Gareth J. McKay

The paper identifies DNA methylation changes linked to cumulative hyperglycaemia in type 1 diabetes and links some to diabetic kidney disease through Mendelian randomization.

This study uses single-cell RNA sequencing to analyze human retinal samples and identifies microglial states and neural-immune interactions in diabetic retinopathy.

The paper identifies cell-type-specific aging signatures in human retinal cells using single-cell RNA sequencing and machine learning, revealing molecular changes and vulnerabilities associated with aging.

The paper constructs a single-cell transcriptomic atlas of the human retina from Chinese donors, identifying 10 cell types and subtypes, and mapping their molecular programs and biological functions.

The paper generates a scRNA-seq reference atlas of living and early post-mortem human retina, revealing distinct transcriptional dynamics and a novel mlCone cluster.

The paper identifies cell-type-specific biomarkers for diabetic retinopathy using single-cell transcriptomics and machine learning, achieving high classification accuracy across disease states.

The paper uses an autoencoder to derive retinal phenotypes from OCT images, identifies 118 genetic loci, and shows their relevance to retinal morphology and disease prediction.

The paper identifies differentially methylated loci in individuals with end-stage kidney disease due to diabetic kidney disease, linking them to key pathways and genes.

This study identifies 16 genome-wide significant loci associated with diabetic kidney disease, including a protective variant in the COL4A3 gene.