John A. Todd

The paper confirms non-neuronal Tau expression in multiple tissues of humans and mice using validated antibodies and tissue microarrays.

This paper integrates multi-omic data from 46,081 individuals to identify latent factors associated with diseases, showing improved prediction performance compared to single biomarkers.

This study shows that reducing tau in human neurons decreases A beta toxicity, supporting the role of tau in Alzheimer's disease mechanisms.

This paper evaluates 53 Tau antibodies for Western blot and immunohistochemistry, identifying those with high specificity and sensitivity for detecting Tau, including low-level and modified forms.

The paper describes the integration of a genetic risk prediction platform into the NHS newborn screening program for Type 1 Diabetes, demonstrating a model for population-based genetic research.

The paper identifies splicing QTLs in human pancreatic islets that contribute to type 2 diabetes susceptibility by affecting gene splicing and revealing novel molecular mechanisms.

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 investigates how HLA-DQβ157 polymorphism influences T cell receptor selection and links it to microbial antigens that mimic insulin, contributing to type 1 diabetes pathogenesis.

The paper identifies deleterious neutrophil states and altered granulopoiesis in sepsis using single-cell multi-omics data from sepsis patients and controls.

The paper shows that HLA class II haplotypes influence T1D risk by selecting for CD4+ T cell receptors with negatively charged CDR3β sequences that bind insulin, and demonstrates this in peripheral and islet T cells.

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 uses single-cell multi-omics to analyze T and NK cells in SLE patients, identifying IFN-driven changes and novel biomarkers.

This study analyzes within-host SARS-CoV-2 diversity and transmission patterns using 1390 UK clinical samples, finding low within-host diversity and a narrow transmission bottleneck.

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 presents an open resource for fine-mapping and gene prioritization at GWAS loci, identifying causal variants and genes and improving drug target identification.

This paper identifies four genetic regions associated with both type 1 and type 2 diabetes, with three showing opposite effect directions and one showing concordant 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.

This study shows that pro-inflammatory cytokines reveal plasticity in β-cell regulatory landscapes and identify T1D-associated variants disrupting cytokine-responsive enhancers.

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.

This study fine-maps RA and T1D loci outside the MHC, identifying causal variants and confirming their functional impact through allele-specific binding and enhancer activity.

Disruption of the Dexi gene accelerates diabetes in NOD mice and alters serum immunoglobulins, gut microbiome, and microbial metabolites.

The paper identifies 1,927 genetic associations with 1,478 plasma proteins using 10.6 million DNA variants and 3,301 individuals, revealing new drug targets and safety concerns.

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.