Polygenic prediction of complex trait phenotypes has become important in human genetics, especially in the context of precision medicine. Recently, mr.mash, a flexible and computationally efficient method that models multiple phenotypes jointly and leverages sharing of effects across such phenotypes to improve prediction accuracy, was introduced.
View Article and Find Full Text PDFBackground: 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.
Background: Effectiveness of disease-modifying treatment (DMT) in people affected by primary progressive multiple sclerosis (PPMS) is limited. Whether specific subgroups may benefit more from DMT in a real-world setting remains unclear. Our aim was to investigate the potential effect of DMT on disability worsening among patients with PPMS stratified by different disability trajectories.
View Article and Find Full Text PDF