Genetic Variation rs16937012 near the NCOA2 Gene Predicts Sustained Response to Interferon in Chronic Hepatitis B patients: the GIANT-B study

WOS: 00036701320000

Genome-wide Association Study Identifies Genetic Variants Associated With Early and Sustained Response to (Pegylated) Interferon in Chronic Hepatitis B Patients: The GIANT-B Study

Background. (Pegylated) Interferon ([Peg]IFN) therapy leads to response in a minority of chronic hepatitis B (CHB) patients. Host genetic determinants of response are therefore in demand

Is Liver Cirrhosis a Risk Factor for Osteonecrosis of the Femoral Head in Adults? A Population-Based 3-Year Follow-Up Study

[[abstract]]Background. The relationship between osteonecrosis of the femoral head (OFH) and liver cirrhosis is controversial. The aim of this study was to determine whether liver cirrhosis is associated with the occurrence of OFH. Methods. We used the National Health Insurance Database, derived from the Taiwan National Health Insurance program. The study cohort comprised 40,769 adult patients with liver cirrhosis. The comparison cohort consisted of 40,769 randomly selected age- and sex-matched subjects. Results. During the 3-year follow-up period, there were 321 (0.8%) cirrhotic patients with OFH, and 126 (0.3%) non-cirrhotic patients with OFH (p<0.001). Cox's regression analysis, adjusted by the patients' age, sex, and other confounding factors, showed that the cirrhotic patients had a higher risk for occurrence of OFH than non-cirrhotic patients during the 3-year period (hazard ratio=2.38, p<0.001). In this 3-year study, the incidence density of cirrhotic patients hospitalized for OFH was 3 episodes/1,000 person-year. Conclusion. We conclude that cirrhotic patients have a higher risk for occurrence of OFH than non-cirrhotic patients.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[countrycodes]]JP

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management