Causal relationships between COVID-19 and osteoporosis: a two-sample Mendelian randomization study in European population.

Introduction: The causal relationship between Coronavirus disease 2019 (COVID-19) and osteoporosis (OP) remains uncertain. We aimed to assess the effect of COVID-19 severity (severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and severe COVID-19) on OP by a two-sample Mendelian randomization (MR) study. Methods: We conducted a two-sample MR analysis using publicly available genome-wide association study (GWAS) data. Inverse variance weighting (IVW) was used as the main analysis method. Four complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple mode method, and the weighted mode method. We utilized the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test to identify the presence of horizontal pleiotropy. Cochran's Q statistics were employed to assess the existence of instrument heterogeneity. We conducted a sensitivity analysis using the leave-one-out method. Results: The primary results of IVW showed that COVID-19 severity was not statistically related to OP (SARS-CoV-2 infection: OR (95% CI) = 0.998 (0.995 ~ 1.001), p = 0.201403; COVID-19 hospitalization: OR (95% CI) =1.001 (0.999 ~ 1.003), p = 0.504735; severe COVID-19: OR (95% CI) = 1.000 (0.998 ~ 1.001), p = 0.965383). In addition, the MR-Egger regression, weighted median, simple mode and weighted mode methods showed consistent results. The results were robust under all sensitivity analyses. Conclusion: The results of the MR analysis provide preliminary evidence that a genetic causal link between the severity of COVID-19 and OP may be absent.

Causal associations between type 1 diabetes and COVID-19 infection and prognosis: a two-sample Mendelian randomization study.

INTRODUCTION: It has been suggested that type 1 diabetes was associated with increased COVID-19 morbidity and mortality. However, their causal relationship is still unclear. Herein, we performed a two-sample Mendelian randomization (MR) to investigate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. RESEARCH DESIGN AND METHODS: The summary statistics of type 1 diabetes were obtained from two published genome-wide association studies of European population, one as a discovery sample including 15 573 cases and 158 408 controls, and the other data as a replication sample consisting of 5913 cases and 8828 controls. We first performed a two-sample MR analysis to evaluate the causal effect of type 1 diabetes on COVID-19 infection and prognosis. Then, reverse MR analysis was conducted to determine whether reverse causality exists. RESULTS: MR analysis results showed that the genetically predicted type 1 diabetes was associated with higher risk of severe COVID-19 (OR=1.073, 95% CI: 1.034 to 1.114, pFDR=1.15×10-3) and COVID-19 death (OR=1.075, 95% CI: 1.033 to 1.119, pFDR=1.15×10-3). Analysis of replication dataset showed similar results, namely a positive association between type 1 diabetes and severe COVID-19 (OR=1.055, 95% CI: 1.029 to 1.081, pFDR=1.59×10-4), and a positively correlated association with COVID-19 death (OR=1.053, 95% CI: 1.026 to 1.081, pFDR=3.50×10-4). No causal association was observed between type 1 diabetes and COVID-19 positive, hospitalized COVID-19, the time to the end of COVID-19 symptoms in the colchicine treatment group and placebo treatment group. Reverse MR analysis showed no reverse causality. CONCLUSIONS: Type 1 diabetes had a causal effect on severe COVID-19 and death after COVID-19 infection. Further mechanistic studies are needed to explore the relationship between type 1 diabetes and COVID-19 infection and prognosis.

Causal associations between chronic hepatitis B and COVID-19 in East Asian populations.

BACKGROUND: The relationship between chronic hepatitis B (CHB) and Coronavirus disease 2019 (COVID-19) has been inconsistent in traditional observational studies. METHODS: We explored the total causal and direct causal associations between CHB and the three COVID-19 outcomes using univariate and multivariate Mendelian randomization (MR) analyses, respectively. Genome-wide association study datasets for CHB and COVID-19 were obtained from the Japan Biobank and the COVID-19 Host Genetics Initiative, respectively. RESULTS: Univariate MR analysis showed that CHB increased the risk of SARS-CoV-2 infection (OR = 1.04, 95% CI 1.01-1.07, P = 3.39E-03), hospitalized COVID-19 (OR = 1.10, 95% CI 1.06-1.13, P = 7.31E-08), and severe COVID-19 (OR = 1.16, 95%CI 1.08-1.26, P = 1.43E-04). A series of subsequent sensitivity analyses ensured the stability and reliability of these results. In multivariable MR analyses adjusting for type 2 diabetes, body mass index, basophil count, and smoking, genetically related CHB is still positively associated with increased risk of SARS-CoV-2 infection (OR = 1.06, 95% CI 1.02-1.11, P = 1.44E-03) and hospitalized COVID-19 (OR = 1.12, 95% CI 1.07-1.16, P = 5.13E-07). However, the causal link between CHB and severe COVID-19 was attenuated after adjustment for the above variables. In addition, the MR analysis did not support the causal effect of COVID-19 on CHB. CONCLUSIONS: This study provides evidence that CHB increases COVID-19 susceptibility and severity among individuals of East Asian ancestry.

