell types associated with common variants identified by selected GWASs.

<p>Each point represents the separate replicate of DHS assay in the given tissue.</p

Systematic association of common variants identified by 94 different GWAS with open chromatin (DHS) in selected tissues and cells.

<p>The individual GWAS are sorted based on the minimal association p-value with 466 DHS (only selected DHS samples are shown). Size of the dot represents the enrichment over the null distribution and color of the dot represents the significance of the enrichment—with black being the most significant. The bar graphs depict the number of associated SNPs (grey), the fraction of coding SNPs (red) and minor allele frequency (green). Heatmap depicts the distribution of SNPs with respect to transcriptional start sites (intensity of blue depicts the fraction of SNPs within the distance bin). Green/orange/white bar shows the presence of the replication cohort (green—present, white—no replication cohort as specified in the in NHGRI GWAS Catalog) separately for each GWAS. Group bar depicts the annotation of GWAS to one of three “GWAS groups”. Green—GWAS with the best association p-value < = 0.0001, orange < = 0.01 and red—GWAS with the best association p-value < 0.01.</p

Comparison of selected GWAS parameter between three GWAS groups.

<p>A) The number of associated SNPs B) the fraction of coding SNPs C) minor allele frequency and D) the distribution of SNPs with respect to transcriptional start sites (no line—non significant, * p-value < 0.05, ** p-value < 0.01, using Wilcoxon signed-rank test), green—GWAS with the best association p-value < = 0.0001, orange < = 0.01 and red—GWAS with the best association p-value < 0.01.</p