Linköping University

Polygenic risk for coronary artery disease no longer collapses into a single number. Conventional scores identify who is at high risk but not which biological processes drive that risk. Decomposing 526 genome-wide significant variants by their associations with 78 CAD-related traits yielded eight latent components: cholesterol-enriched lipoproteins, triglyceride-rich lipoproteins, lipoprotein(a), metabolically unhealthy normal weight, obesity, endothelial dysfunction, coagulation and cardiac remodeling. Process-specific scores built in UK Biobank, SCAPIS and Mass General Brigham Biobank tracked clinical coronary events, subclinical atherosclerosis and distinct biomarker and comorbidity profiles.

Polygenic risk for coronary artery disease no longer collapses into a single number. Conventional scores identify who is at high risk but not which biological processes drive that risk. Decomposing 526 genome-wide significant variants by their associations with 78 CAD-related traits yielded eight latent components: cholesterol-enriched lipoproteins, triglyceride-rich lipoproteins, lipoprotein(a), metabolically unhealthy normal weight, obesity, endothelial dysfunction, coagulation and cardiac remodeling. Process-specific scores built in UK Biobank, SCAPIS and Mass General Brigham Biobank tracked clinical coronary events, subclinical atherosclerosis and distinct biomarker and comorbidity profiles.

The authors introduce SDECast, a Neural Stochastic Differential Equation (SDE) framework for continuous-time probabilistic weather forecasting that extends SDE Matching to learn stochastic dynamics directly in physical space without repeated SDE simulation during training; on a simulated geophysical flow, SDECast recovers meaningful drift dynamics and faithfully reproduces the underlying continuous-time behavior, and it then scales to global weather forecasting at hourly resolution, producing skillful probabilistic forecasts for lead times up to five days.

The authors introduce SDECast, a Neural Stochastic Differential Equation (SDE) framework for continuous-time probabilistic weather forecasting that extends SDE Matching to learn stochastic dynamics directly in physical space without repeated SDE simulation during training; on a simulated geophysical flow, SDECast recovers meaningful drift dynamics and faithfully reproduces the underlying continuous-time behavior, and it then scales to global weather forecasting at hourly resolution, producing skillful probabilistic forecasts for lead times up to five days.