Caroline C. W. Klaver

This paper reanalyzes short-read WGS data and applies long-read WGS to improve genetic diagnosis of inherited retinal disease cases that were previously unresolved.

The paper uses AI-derived retinal thickness phenotypes from 2D images to identify novel genetic loci for neurodegenerative diseases.

This study performs genome-wide association meta-analyses on optic nerve structure biomarkers, identifying novel loci and potential drug targets independent of intraocular pressure.

The paper identifies de novo and inherited variants in U4 and U6 snRNAs causing retinitis pigmentosa and shows their impact on snRNP biogenesis.

The paper analyzes retinal vascular phenotypes from fundus images and their genetic and disease associations using UK Biobank data, finding novel links between vascular features and health outcomes.

This paper investigates the impact of the PDE11A-Y727C variant on neural physiology, showing reduced catalytic activity and altered subcellular compartmentalization without significant effects on eye growth or sleep quality.

The paper identifies 46 genetic loci associated with retinal thickness using OCT images from UK Biobank, finding new links to foveal hypoplasia and visual acuity.

GenNet predicts phenotypes from genetic variants using interpretable deep learning, identifying genes and pathways associated with traits like hair color and schizophrenia.

The paper identifies pathogenic deep-intronic and structural variants in 26% of unsolved Stargardt disease cases using integrated genomics and transcriptomics.

This paper introduces MR-BMA, a Bayesian model averaging approach for multivariable Mendelian randomization, to identify causal risk factors from high-throughput data.