An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility

Context-specific eQTLs provide deeper insight into causal genes underlying shared genetic architecture of COVID-19 and idiopathic pulmonary fibrosis

Most genetic variants identified through genome-wide association studies (GWASs) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell type- and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of idiopathic pulmonary fibrosis (IPF) and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWASs, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.

Identification of potential drug targets for pelvic organ prolapse using a proteome-wide Mendelian randomization approach

Pelvic organ prolapse (POP) significantly impacts patients' quality of life, and current treatment options remain limited due to high recurrence rates, making the exploration of new therapeutic targets essential. Using data from the FinnGen cohort, we performed a proteome-wide Mendelian randomization (PW-MR) analysis. Through PW-MR and Bayesian colocalization analyses, we identified EFEMP1 and MFAP4 as potential key drug targets, with EFEMP1 potentially exerting a protective effect, whereas MFAP4 may be associated with an increased risk of POP. To further support these findings, we analysed single-cell RNA sequencing data to evaluate the expression patterns of EFEMP1 and MFAP4 in different cell populations. The analysis revealed that EFEMP1 and MFAP4 are specifically enriched in cell types involved in tissue remodelling and fibrosis. Findings of phenome-wide association studies indicated that the risk of side effects for these targets may be low, suggesting the safety of treatment focused on these targets. Preliminary molecular docking analysis findings suggested that EFEMP1 and MFAP4 may have strong binding affinities with candidate drugs, further supporting the feasibility of EFEMP1 and MFAP4 as drug targets. In conclusion, our findings indicate that EFEMP1 and MFAP4 are promising therapeutic targets for POP, providing important insights for the development of safe and effective treatments.

Genetic evidence for the causal effect of clonal hematopoiesis on pulmonary arterial hypertension

BACKGROUND

Pulmonary arterial hypertension (PAH) is a severe and progressive cardiovascular disease. While potential links between clonal hematopoiesis (CH) and cardiovascular diseases have been identified, the causal relationship between CH and PAH remains unclear. This study aims to investigate the causal effect of CH on the risk of PAH using a two-sample Mendelian randomization (MR) approach.

METHODS

We utilized genetic variants associated with CH as instrumental variables, identified from two large genome-wide association studies (GWAS) involving 359,088 participants in the discovery cohort and 184,121 participants in the validation cohort, all of European descent. We obtained GWAS summary statistics for PAH. The inverse-variance weighted (IVW) method was employed as the primary analysis, complemented by sensitivity analyses to assess the robustness of our findings. A bidirectional MR analysis was conducted to estimate the causation between CH and PAH.

RESULTS

Our results indicate a causal effect of CH on the risk of PAH in the discovery cohort, with TET2 showing an IVW odds ratio (OR) of 1.200 (95% CI: 1.001-1.438, P = 0.049). Sensitivity analysis did not reveal significant pleiotropy or heterogeneity. In the validation cohort, we found that TET2 remains a risk factor for PAH (OR = 2.3E + 08, 95% CI 17.007-3.1E + 15, P = 0.022). Additionally, no causal relationship was found between other CH genes, such as DNMT3A and PAH (P > 0.05). The reverse MR analysis provided no evidence of causal effects of PAH on CH.

CONCLUSION

These findings showed that individuals with CH due to TET2 mutations may have a higher risk of developing PAH, suggesting that the CH patients may be tested for TET2 gene mutations.

Biological sex affects functional variation across the human genome

Humans display sexual dimorphism across many traits, but little is known about underlying genetic mechanisms and impacts on disease. We utilized single-cell RNA-seq of 480 lymphoblastoid cell lines to reveal that the vast majority (79%) of sex-biased genes are targets of transcription factors that display sex-biased expression. Further, we developed a two-step regression method that identified sex-biased expression quantitative trait loci (sb-eQTL) across the genome. In contrast to previous work, these sb-eQTL are abundant (n=10,754; FDR 5%) and reproducible (replication up to π1=0.56). These sb-eQTL are enriched in over 600 GWAS phenotypes, including 120 sb-eQTL associated with the female-biased autoimmune disease multiple sclerosis. Our results demonstrate widespread genetic impacts on sexual dimorphism and identify possible mechanisms and clinical targets for sex differences in diverse diseases.

Context-specific eQTLs reveal causal genes underlying shared genetic architecture of critically ill COVID-19 and idiopathic pulmonary fibrosis

Most genetic variants identified through genome-wide association studies (GWAS) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell-type and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of IPF and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWAS, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.