Mapping interindividual dynamics of innate immune response at single-cell resolution.

Common genetic variants across individuals modulate the cellular response to pathogens and are implicated in diverse immune pathologies, yet how they dynamically alter the response upon infection is not well understood. Here, we triggered antiviral responses in human fibroblasts from 68 healthy donors, and profiled tens of thousands of cells using single-cell RNA-sequencing. We developed GASPACHO (GAuSsian Processes for Association mapping leveraging Cell HeterOgeneity), a statistical approach designed to identify nonlinear dynamic genetic effects across transcriptional trajectories of cells. This approach identified 1,275 expression quantitative trait loci (local false discovery rate 10%) that manifested during the responses, many of which were colocalized with susceptibility loci identified by genome-wide association studies of infectious and autoimmune diseases, including the OAS1 splicing quantitative trait locus in a COVID-19 susceptibility locus. In summary, our analytical approach provides a unique framework for delineation of the genetic variants that shape a wide spectrum of transcriptional responses at single-cell resolution.

The public health impact of poor sleep on severe COVID-19, influenza and upper respiratory infections.

BACKGROUND: Poor sleep is associated with an increased risk of infections and all-cause mortality but the causal direction between poor sleep and respiratory infections has remained unclear. We examined if poor sleep contributes as a causal risk factor to respiratory infections. METHODS: We used data on insomnia, influenza and upper respiratory infections (URIs) from primary care and hospital records in the UK Biobank (N ≈ 231,000) and FinnGen (N ≈ 392,000). We computed logistic regression to assess association between poor sleep and infections, disease free survival hazard ratios, and performed Mendelian randomization analyses to assess causality. FINDINGS: Utilizing 23 years of registry data and follow-up, we discovered that insomnia diagnosis associated with increased risk for infections (FinnGen influenza Cox's proportional hazard (CPH) HR = 4.34 [3.90, 4.83], P = 4.16 × 10-159, UK Biobank influenza CPH HR = 1.54 [1.37, 1.73], P = 2.49 × 10-13). Mendelian randomization indicated that insomnia causally predisposed to influenza (inverse-variance weighted (IVW) OR = 1.65, P = 5.86 × 10-7), URI (IVW OR = 1.94, P = 8.14 × 10-31), COVID-19 infection (IVW OR = 1.08, P = 0.037) and risk of hospitalization from COVID-19 (IVW OR = 1.47, P = 4.96 × 10-5). INTERPRETATION: Our findings indicate that chronic poor sleep is a causal risk factor for contracting respiratory infections, and in addition contributes to the severity of respiratory infections. These findings highlight the role of sleep in maintaining sufficient immune response against pathogens. FUNDING: Instrumentarium Science Foundation, Academy of Finland, Signe and Ane Gyllenberg Foundation, National Institutes of Health.

Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19.

As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.

Serum KL-6 levels predict clinical outcomes and are associated with MUC1 polymorphism in Japanese patients with COVID-19.

