Jorgen Engmann

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 identifies 12,111 SNPs associated with height from 5.4 million individuals, explaining most common SNP-based heritability and showing ancestry-specific prediction accuracy.

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

This study identifies 242 genetic loci for glycaemic traits using trans-ancestry meta-analysis, finding 99 novel loci and improved fine-mapping resolution compared to single-ancestry analyses.

This study identifies 47 genetic associations with sleep traits using accelerometer data from 85,670 individuals, including novel variants for sleep quality and duration.

The paper connects GWAS loci to druggable genes, compounds, and clinical indications to identify drug development opportunities and design a genotyping array for druggable genome-wide association studies.