Single nucleotide variants in the CCL2, OAS1 and DPP9 genes and their association with the severity of COVID-19 in an Ecuadorian population

COVID-19 has a broad clinical spectrum, ranging from asymptomatic-mild form to severe phenotype. The severity of COVID-19 is a complex trait influenced by various genetic and environmental factors. Ethnic differences have been observed in relation to COVID-19 severity during the pandemic. It is currently unknown whether genetic variations may contribute to the increased risk of severity observed in Latin-American individuals The aim of this study is to investigate the potential correlation between gene variants at CCL2, OAS1, and DPP9 genes and the severity of COVID-19 in a population from Quito, Ecuador. This observational case-control study was conducted at the Carrera de Biologia from the Universidad Central del Ecuador and the Hospital Quito Sur of the Instituto Ecuatoriano de Seguridad Social (Quito-SUR-IESS), Quito, Ecuador. Genotyping for gene variants at rs1024611 (A>G), rs10774671 (A>G), and rs10406145 (G>C) of CCL2, OAS1, and DPP9 genes was performed on 100 COVID-19 patients (43 with severe form and 57 asymptomatic-mild) using RFLP-PCR. The genotype distribution of all SNVs throughout the entire sample of 100 individuals showed Hardy Weinberg equilibrium (P=0.53, 0.35, and 0.4 for CCL2, OAS1, and DPP9, respectively). The HWE test did not find any statistically significant difference in genotype distribution between the study and control groups for any of the three SNVs. The multivariable logistic regression analysis showed that individuals with the GG of the CCL2 rs1024611 gene variant had an increased association with the severe COVID-19 phenotype in a recessive model (P = 0.0003, OR = 6.43, 95% CI 2.19-18.89) and for the OAS1 rs10774671 gene variant, the log-additive model showed a significant association with the severe phenotype of COVID-19 (P=0.0084, OR=3.85, 95% CI 1.33-11.12). Analysis of haplotype frequencies revealed that the coexistence of GAG at CCL2, OAS1, and DPP9 variants, respectively, in the same individual increased the presence of the severe COVID-19 phenotype (OR=2.273, 95% CI: 1.271-4.068, P=0.005305). The findings of the current study suggests that the ethnic background affects the allele and genotype frequencies of genes associated with the severity of COVID-19. The experience with COVID-19 has provided an opportunity to identify an ethnicity-based approach to recognize genetically high-risk individuals in different populations for emerging diseases.

Combating DC-SIGN-mediated SARS-CoV-2 dissemination by glycan-mimicking polymers

Many viruses exploit the human C-type lectin receptor dendritic cell-specific ICAM-3 grabbing nonintegrin (DC-SIGN) for cell entry and virus dissemination. An inhibition of DC-SIGN-mediated virus attachment by glycan-derived ligands has, thus, emerged as a promising strategy toward broad-spectrum antiviral therapeutics. In this contribution, several cognate fragments of oligomannose- and complex-type glycans grafted onto a poly-l-lysine scaffold are evaluated as polyvalent DC-SIGN ligands. The range of selected carbohydrate epitopes encompasses linear (α- d-Man-(1→2)-α- d-Man, α- d-Man-(1→2)-α- d-Man-(1→2)-α- d-Man-(1→3)-α- d-Man) and branched (α- d-Man-(1→6)-[α- d-Man-(1→3)]-α- d-Man) oligomannosides, as well as α- l-Fuc. The thermodynamics of binding are investigated on a mono- and multivalent level to shed light on the molecular details of the interactions with the tetravalent receptor. Cellular models of virus attachment and DC-SIGN-mediated virus dissemination reveal a high potency of the presented glycopolymers in the low pico- and nanomolar ranges, respectively. The high activity of oligomannose epitopes in combination with the biocompatible properties of the poly- l-lysine scaffold highlights the potential for further preclinical development of polyvalent DC-SIGN ligands.

Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation

There is currently no medical therapy to prevent calcific aortic valve stenosis (CAVS). Multi-omics approaches could lead to the identification of novel molecular targets. Here, we perform a genome-wide association study (GWAS) meta-analysis including 14,819 cases among 941,863 participants of European ancestry. We report 32 genomic loci, among which 20 are novel. RNA sequencing of 500 human aortic valves highlights an enrichment in expression regulation at these loci and prioritizes candidate causal genes. Homozygous genotype for a risk variant near TWIST1, a gene involved in endothelial-mesenchymal transition, has a profound impact on aortic valve transcriptomics. We identify five genes outside of GWAS loci by combining a transcriptome-wide association study, colocalization, and Mendelian randomization analyses. Using cross-phenotype and phenome-wide approaches, we highlight the role of circulating lipoproteins, blood pressure and inflammation in the disease process. Our findings pave the way for the development of novel therapies for CAVS.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

MOTIVATION

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.

RESULTS

Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

AVAILABILITY AND IMPLEMENTATION

The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Host genetic variants associated with COVID-19 reconsidered in a Slovak cohort

We present the results of an association study involving hospitalized coronavirus disease 2019 (COVID-19) patients with a clinical background during the 3rd pandemic wave of COVID-19 in Slovakia. Seventeen single nucleotide variants (SNVs) in the eleven most relevant genes, according to the COVID-19 Host Genetics Initiative, were investigated. Our study confirms the validity of the influence of LZTFL1 and 2'-5'-oligoadenylate synthetase (OAS)1/OAS3 genetic variants on the severity of COVID-19. For two LZTFL1 SNVs in complete linkage disequilibrium, rs17713054 and rs73064425, the odds ratios of baseline allelic associations and logistic regressions (LR) adjusted for age and sex ranged in the four tested designs from 2.04 to 2.41 and from 2.05 to 3.98, respectively. The OAS1/OAS3 haplotype 'gttg' carrying a functional allele G of splice-acceptor variant rs10774671 manifested its protective function in the Delta pandemic wave. Significant baseline allelic associations of two DPP9 variants in all tested designs and two IFNAR2 variants in the Omicron pandemic wave were not confirmed by adjusted LR. Nevertheless, adjusted LR showed significant associations of NOTCH4 rs3131294 and TYK2 rs2304256 variants with severity of COVID-19. Hospitalized patients' reported comorbidities were not correlated with genetic variants, except for obesity, smoking (IFNAR2), and hypertension (NOTCH4). The results of our study suggest that host genetic variations have an impact on the severity and duration of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Considering the differences in allelic associations between pandemic waves, they support the hypothesis that every new SARS-CoV-2 variant may modify the host immune response by reconfiguring involved pathways.