BACKGROUND: Krebs von den Lungen-6 (KL-6) is a known biomarker for diagnosis and monitoring of interstitial lung diseases. However, the role of serum KL-6 and the mucin 1 (MUC1) variant (rs4072037) in COVID-19 outcomes remains to be elucidated. We aimed to evaluate the relationships among serum KL-6 levels, critical outcomes and the MUC1 variant in Japanese patients with COVID-19. METHODS: This is a secondary analysis of a multicentre retrospective study using data from the Japan COVID-19 Task Force collected from February 2020 to November 2021, including 2226 patients with COVID-19 whose serum KL-6 levels were measured. An optimal serum KL-6 level cut-off to predict critical outcomes was determined and used for multivariable logistic regression analysis. Furthermore, the relationship among the allele dosage of the MUC1 variant, calculated from single nucleotide polymorphism typing data of genome-wide association studies using the imputation method, serum KL-6 levels and COVID-19 critical outcomes was evaluated. RESULTS: Serum KL-6 levels were significantly higher in patients with COVID-19 with critical outcomes (511±442 U/mL) than those without (279±204 U/mL) (p<0.001). Serum KL-6 levels ≥304 U/mL independently predicted critical outcomes (adjusted OR (aOR) 3.47, 95% CI 2.44 to 4.95). Moreover, multivariable logistic regression analysis with age and sex indicated that the MUC1 variant was independently associated with increased serum KL-6 levels (aOR 0.24, 95% CI 0.28 to 0.32) but not significantly associated with critical outcomes (aOR 1.11, 95% CI 0.80 to 1.54). CONCLUSION: Serum KL-6 levels predicted critical outcomes in Japanese patients with COVID-19 and were associated with the MUC1 variant. Therefore, serum KL-6 level is a potentially useful biomarker of critical COVID-19 outcomes.

Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19.

Despite the high contagion and mortality rates that have accompanied the coronavirus disease-19 (COVID-19) pandemic, the clinical presentation of the syndrome varies greatly from one individual to another. Potential host factors that accompany greater risk from COVID-19 have been sought and schizophrenia (SCZ) patients seem to present more severe COVID-19 than control counterparts, with certain gene expression similarities between psychiatric and COVID-19 patients reported. We used summary statistics from the last SCZ, bipolar disorder (BD), and depression (DEP) meta-analyses available on the Psychiatric Genomics Consortium webpage to calculate polygenic risk scores (PRSs) for a target sample of 11,977 COVID-19 cases and 5943 subjects with unknown COVID-19 status. Linkage disequilibrium score (LDSC) regression analysis was performed when positive associations were obtained from the PRS analysis. The SCZ PRS was a significant predictor in the case/control, symptomatic/asymptomatic, and hospitalization/no hospitalization analyses in the total and female samples; and of symptomatic/asymptomatic status in men. No significant associations were found for the BD or DEP PRS or in the LDSC regression analysis. SNP-based genetic risk for SCZ, but not for BD or DEP, may be associated with higher risk of SARS-CoV-2 infection and COVID-19 severity, especially among women; however, predictive accuracy barely exceeded chance level. We believe that the inclusion of sexual loci and rare variations in the analysis of genomic overlap between SCZ and COVID-19 will help to elucidate the genetic commonalities between these conditions.

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

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

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

The causal links between gut microbiota and COVID-19: A Mendelian randomization study.

Several studies have shown a possible correlation between gut microbiota and COVID-19. However, the cause-and-effect relationship between the two has not been investigated. We conducted a two-sample Mendelian randomization study (MR) study using publicly available GWAS data. Inverse variance weighted (IVW) analysis was the main MR analysis technique and was supplemented with other sensitivity analyses. Forty-two bacterial genera were associated with COVID-19 susceptibility, hospitalization, and severity in the IVW method. Among these gut microbiota, five gut microbiota (genus unknowngenus [id.1000005472], family unknownfamily [id.1000005471], genus Tyzzerella3, order MollicutesRF9.id.11579, and phylum Actinobacteria) were significantly associated with COVID-19 hospitalization and severity. Three gut microbiota (class Negativicutes, order Selenomonadales, and class Actinobacteria) were significantly associated with COVID-19 hospitalization and susceptibility, while two microbiota (class Negativicutes and order Selenomonadales) were significantly associated with COVID-19 hospitalization and severity, and susceptibility. Sensitivity analysis did not detect any heterogeneity and horizontal pleiotropy. Our findings demonstrated that several microorganisms were causally linked to COVID-19, and improved our understanding of the relationship between gut microbiota and COVID-19 pathology.

Pilot genome-wide association study of antibody response to inactivated SARS-CoV-2 vaccines.

Vaccines are a key weapon against the COVID-19 pandemic caused by SARS-CoV-2. However, there are inter-individual differences in immune response to SARS-CoV-2 vaccines and genetic contributions to these differences have barely been investigated. Here, we performed genome-wide association study (GWAS) of antibody levels in 168 inactivated SARS-CoV-2 vaccine recipients. A total of 177 SNPs, corresponding to 41 independent loci, were identified to be associated with IgG, total antibodies or neutral antibodies. Specifically, the rs4543780, the intronic variant of FAM89A gene, was associated with total antibodies level and was annotated as a potential regulatory variant affecting gene expression of FAM89A, a biomarker differentiating bacterial from viral infections in febrile children. These findings might advance our knowledge of the molecular mechanisms driving immunity to SARS-CoV-2 vaccine.

