Evaluation of Bayesian Linear Regression derived gene set test methods.

Background: Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations.

Results: Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation.

INfluenza VaccInation To mitigate typE 1 Diabetes (INVITED): a study protocol for a randomised, double-blind, placebo-controlled clinical trial in children and adolescents with recent-onset type 1 diabetes.

Introduction: Children and adolescents with recent-onset type 1 diabetes (T1D) commonly maintain a certain level of insulin production during the remission phase, which can last months to years. Preserving β-cell function can reduce T1D complications and improve glycaemic control. Influenza vaccination has pleiotropic effects and administration of the vaccine during the early phases of T1D may offer β-cell protection.

Extracellular vesicles in glioblastoma: a challenge and an opportunity.

Glioblastoma is a highly heterogeneous tumor whose pathophysiological complexities dictate both the diagnosis of disease severity as well as response to therapy. Conventional diagnostic tools and standard treatment regimens have only managed to achieve limited success in the management of patients suspected of glioblastoma. Extracellular vesicles are an emerging liquid biopsy tool that has shown great promise in resolving the limitations presented by the heterogeneous nature of glioblastoma.