Alex Bloemendal

The Evidence Aggregator (EvAgg) uses generative AI to streamline rare disease diagnostics by extracting and summarizing genetic variant and clinical information from literature, improving efficiency and accuracy.

The paper develops an automated tool for reanalyzing genomic data in rare disease cohorts, showing improved diagnostic yields through iterative reanalysis.

The paper identifies compound heterozygous variants in UK Biobank data and shows their phenotypic impact on common diseases using statistical phasing and mixed models.

This paper introduces GUIDE, a method to identify latent genetic factors from GWAS data, revealing biological pathways and environmental influences in complex traits.

The paper introduces LD graphical models (LDGMs) for efficient linkage disequilibrium representation in diverse ancestry studies, showing significant runtime improvements and better polygenic prediction.

The paper evaluates dominance effects in over 1,000 UK Biobank phenotypes using GWAS, finding no significant dominance contribution to phenotypic variance from common variation.

The paper maps coronary artery disease risk loci to endothelial cell gene programs, identifying convergence on the CCM pathway and a new CAD gene, TLNRD1.

This paper presents a data harmonization pipeline for GWAS that improves power by integrating external controls and reduces batch effects, demonstrating its effectiveness in a Crohn's disease study.

The paper estimates the risk of five disorders from paternal-age-related de novo mutations using whole-exome-sequencing data and Danish registry data.

PINES improves noncoding variant functional impact prediction by integrating phenotype-dependent epigenetic annotations.

This study finds that ultra-rare disruptive variants in highly constrained genes are associated with reduced educational attainment in the general population.

The paper correlates Gibbs free energy of protein-protein interactions with tumor stage and 5-year survival in cancer patients.