Host Genetic Factors, Comorbidities and the Risk of Severe COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was varied in disease symptoms. We aim to explore the effect of host genetic factors and comorbidities on severe COVID-19 risk. METHODS: A total of 20,320 COVID-19 patients in the UK Biobank cohort were included. Genome-wide association analysis (GWAS) was used to identify host genetic factors in the progression of COVID-19 and a polygenic risk score (PRS) consisted of 86 SNPs was constructed to summarize genetic susceptibility. Colocalization analysis and Logistic regression model were used to assess the association of host genetic factors and comorbidities with COVID-19 severity. All cases were randomly split into training and validation set (1:1). Four algorithms were used to develop predictive models and predict COVID-19 severity. Demographic characteristics, comorbidities and PRS were included in the model to predict the risk of severe COVID-19. The area under the receiver operating characteristic curve (AUROC) was applied to assess the models' performance. RESULTS: We detected an association with rs73064425 at locus 3p21.31 reached the genome-wide level in GWAS (odds ratio: 1.55, 95% confidence interval: 1.36-1.78). Colocalization analysis found that two genes (SLC6A20 and LZTFL1) may affect the progression of COVID-19. In the predictive model, logistic regression models were selected due to simplicity and high performance. Predictive model consisting of demographic characteristics, comorbidities and genetic factors could precisely predict the patient's progression (AUROC = 82.1%, 95% CI 80.6-83.7%). Nearly 20% of severe COVID-19 events could be attributed to genetic risk. CONCLUSION: In this study, we identified two 3p21.31 genes as genetic susceptibility loci in patients with severe COVID-19. The predictive model includes demographic characteristics, comorbidities and genetic factors is useful to identify individuals who are predisposed to develop subsequent critical conditions among COVID-19 patients.

Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging.

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits-healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health-in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging.

Genetic predisposition to subjective well-being, depression, and suicide in relation to COVID-19 susceptibility and severity.

BACKGROUND: Epidemiological studies have reported associations between subjective well-being (SWB), depression, and suicide with COVID-19 illness, but the causality has not been established. We performed a two-sample Mendelian randomization (MR) analysis to investigate the causal link between SWB, depression, suicide and COVID-19 susceptibility and severity. METHODS: Summary statistics for SWB (298,420 cases), depression (113,769 cases) and suicide (52,208 cases) were obtained from three large-scale GWAS. Data on the associations between the Single Nucleotide Polymorphisms (SNPs) and COVID-19 (159,840 cases), hospitalized COVID-19 (44,986 cases), and severe COVID-19 (18,152 cases) were collected from the COVID-19 host genetics initiative. The causal estimate was calculated by the Inverse Variance Weighted, MR Egger and Weighted Median methods. Sensitivity tests were used to evaluate the validity of the causal relationship. RESULTS: Our results showed that genetically predicted SWB (OR = 0.98, 95 % CI: 0.86-1.10, P = 0.69), depression (OR = 0.76, 95 % CI: 0.54-1.06, P = 0.11), and suicide (OR = 0.99, 95 % CI: 0.96-1.02, P = 0.56) were not causally related to COVID-19 susceptibility. Similarly, we did not find a potential causal relationship between SWB, depression, suicide and COVID-19 severity. CONCLUSIONS: This indicated that positive or negative emotions would not make COVID-19 better or worse, and strategies that attempted to use positive emotions to improve COVID-19 symptoms may be useless. Improving knowledge about the SARS-CoV-2 and timely medical intervention to reduce panic during a pandemic is one of the effective measures to deal with the current decrease in well-being and increase in depression and suicide rates.

Causal effects of COVID-19 on cancer risk: A Mendelian randomization study.

