Identification of Causal Genes of COVID-19 Using the SMR Method

Discovery of runs-of-homozygosity diplotype clusters and their associations with diseases in UK Biobank

Runs-of-homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE (runs-of-homozygous diplotype cluster enumerator), to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 single nucleotide polymorphisms (SNPs) and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended human leukocyte antigen (HLA) region and autoimmune disorders. We found an association between a diplotype covering the homeostatic iron regulator (HFE) gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (p-value = 1.82 × 10-11). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale.

COVID-19 progression towards ARDS: a genome wide study reveals host factors underlying critical COVID-19

Coronavirus disease 2019 (COVID-19) is a viral infection produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus epidemic, which was declared a global pandemic in March 2020. The World Health Organization has recorded around 43.3 billion cases and 59.4 million casualties to date, posing a severe threat to global health. Severe COVID-19 indicates viral pneumonia caused by the SARS-CoV-2 infections, which can induce fatal consequences, including acute respiratory distress syndrome (ARDS). The purpose of this research is to better understand the COVID-19 and ARDS pathways, as well as to find targeted single nucleotide polymorphism. To accomplish this, we retrieved over 100 patients' samples from the Sequence Read Archive, National Center for Biotechnology Information. These sequences were processed through the Galaxy server next generation sequencing pipeline for variant analysis and then visualized in the Integrative Genomics Viewer, and performed statistical analysis using t-tests and Bonferroni correction, where six major genes were identified as DNAH7, CLUAP1, PPA2, PAPSS1, TLR4, and IFITM3. Furthermore, a complete understanding of the genomes of COVID-19-related ARDS will aid in the early identification and treatment of target proteins. Finally, the discovery of novel therapeutics based on discovered proteins can assist to slow the progression of ARDS and lower fatality rates.

MARVEL: an integrated alternative splicing analysis platform for single-cell RNA sequencing data

Alternative splicing is an important source of heterogeneity underlying gene expression between individual cells but remains an understudied area due to the paucity of computational tools to analyze splicing dynamics at single-cell resolution. Here, we present MARVEL, a comprehensive R package for single-cell splicing analysis applicable to RNA sequencing generated from the plate- and droplet-based methods. We performed extensive benchmarking of MARVEL against available tools and demonstrated its utility by analyzing multiple publicly available datasets in diverse cell types, including in disease. MARVEL enables systematic and integrated splicing and gene expression analysis of single cells to characterize the splicing landscape and reveal biological insights.

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients

COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.

Inflammatory bowel disease and cardiovascular disease: A two-sample Mendelian randomization analysis

Background

Although epidemiological studies have shown a positive relationship between inflammatory bowel disease (IBD) and risk of cardiovascular disease (CVD) outcomes, a solid causal relationship has not been established. Thus, a two-sample Mendelian randomization (MR) study was conducted to explore the potential causal effect between IBD and CVD outcomes.

Methods

We performed a two-sample MR analysis to analyze the causal effect of the IBD on CVD outcome by using summary-level genome-wide association studies of European descent. The inverse-variance weighted (IVW) method was used as the main MR analysis, with complementary analyses of MR Egger, maximum likelihood, weighted median, penalized weighted media, simple mode, weighted mode, and MR-PRESSO methods. Multiple sensitivity analyses were used to evaluate the robustness of our results.

Results

All P-values were greater than 0.05 in the IVW method, showing no evidence of a causal association between circulating IBD and CVD. Similar results were observed by using other MR methods. No evidence of heterogeneity, pleiotropy, or outlier single-nucleotide polymorphisms was detected. Sensitivity analyses demonstrated the robustness of the results.

Conclusion

The findings of this study provided no evidence to support that IBD has a large effect on risk of CVD outcomes, which is in contrast to many previous observational reports. Further studies are needed to determine the potential mechanism of association identified in observational studies.