Amand Floriaan Schmidt

The paper integrates multi-modal omics data to identify therapeutic pathways in atherosclerosis for coronary heart disease, highlighting amino acid metabolism and 16 relevant proteins.

This paper identifies and prioritizes drug targets for major depressive disorder using genetic methods, finding that 82% of approved depression/anxiety drugs have significant genetic targets.

The paper introduces MR-Fish, a method using proteomics and Mendelian randomisation to identify causal relationships between proteins and diseases, finding causal links for several proteins associated with conditions like T2DM and CHD.

This paper introduces M-REGLE, a multimodal deep learning method that improves genetic analysis of cardiovascular traits by integrating multiple clinical data modalities and outperforms unimodal approaches in locus discovery and risk prediction.

This study uses Mendelian randomization to investigate the causal role of lipids and statin interventions on multiple sclerosis risk and severity, finding HDL-C increases risk while RAC2 reduces it.

This study evaluates polygenic risk scores for population screening, individual risk prediction, and risk stratification, finding poor performance across all three applications.

The paper uses Mendelian randomization and clinical/biological evidence to prioritize lipid-modifying drug targets for coronary heart disease prevention.

This study evaluates how polygenic risk scores for coronary artery disease (CAD) relate to future events in individuals with and without CAD, finding attenuated or inverse associations in established cases.

The paper analyzes genetic variants at the PCSK9 locus to assess their phenotypic effects on lipid levels and disease outcomes, finding concordance with PCSK9 inhibitor trials for myocardial infarction but discordance for type 2 diabetes.

This paper introduces a mathematical framework to improve Mendelian randomization analysis for drug targets by addressing horizontal pleiotropy when proteins are the risk factors.