In contemporary literature, little attention has been paid to the association between coronavirus disease-2019 (COVID-19) and cancer risk. We performed the Mendelian randomization (MR) to investigate the causal associations between the three types of COVID-19 exposures (critically ill COVID-19, hospitalized COVID-19, and respiratory syndrome coronavirus 2 (SARS-CoV-2) infection) and 33 different types of cancers of the European population. The results of the inverse-variance-weighted model indicated that genetic liabilities to critically ill COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (odds ratio [OR] = 1.0924; p-value = 0.0116), esophageal cancer (OR = 1.0004; p-value = 0.0226), colorectal cancer (OR = 1.0010; p-value = 0.0242), stomach cancer (OR = 1.2394; p-value = 0.0331), and colon cancer (OR = 1.0006; p-value = 0.0453). The genetic liabilities to hospitalized COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (OR = 1.1096; p-value = 0.0458), esophageal cancer (OR = 1.0005; p-value = 0.0440) as well as stomach cancer (OR = 1.3043; p-value = 0.0476). The genetic liabilities to SARS-CoV-2 infection had suggestive causal associations with the increased risk for stomach cancer (OR = 2.8563; p-value = 0.0019) but with the decreasing risk for head and neck cancer (OR = 0.9986, p-value = 0.0426). The causal associations of the above combinations were robust through the test of heterogeneity and pleiotropy. Together, our study indicated that COVID-19 had causal effects on cancer risk.

Association of gut microbiota with COVID-19 susceptibility and severity: A two-sample Mendelian randomization study.

Evidence supports the observational associations of gut microbiota with the risk of COVID-19; however, it is unclear whether these associations reflect a causal relationship. This study investigated the association of gut microbiota with COVID-19 susceptibility and severity. Data were obtained from a large-scale gut microbiota data set (n = 18 340) and the COVID-19 Host Genetics Initiative (n = 2 942 817). Causal effects were estimated with inverse variance weighted (IVW), MR-Egger, and weighted median, and sensitivity analyses were implemented with Cochran's Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis, and funnel plots. For COVID-19 susceptibility, IVW estimates suggested that Gammaproteobacteria (odds ratio [OR] = 0.94, 95% confidence interval [CI], 0.89-0.99, p = 0.0295] and Streptococcaceae (OR = 0.95, 95% CI, 0.92-1.00, p = 0.0287) had a reduced risk, while Negativicutes (OR = 1.05, 95% CI, 1.01-1.10, p = 0.0302), Selenomonadales (OR = 1.05, 95% CI, 1.01-1.10, p = 0.0302), Bacteroides (OR = 1.06, 95% CI, 1.01-1.12, p = 0.0283), and Bacteroidaceae (OR = 1.06, 95% CI, 1.01-1.12, p = 0.0283) were associated with an increased risk (all p < 0.05, nominally significant). For COVID-19 severity, Subdoligranulum (OR = 0.80, 95% CI, 0.69-0.92, p = 0.0018), Cyanobacteria (OR = 0.85, 95% CI, 0.76-0.96, p = 0.0062), Lactobacillales (OR = 0.87, 95% CI, 0.76-0.98, p = 0.0260), Christensenellaceae (OR = 0.87, 95% CI, 0.77-0.99, p = 0.0384), Tyzzerella3 (OR = 0.89, 95% CI, 0.81-0.97, p = 0.0070), and RuminococcaceaeUCG011 (OR = 0.91, 95% CI, 0.83-0.99, p = 0.0247) exhibited negative correlations, while RikenellaceaeRC9 (OR = 1.09, 95% CI, 1.01-1.17, p = 0.0277), LachnospiraceaeUCG008 (OR = 1.12, 95% CI, 1.00-1.26, p = 0.0432), and MollicutesRF9 (OR = 1.14, 95% CI, 1.01-1.29, p = 0.0354) exhibited positive correlations (all p < 0.05, nominally significant). Sensitivity analyses validated the robustness of the above associations. These findings suggest that gut microbiota might influence the susceptibility and severity of COVID-19 in a causal way, thus providing novel insights into the gut microbiota-mediated development mechanism of COVID-19.

Associations between brain gene expression perturbations implicated by COVID-19 and psychiatric disorders.

BACKGROUND: Currently, there is increasing evidence from clinic, epidemiology, as well as neuroimaging, demonstrating neuropsychiatric abnormalities in COVID-19, however, whether there were associations between brain changes caused by COVID-19 and genetic susceptibility of psychiatric disorders was still unknown. METHODS: In this study, we performed a meta-analysis to investigate these associations by combing single-cell RNA sequencing datasets of brain tissues of COVID-19 and genome-wide association study summary statistics of psychiatric disorders. RESULTS: The analysis demonstrated that among ten psychiatric disorders, gene expression perturbations implicated by COVID-19 in excitatory neurons of choroid plexus were significantly associated with schizophrenia. CONCLUSIONS: Our analysis might provide insights for the underlying mechanism of the psychiatric consequence of COVID-19.