Richard A. Oram

The paper identifies an IFN-induced PD-L1 splice variant, PD-L1Δ3, which is retained intracellularly and fails to suppress T cell activation, altering β cell immune communication in type 1 diabetes.

The paper introduces a masked-MLP neural network to improve genetic risk score calculation from noisy and missing data, showing better robustness than standard methods.

The paper identifies biallelic variants in RNU6ATAC and RNU4ATAC as causes of early-onset autoimmune diabetes and immune dysregulation, showing the minor spliceosome's role in immune regulation.

This study performs a genome-wide association study of T1D stratified by HLA DR3 and DR4 status, revealing genetic and biological heterogeneity in T1D progression.

This paper demonstrates that deep learning can generate polygenic risk scores with high accuracy, even with missing data, and shows that a single model can predict multiple scores.

This study estimates that ~6% of insulin-treated diabetic individuals with ambiguous features in British Pakistanis and Bangladeshis have T1D, highlighting the need for autoantibody and C-peptide testing.

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.

This study identifies genetic risk factors for type 1 diabetes in individuals without high-risk HLA-DR3/DR4 haplotypes and develops a new risk score for better classification.

The paper evaluates the performance of a type 1 diabetes genetic risk score in an Indian cohort, finding lower discriminative ability compared to Europeans due to HLA diversity.

This paper develops a polygenic risk score model to predict colorectal cancer risk in symptomatic patients using UK Biobank data, achieving high predictive accuracy.

This paper simulates genetic risk scores using summary statistics to enable comparison across datasets, showing similar performance to real data in type 1 diabetes.

This paper estimates disease prevalence using genetic risk scores and evaluates methods for robust prevalence estimation across different scenarios.