To address the expression pattern of the SARS-CoV-2 receptor ACE2 and the viral priming protease TMPRSS2 in the respiratory tract, this study investigated RNA sequencing transcriptome profiling of samples of airway and oral mucosa. As shown, ACE2 has medium levels of expression in both small airway epithelium and masticatory mucosa, and high levels of expression in nasal epithelium. The expression of ACE2 is low in mucosal-associated invariant T (MAIT) cells and cannot be detected in alveolar macrophages. TMPRSS2 is highly expressed in small airway epithelium and nasal epithelium and has lower expression in masticatory mucosa. Our results provide the molecular basis that the nasal mucosa is the most susceptible locus in the respiratory tract for SARS-CoV-2 infection and consequently for subsequent droplet transmission and should be the focus for protection against SARS-CoV-2 infection.

Background: Both obesity and type 2 diabetes (T2D) are reported to be highly enriched in hospitalized COVID-19 patients. Due to the close correlation between obesity and T2D, it is important to examine whether obesity and T2D are independently related to COVID-19 hospitalization. Objective: To examine the causal effect of obesity and T2D in hospitalized COVID-19 patients using Mendelian randomization (MR).Research design and methods: This two-sample MR analysis applied genetic markers of obesity identified in the genome wide association study (GWAS) by the GIANT Consortium as instrumental variables (IVs) of obesity; and genetic markers of T2D identified by the DIAGRAM Consortium as IVs of T2D. The MR analysis was performed in hospitalized COVID-19 patient by the COVID-19 Host Genetics Initiative using the MR-Base platform. Results: All 3 classes of obesity (Class 1/2/3) were shown as the causal risk factors of COVID-19 hospitalization; however, T2D doesn't increase the risk of hospitalization or critically ill COVID-19 as an independent factor. Conclusions: Obesity, but not T2D, is a primary risk factor of COVID-19 hospitalization.

We previously observed enhanced immunoglobulin A (IgA) responses in severe COVID‐19, which might confer damaging effects. Given the important role of IgA in immune and inflammatory responses, the aim of this study was to investigate the dynamic response of the IgA isotype switch factor TGF‐β1 in COVID‐19 patients. We observed, in a total of 153 COVID‐19 patients, that the serum levels of TGF‐β1 were increased significantly at the early and middle stages of COVID‐19, and correlated with the levels of SARS‐CoV‐2‐specific IgA, as well as with the APACHE II score in patients with severe disease. In view of the genetic association of the TGF‐β1 activator THBS3 with severe COVID‐19 identified by the COVID‐19 Host Genetics Initiative, this study suggests TGF‐β1 may play a key role in COVID‐19.

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

Background Precise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta‐islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D‐GRS2) capable of predicting T1D risk in children of European ancestry. The T1D‐GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans‐ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities. Methods Here, we describe a T1D‐GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia. Results It demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population‐specific thresholds are needed for different populations. Conclusions The study highlights the potential to further improve T1D‐GRS2 performance with the inclusion of additional genetic markers.