A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review

Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incremental-performance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Identification of genetic loci jointly influencing COVID-19 and coronary heart diseases

BACKGROUND

Comorbidities of coronavirus disease 2019 (COVID-19)/coronary heart disease (CHD) pose great threats to disease outcomes, yet little is known about their shared pathology. The study aimed to examine whether comorbidities of COVID-19/CHD involved shared genetic pathology, as well as to clarify the shared genetic variants predisposing risks common to COVID-19 severity and CHD risks.

METHODS

By leveraging publicly available summary statistics, we assessed the genetically determined causality between COVID-19 and CHD with bidirectional Mendelian randomization. To further quantify the causality contributed by shared genetic variants, we interrogated their genetic correlation with the linkage disequilibrium score regression method. Bayesian colocalization analysis coupled with conditional/conjunctional false discovery rate analysis was applied to decipher the shared causal single nucleotide polymorphisms (SNPs).

FINDINGS

Briefly, we observed that the incident CHD risks post COVID-19 infection were partially determined by shared genetic variants. The shared genetic variants contributed to the causality at a proportion of 0.18 (95% CI 0.18-0.19) to 0.23 (95% CI 0.23-0.24). The SNP (rs10490770) located near LZTFL1 suggested direct causality (SNPs → COVID-19 → CHD), and SNPs in ABO (rs579459, rs495828), ILRUN(rs2744961), and CACFD1(rs4962153, rs3094379) may simultaneously influence COVID-19 severity and CHD risks.

INTERPRETATION

Five SNPs located near LZTFL1 (rs10490770), ABO (rs579459, rs495828), ILRUN (rs2744961), and CACFD1 (rs4962153, rs3094379) may simultaneously influence their risks. The current study suggested that there may be shared mechanisms predisposing to both COVID-19 severity and CHD risks. Genetic predisposition to COVID-19 is a causal risk factor for CHD, supporting that reducing the COVID-19 infection risk or alleviating COVID-19 severity among those with specific genotypes might reduce their subsequent CHD adverse outcomes. Meanwhile, the shared genetic variants identified may be of clinical implications for identifying the target population who are more vulnerable to adverse CHD outcomes post COVID-19 and may also advance treatments of 'Long COVID-19.'

A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

Integrating population-level and cell-based signatures for drug repositioning

Drug repositioning presents a streamlined and cost-efficient way to expand the range of therapeutic possibilities. Furthermore, drugs with genetic evidence are more likely to progress successfully through clinical trials towards FDA approval. Exploiting these developments, single gene-based drug repositioning methods have been implemented, but approaches leveraging the entire spectrum of molecular signatures are critically underexplored. Most multi-gene-based approaches rely on differential gene expression (DGE) analysis, which is prone to identify the molecular consequence of disease and renders causal inference challenging. We propose a framework TReD (Transcriptome-informed Reversal Distance) that integrates population-level disease signatures robust to reverse causality and cell-based drug-induced transcriptome response profiles. TReD embeds the disease signature and drug profile in a high-dimensional normed space, quantifying the reversal potential of candidate drugs in a disease-related cell screen assay. The robustness is ensured by evaluation in additional cell screens. For an application, we implement the framework to identify potential drugs against COVID-19. Taking transcriptome-wide association study (TWAS) results from four relevant tissues and three DGE results as disease features, we identify 37 drugs showing potential reversal roles in at least four of the seven disease signatures. Notably, over 70% (27/37) of the drugs have been linked to COVID-19 from other studies, and among them, eight drugs are supported by ongoing/completed clinical trials. For example, TReD identifies the well-studied JAK1/JAK2 inhibitor baricitinib, the first FDA-approved immunomodulatory treatment for COVID-19. Novel potential candidates, including enzastaurin, a selective inhibitor of PKC-beta which can be activated by SARS-CoV-2, are also identified. In summary, we propose a comprehensive genetics-anchored framework integrating population-level signatures and cell-based screens that can accelerate the search for new therapeutic strategies.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

Genome-wide cross-trait analysis and Mendelian randomization reveal a shared genetic etiology and causality between COVID-19 and venous thromboembolism

Venous thromboembolism occurs in up to one-third of patients with COVID-19. Venous thromboembolism and COVID-19 may share a common genetic architecture, which has not been clarified. To fill this gap, we leverage summary-level genetic data from the latest COVID-19 host genetics consortium and UK Biobank and examine the shared genetic etiology and causal relationship between COVID-19 and venous thromboembolism. The cross-trait and co-localization analyses identify 2, 3, and 4 shared loci between venous thromboembolism and severe COVID-19, COVID-19 hospitalization, SARS-CoV-2 infection respectively, which are mapped to ABO, ADAMTS13, FUT2 genes involved in coagulation functions. Enrichment analysis supports shared biological processes between COVID-19 and venous thromboembolism related to coagulation and immunity. Bi-directional Mendelian randomization suggests that venous thromboembolism was associated with higher risk of three COVID-19 traits, and SARS-CoV-2 infection was associated with a higher risk of venous thromboembolism. Our study provides timely evidence for the genetic etiology between COVID-19 and venous thromboembolism (VTE). Our findings contribute to the understanding of COVID-19 and VTE etiology and provide insights into the prevention and comorbidity management of COVID-19.

