Antony J. Cutler

The paper identifies a genetic variant that affects transcription factor binding and reduces type 1 diabetes risk in infants by altering thymic gene expression.

The paper uses long-read sequencing and de novo assembly to identify structural variation in immune system gene regions from a single individual, revealing challenges in current variant calling methods.

This study identifies causal variants, cells, genes, and drug targets for type 1 diabetes using large-scale genetic and functional genomic analyses.

The paper identifies 302 new genetic signals for type 1 diabetes and autoimmune thyroid disease by combining effect size and significance, showing that only 28% of genome-wide significant signals have large effects.

The paper develops a platform combining ATAC-seq, DNaseI footprints, NG Capture-C, and machine learning to identify causal variants and genes for polygenic human traits.

The paper identifies genetic risk factors associated with early-onset type 1 diabetes, showing stronger effects in children diagnosed before age 7 compared to those diagnosed at 13 or older.

The paper identifies DNA methylation oscillations as a marker for enhancer classification, showing high predictive power compared to other methods.

The paper identifies autoimmune disease candidate genes by integrating promoter capture Hi-C data with genetic associations in activated CD4+ T cells.

The paper identifies SNP rs61839660 as the causal variant for autoimmunity by showing its loss reduces IL2RA expression and T1D protection.