Therese Haugdahl Nøst

This study identifies 135 genomic loci associated with venous thromboembolism risk through cross-ancestry GWAS and provides insights into biological pathways and potential therapeutic targets.

This paper uses proteome-wide Mendelian randomization across multiple ancestries to identify causal protein-disease associations and drug targets in over 1.26 million individuals.

The paper introduces a pipeline to normalize and pool metabolomics data across studies, improving comparability and reducing inter-study variability.

The paper shows that integrating multiple omics data sources improves predictive models for cancer outcomes by preserving both joint and individual variation patterns.

The paper provides an R-based framework for reproducible data management and analysis in epidemiological studies using -omics data, with applications in the NOWAC study.