A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.

Proteome-Wide Mendelian Randomization Identifies Causal Links Between Blood Proteins and Acute Pancreatitis

BACKGROUND & AIMS

Acute pancreatitis (AP) is a complex disease and the leading cause of gastrointestinal disease-related hospital admissions. Few therapeutic options exist for AP prevention. Blood proteins with causal evidence may represent promising drug targets, but few have been causally linked with AP. Our objective was to identify blood proteins linked with AP by combining genome-wide association meta-analysis and proteome-wide Mendelian randomization (MR) studies.

METHODS

We performed a genome-wide association meta-analysis totalling 10,630 patients with AP and 844,679 controls and a series of inverse-variance weighted MR analyses using cis-acting variants on 4719 blood proteins from the deCODE study (n = 35,559) and 4979 blood proteins from the Fenland study (n = 10,708).

RESULTS

The meta-analysis identified genome-wide significant variants (P <5 × 10^-8) at 5 loci (ABCG5/8, TWIST2, SPINK1, PRSS2 and MORC4). The proteome-wide MR analyses identified 68 unique blood proteins that may causally be associated with AP, including 29 proteins validated in both data sets. Functional annotation of these proteins confirmed expression of many proteins in metabolic tissues responsible for digestion and energy metabolism, such as the esophagus, adipose tissue, and liver as well as acinar cells of the pancreas. Genetic colocalization and investigations into the druggable genome also identified potential drug targets for AP.

CONCLUSIONS

This large genome-wide association study meta-analysis for AP identified new variants linked with AP as well as several blood proteins that may be causally associated with AP. This study provides new information on the genetic architecture of this disease and identified pathways related to AP, which may be further explored as possible therapeutic targets for AP.

A putative xanthine dehydrogenase is critical for Borrelia burgdorferi survival in ticks and mice

Borrelia burgdorferi is a pathogenic bacterium and the causative agent of Lyme disease. It is exposed to reactive oxygen species (ROS) in both the vertebrate and tick hosts. While some mechanisms by which B. burgdorferi ameliorates the effects of ROS exposure have been studied, there are likely other unknown mechanisms of ROS neutralization that contribute to virulence. Here, we follow up on a three gene cluster of unknown function, bb_0554, bb_0555, and bb_0556, that our prior unbiased transposon insertional sequencing studies implicated in both ROS survival and survival in Ixodes scapularis. We confirmed these findings through genetic knockout and provide evidence that these genes are co-transcribed as an operon to produce a xanthine dehydrogenase. In agreement with these results, we found that B. burgdorferi exposure to either uric acid (a product of xanthine dehydrogenase) or allopurinol (an inhibitor of xanthine dehydrogenase) could modulate sensitivity to ROS in a bb_0554-bb_0556 dependent manner. Together, this study identifies a previously uncharacterized three gene operon in B. burgdorferi as encoding a putative xanthine dehydrogenase critical for virulence. We propose renaming this locus xdhACB.

Genetic Associations with Coronavirus Susceptibility and Disease Severity

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19) global public health emergency, and the disease it causes is highly variable in its clinical presentation. Host genetic factors are increasingly recognised as a determinant of infection susceptibility and disease severity. Several initiatives and groups have been established to analyse and review host genetic epidemiology associated with COVID-19 outcomes. Here, we review the genetic loci associated with COVID-19 susceptibility and severity focusing on the common variants identified in genome-wide association studies.

DNA Repair Mechanisms are Activated in Circulating Lymphocytes of Hospitalized Covid-19 Patients

Purpose

Reactive oxygen species (ROS) are an important part of the inflammatory response during infection but can also promote DNA damage. Due to the sustained inflammation in severe Covid-19, we hypothesized that hospitalized Covid-19 patients would be characterized by increased levels of oxidative DNA damage and dysregulation of the DNA repair machinery.

Patients and Methods

Levels of the oxidative DNA lesion 8-oxoG and levels of base excision repair (BER) proteins were measured in peripheral blood mononuclear cells (PBMC) from patients (8-oxoG, n = 22; BER, n = 17) and healthy controls (n = 10) (Cohort 1). Gene expression related to DNA repair was investigated in two independent cohorts of hospitalized Covid-19 patients (Cohort 1; 15 patents and 5 controls, Cohort 2; 15 patients and 6 controls), and by publicly available datasets.

