Astrid Johannesson Hjelholt

This study uses genetic correlations to prioritize drug groups for repurposing in type 2 diabetes, identifying several drug classes with significant genetic relevance.

This paper evaluates Bayesian Linear Regression derived gene set tests, showing they outperform MAGMA in detecting genetic associations with complex diseases.

This study evaluates Bayesian Linear Regression models for gene set prioritization in complex diseases, showing improved performance in identifying T2D-related pathways using multi-trait analysis.

This paper evaluates Bayesian Linear Regression models with BayesC and BayesR priors as fine mapping tools, comparing them to existing methods and showing improved F1 scores and credible set properties.