Göran M.L. Bergström

This paper evaluates the clinical use of long-read genome sequencing in Sweden, showing it provides molecular diagnoses and reduces diagnostic costs.

The paper decomposes polygenic risk of coronary artery disease into distinct biological processes using latent factor decomposition of genetic variants.

The paper uses machine learning on circulating proteomic data to identify biomarkers and reveal molecular subgroups in obesity and type 2 diabetes.

The paper integrates multi-omics data from 101 individuals over two years to identify stable immune patterns and genetic associations with immune cell types and functions.

The paper analyzes how genetics and environmental factors influence metabolic profiles over two years in 101 healthy individuals, identifying 22 genetically determined metabolites and 87 causal metabolite-protein associations.

This study applies longitudinal exposomics to a multiomic cohort, revealing dynamic environmental chemical mixtures and their associations with biological markers over time.