Results

Patients and healthy controls showed comparable amounts of oxidative DNA damage as assessed by 8-oxoG while levels of several BER proteins were increased in Covid-19 patients, indicating enhanced DNA repair in acute Covid-19 disease. Furthermore, gene expression analysis demonstrated regulation of genes involved in BER and double strand break repair (DSBR) in PBMC of Covid-19 patients and expression level of several DSBR genes correlated with the degree of respiratory failure. Finally, by re-analyzing publicly available data, we found that the pathway Hallmark DNA repair was significantly more regulated in circulating immune cells during Covid-19 compared to influenza virus infection, bacterial pneumonia or acute respiratory infection due to seasonal coronavirus.

Conclusion

Although beneficial by protecting against DNA damage, long-term activation of the DNA repair machinery could also contribute to persistent inflammation, potentially through mechanisms such as the induction of cellular senescence. However, further studies that also include measurements of additional markers of DNA damage are required to determine the role and precise molecular mechanisms for DNA repair in SARS-CoV-2 infection.

The Awesome Power of Human Genetics of Infectious Disease

Since the identification of sickle cell trait as a heritable form of resistance to malaria, candidate gene studies, linkage analysis paired with sequencing, and genome-wide association (GWA) studies have revealed many examples of genetic resistance and susceptibility to infectious diseases. GWA studies enabled the identification of many common variants associated with small shifts in susceptibility to infectious diseases. This is exemplified by multiple loci associated with leprosy, malaria, HIV, tuberculosis, and coronavirus disease 2019 (COVID-19), which illuminate genetic architecture and implicate pathways underlying pathophysiology. Despite these successes, most of the heritability of infectious diseases remains to be explained. As the field advances, current limitations may be overcome by applying methodological innovations such as cellular GWA studies and phenome-wide association (PheWA) studies as well as by improving methodological rigor with more precise case definitions, deeper phenotyping, increased cohort diversity, and functional validation of candidate loci in the laboratory or human challenge studies.

DPP9 as a Potential Novel Mediator in Gastrointestinal Virus Infection

Dipeptidyl peptidase 9 (DPP9) is a member of the dipeptidyl peptidase IV family. Inhibition of DPP9 has recently been shown to activate the nucleotide-binding domain leucine-rich repeat 1 (NLRP1) inflammasome. NLRP1 is known to bind nucleic acids with high affinity and directly interact with double stranded RNA, which plays a key role in viral replication. DPP9 has also recently emerged as a key gene related to lung-inflammation in critical SARS-CoV-2 infection. Importantly, DPP9 activity is strongly dependent on the oxidative status. Here, we explored the potential role of DPP9 in the gastrointestinal tract. We performed transcriptomics analyses of colon (microarray, n = 37) and jejunal (RNA sequencing, n = 31) biopsies from two independent cohorts as well as plasma metabolomics analyses in two independent cohorts (n = 37 and n = 795). The expression of DPP9 in the jejunum, colon, and blood was significantly associated with circulating biomarkers of oxidative stress (uric acid, bilirubin). It was also associated positively with the expression of transcription factors (NRF-2) and genes (SOD, CAT, GPX) encoding for antioxidant enzymes, but negatively with that of genes (XDH, NOX) and transcription factors (NF-KB) involved in ROS-generating enzymes. Gene co-expression patterns associated with DPP9 identified several genes participating in antiviral pathways in both tissues. Notably, DPP9 expression in the colon and plasma was strongly positively associated with several circulating nucleotide catabolites (hypoxanthine, uric acid, 3-ureidopropionic acid) with important roles in the generation of ROS and viral infection, as well as other metabolites related to oxidative stress (Resolvin D1, glutamate-containing dipeptides). Gene-drug enrichment analyses identified artenimol, puromycin, anisomycin, 3-phenyllactic acid, and linezolid as the most promising drugs targeting these DPP9-associated genes. We have identified a novel potential pathogenic mechanism of viral infection in the digestive tract and promising existing drugs that can be repositioned against viral infection.

Leveraging global multi-ancestry meta-analysis in the study of idiopathic pulmonary fibrosis genetics

The research of rare and devastating orphan diseases, such as idiopathic pulmonary fibrosis (IPF) has been limited by the rarity of the disease itself. The prognosis is poor-the prevalence of IPF is only approximately four times the incidence, limiting the recruitment of patients to trials and studies of the underlying biology. Global biobanking efforts can dramatically alter the future of IPF research. We describe a large-scale meta-analysis of IPF, with 8,492 patients and 1,355,819 population controls from 13 biobanks around the globe. Finally, we combine this meta-analysis with the largest available meta-analysis of IPF, reaching 11,160 patients and 1,364,410 population controls. We identify seven novel genome-wide significant loci, only one of which would have been identified if the analysis had been limited to European ancestry individuals. We observe notable pleiotropy across IPF susceptibility and severe COVID-19 infection and note an unexplained sex-heterogeneity effect at the strongest IPF locus MUC5B.

New insights into the role of dipeptidyl peptidase 8 and dipeptidyl peptidase 9 and their inhibitors

Dipeptidyl peptidase 8 (DPP8) and 9 (DPP9) are widely expressed in mammals including humans, mainly locate in the cytoplasm. The DPP8 and DPP9 (DPP8/9) belong to serine proteolytic enzymes, they can recognize and cleave N-terminal dipeptides of specific substrates if proline is at the penultimate position. Because the localization of DPP8/9 is different from that of DPP4 and the substrates for DPP8/9 are not yet completely clear, their physiological and pathological roles are still being further explored. In this article, we will review the recent research advances focusing on the expression, regulation, and functions of DPP8/9 in physiology and pathology status. Emerging research results have shown that DPP8/9 is involved in various biological processes such as cell behavior, energy metabolism, and immune regulation, which plays an essential role in maintaining normal development and physiological functions of the body. DPP8/9 is also involved in pathological processes such as tumorigenesis, inflammation, and organ fibrosis. In recent years, related research on immune cell pyroptosis has made DPP8/9 a new potential target for the treatment of hematological diseases. In addition, DPP8/9 inhibitors also have great potential in the treatment of tumors and chronic kidney disease.

Genetic association and Mendelian randomization for hypothyroidism highlight immune molecular mechanisms

We carried out a genome-wide association analysis including 51,194 cases of hypothyroidism and 443,383 controls. In total, 139 risk loci were associated to hypothyroidism with genes involved in lymphocyte function. Candidate genes associated with hypothyroidism were identified by using molecular quantitative trait loci, colocalization, and enhancer-promoter chromatin looping. Mendelian randomization (MR) identified 42 blood expressed genes and circulating proteins as candidate causal molecules in hypothyroidism. Drug-gene interaction analysis provided evidence that immune checkpoint and tyrosine kinase inhibitors used in cancer therapy increase the risk of hypothyroidism. Hence, integrative mapping and MR support that expression of genes and proteins enriched in lymphocyte function are associated with the risk of hypothyroidism and provide genetic evidence for drug-induced hypothyroidism and identify actionable potential drug targets.

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GWAS for hypothyroidism identified 139 risk loci including 76 novel associations

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GWAS was enriched in pathways related to lymphocyte function

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In total, 28 potentially deleterious missense variants were identified

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Mendelian randomization and colocalization identified 61 blood causal candidate genes

Identification of risk factors for 1-year mortality among critically ill older adults with hip fractures surgery: A single medical center retrospective study

Aim

The purpose of this study was to analyze the potential risk factors for mortality 1 year after hip fracture surgery in critically ill older adults.

Methods

We reviewed 591 critically ill older adults who underwent hip surgery at our institution from January 2018 to April 2021. We collected baseline demographics, clinical information, and 1-year survival status of the sample patients by means of medical record systems and follow-up phone calls. Patients were divided into survival and mortality groups based on survival within 1 year after surgery.

Results

Based on the results of the 1-year postoperative follow-up of patients, we obtained 117 cases in the death group and 474 cases in the survival group, and this led to a 1-year mortality rate of 19.8% (117/591) after hip fracture in critically ill older adults at our hospital. The risk factors that influenced the 1-year postoperative mortality were identified as advanced age (HR:1.04, 95%, 1.01–1.06), preoperative arrhythmia (HR: 1.95, 95%, 1.26–2.70), high level of NLR (HR:1.03, 95%, 1.01–1.06), respiratory failure (HR: 2.63, 95%, 1.32–5.23), and acute cardiovascular failure. 5.23) and acute cardiovascular events (HR: 1.65, 95%, 1.05–2.59).

Conclusion

Advanced age, preoperative arrhythmias, high levels of NLR, postoperative respiratory failure, and acute cardiovascular events were independent risk factors for survival of critically ill older adults with hip fracture at 1 year after surgery. Therefore, laboratory tests such as high levels of preoperative NLR can be an important indicator of patient prognosis.

Mendelian randomization of circulating proteome identifies actionable targets in heart failure

Background

Heart failure (HF) is a prevalent cause of mortality and morbidity. The molecular drivers of HF are still largely unknown.

Results

We aimed to identify circulating proteins causally associated with HF by leveraging genome-wide genetic association data for HF including 47,309 cases and 930,014 controls. We performed two-sample Mendelian randomization (MR) with multiple cis instruments as well as network and enrichment analysis using data from blood protein quantitative trait loci (pQTL) (2,965 blood proteins) measured in 3,301 individuals. Nineteen blood proteins were causally associated with HF, were not subject to reverse causality and were enriched in ligand-receptor and glycosylation molecules. Network pathway analysis of the blood proteins showed enrichment in NF-kappa B, TGF beta, lipid in atherosclerosis and fluid shear stress. Cross-phenotype analysis of HF identified genetic overlap with cardiovascular drugs, myocardial infarction, parental longevity and low-density cholesterol. Multi-trait MR identified causal associations between HF-associated blood proteins and cardiovascular outcomes. Multivariable MR showed that association of BAG3, MIF and APOA5 with HF were mediated by the blood pressure and coronary artery disease. According to the directional effect and biological action, 7 blood proteins are targets of existing drugs or are tractable for the development of novel therapeutics. Among the pathways, sialyl Lewis x and the activin type II receptor are potential druggable candidates.

Conclusions

Integrative MR analyses of the blood proteins identified causally-associated proteins with HF and revealed pleiotropy of the blood proteome with cardiovascular risk factors. Some of the proteins or pathway related mechanisms could be targeted as novel treatment approach in HF.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08811-2.

Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic lung condition with poor survival times. We previously published a genome-wide meta-analysis of IPF risk across three studies with independent replication of associated variants in two additional studies. To maximise power and to generate more accurate effect size estimates, we performed a genome-wide meta-analysis across all five studies included in the previous IPF risk genome-wide association studies. We used the distribution of effect sizes across the five studies to assess the replicability of the results and identified five robust novel genetic association signals implicating mTOR (mammalian target of rapamycin) signalling, telomere maintenance and spindle assembly genes in IPF risk.

HYGIEIA: HYpothesizing the Genesis of Infectious Diseases and Epidemics through an Integrated Systems Biology Approach

More than two years on, the COVID-19 pandemic continues to wreak havoc around the world and has battle-tested the pandemic-situation responses of all major global governments. Two key areas of investigation that are still unclear are: the molecular mechanisms that lead to heterogenic patient outcomes, and the causes of Post COVID condition (AKA Long-COVID). In this paper, we introduce the HYGIEIA project, designed to respond to the enormous challenges of the COVID-19 pandemic through a multi-omic approach supported by network medicine. It is hoped that in addition to investigating COVID-19, the logistics deployed within this project will be applicable to other infectious agents, pandemic-type situations, and also other complex, non-infectious diseases. Here, we first look at previous research into COVID-19 in the context of the proteome, metabolome, transcriptome, microbiome, host genome, and viral genome. We then discuss a proposed methodology for a large-scale multi-omic longitudinal study to investigate the aforementioned biological strata through high-throughput sequencing (HTS) and mass-spectrometry (MS) technologies. Lastly, we discuss how a network medicine approach can be used to analyze the data and make meaningful discoveries, with the final aim being the translation of these discoveries into the clinics to improve patient care.

Comprehensive Profiling Analysis of CD209 in Malignancies Reveals the Therapeutic Implication for Tumor Patients Infected With SARS-CoV-2

Coronavirus disease 2019 (COVID-19), which is known to be caused by the virus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is characterized by pneumonia, cytokine storms, and lymphopenia. Patients with malignant tumors may be particularly vulnerable to SARS-CoV-2 infection and possibly more susceptible to severe complications due to immunosuppression. Recent studies have found that CD209 (DC-SIGN) might be a potential binding receptor for SARS-CoV-2 in addition to the well-known receptor ACE2. However, pan-cancer studies of CD209 remain unclear. In this study, we first comprehensively investigated the expression profiles of CD209 in malignancies in both pan-carcinomas and healthy tissues based on bioinformatic techniques. The CD209 expression declined dramatically in various cancer types infected by SARS-CoV-2. Remarkably, CD209 was linked with diverse immune checkpoint genes and infiltrating immune cells. These findings indicate that the elevation of CD209 among specific cancer patients may delineate a mechanism accounting for a higher vulnerability to infection by SARS-CoV-2, as well as giving rise to cytokine storms. Taken together, CD209 plays critical roles in both immunology and metabolism in various cancer types. Pharmacological inhibition of CD209 antigen (D-mannose), together with other anti-SARS-CoV-2 strategies, might provide beneficial therapeutic effects in specific cancer patients.

mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights

Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 genome-wide association studies with metabolomics (mGWAS) to date. Obtaining mechanistic or functional insights from these associations for translational applications has become a key research area in the mGWAS community. Here, we introduce mGWAS-Explorer, a user-friendly web-based platform to help connect SNPs, metabolites, genes, and their known disease associations via powerful network visual analytics. The application of the mGWAS-Explorer was demonstrated using a COVID-19 and a type 2 diabetes case studies.

Shared genetic etiology and causality between COVID-19 and venous thromboembolism: evidence from genome-wide cross trait analysis and bi-directional Mendelian randomization study

Venous thromboembolism (VTE) occurs in up to one third patients with COVID-19. VTE and COVID-19 may share a common genetic architecture, which has not been clarified yet. To fill this gap, we leveraged summary-level genetic data from the latest COVID-19 host genetics consortium and UK Biobank and examined the shared genetic etiology and causal relationship between COVID-19 and VTE. The cross-trait analysis identified 8, 11, and 7 shared loci between VTE and severe COVID-19, COVID-19 hospitalization, SARS-CoV-2 infection respectively, in 13 genes involved in coagulation and immune function and enriched in the lung. Co-localization analysis identified eight shared loci in ABO, ADAMTS13 and FUT2 genes. Bi-direction Mendelian randomization suggested that VTE was associated with higher risks of all COVID-19 related traits, and SARS-CoV-2 infection was associated with higher risk of VTE. Our study provided timely evidence and novel insights into the genetic etiology between COVID-19 and VTE.

Medicinal chemistry of the myeloid C-type lectin receptors Mincle, Langerin, and DC-SIGN

In their role as pattern-recognition receptors on cells of the innate immune system, myeloid C-type lectin receptors (CLRs) assume important biological functions related to immunity, homeostasis, and cancer. As such, this family of receptors represents an appealing target for therapeutic interventions for modulating the outcome of many pathological processes, in particular related to infectious diseases. This review summarizes the current state of research into glycomimetic or drug-like small molecule ligands for the CLRs Mincle, Langerin, and DC-SIGN, which have potential therapeutic applications in vaccine research and anti-infective therapy.

Activation of Type I and Type II Interferon Signaling in SARS-CoV-2-Positive Thyroid Tissue of Patients Dying from COVID-19

Background: Thyroid dysfunctions have been reported after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. However, the biological mechanisms behind these conditions remain unexplored. Herein, we report on changes of the immune transcriptome in autoptic thyroid tissues of people who have died from coronavirus disease 2019 (COVID-19). Methods: Twenty-five autoptic thyroid specimens of subjects dying from COVID-19 were investigated. Eleven autoptic thyroid specimens of subjects dying from causes other than infectious conditions served as controls. RNA transcripts of 770 immune-related genes together with RNA genomes of multiple coronavirus types were measured by the nCounter system. Reverse transcription-polymerase chain reaction for two SARS-CoV-2 genes was used to assess virus positivity. Results were validated by immunohistochemistry. Results: The SARS-CoV-2 genome and antigens were detected in 9 of 25 (36%) thyroid specimens from the COVID-19 cohort. Virus-negative thyroid tissues from COVID-19 subject did not show changes of gene transcription nor significant numbers of infiltrating immune cells. Conversely, SARS-CoV-2-positive thyroid specimens showed marked upregulation of immune genes, especially those proper of the type I and type II interferon (IFN) pathways. In infected tissues, infiltrates of innate immune cells (macrophages and polymorphonuclear neutrophils) were prevalent. Conclusions: The thyroid gland can be directly infected by the SARS-CoV-2. Infection strongly activates IFN pathways. The direct viral insult combined with an intense immune response may trigger or worsen thyroid conditions in predisposed individuals.

Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes

Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.

Neurotropism of SARS-CoV-2 and neurological diseases of the central nervous system in COVID-19 patients

The devastating COVID-19 pandemic is caused by the SARS-CoV-2 virus. It primarily affects the lung and induces acute respiratory distress leading to a decrease in oxygen supply to the cells. This lung insufficiency caused by SARS-CoV-2 virus contributes to hypoxia which can affect the brain and other organ systems. The heightened cytokine storm in COVID-19 patients leads to an immune reaction in the vascular endothelial cells that compromise the host defenses against the SARS-CoV-2 virus in various organs. The vascular endothelial cell membrane breach allows access for SARS-CoV-2 to infect multiple tissues and organs. The neurotropism of spike protein in SARS-CoV-2 rendered by furin site insertion may increase neuronal infections. These could result in encephalitis and encephalopathy. The COVID-19 patients suffered severe lung deficiency often showed effects in the brain and neural system. The early symptoms include headache, loss of smell, mental confusion, psychiatric disorders and strokes, and rarely encephalitis, which indicated the vulnerability of the nervous system to SARS-CoV-2. Infection of the brain and peripheral nervous system can lead to the dysfunction of other organs and result in multi-organ failure. This review focuses on discussing the vulnerability of the nervous system based on the pattern of expression of the receptors for the SARS-CoV-2 and the mechanisms of its cell invasion. The SARS-CoV-2 elicited immune response and host immune response evasion are further discussed. Then the effects on the nervous system and its consequences on neuro-sensory functions are discussed. Finally, the emerging information on the overall genetic susceptibility seen in COVID-19 patients and its implications for therapy outlook is discussed.

Host genetic factors determining COVID-19 susceptibility and severity

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) poses an unprecedented challenge to humanity. SARS-CoV-2 infections range from asymptomatic to severe courses of COVID-19 with acute respiratory distress syndrome (ARDS), multiorgan involvement and death. Risk factors for disease severity include older age, male sex, increased BMI and pre-existing comorbidities. Ethnicity is also relevant to COVID-19 susceptibility and severity. Host genetic predisposition to COVID-19 is now increasingly recognized and whole genome and candidate gene association studies regarding COVID-19 susceptibility have been performed. Several common and rare variants in genes related to inflammation or immune responses have been identified. We summarize research on COVID-19 host genetics and compile genetic variants associated with susceptibility to COVID-19 and disease severity. We discuss candidate genes that should be investigated further to understand such associations and provide insights relevant to pathogenesis, risk classification, therapy response, precision medicine, and drug repurposing.

Distinctive features of severe SARS-CoV-2 pneumonia

The coronavirus disease 2019 (COVID-19) pandemic is among the most important public health crises of our generation. Despite the promise of prevention offered by effective vaccines, patients with severe COVID-19 will continue to populate hospitals and intensive care units for the foreseeable future. The most common clinical presentation of severe COVID-19 is hypoxemia and respiratory failure, typical of the acute respiratory distress syndrome (ARDS). Whether the clinical features and pathobiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia differ from those of pneumonia secondary to other pathogens is unclear. This uncertainty has created variability in the application of historically proven therapies for ARDS to patients with COVID-19. We review the available literature and find many similarities between patients with ARDS from pneumonia attributable to SARS-CoV-2 versus other respiratory pathogens. A notable exception is the long duration of illness among patients with COVID-19, which could result from its unique pathobiology. Available data support the use of care pathways and therapies proven effective for patients with ARDS, while pointing to unique features that might be therapeutically targeted for patients with severe SARS-CoV-2 pneumonia.