We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide significant ( P < 5 × 10 −8 ) regions, including 36 novel. We define credible sets of T1D-associated variants and show they are enriched in immune cell-accessible chromatin, particularly CD4 + effector T cells. Using chromatin accessibility profiling of CD4 + T cells from 115 individuals, we map chromatin accessibility quantitative trait loci (caQTLs) and identify five regions where T1D risk variants colocalize with caQTLs. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps, and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

33Japan 34 35 ABSTRACT 37 Genome-wide association studies (GWAS) have identified over 150,000 links between 38 common genetic variants and human traits or complex diseases. Over 80% of these 39 associations map to polymorphisms in non-coding DNA. Therefore, the challenge is 40 to identify disease-causing variants, the genes they affect, and the cells in which 41 these effects occur. We have developed a platform using ATAC-seq, DNaseI 42 footprints, NG Capture-C and machine learning to address this challenge. Applying 43 this approach to red blood cell traits identifies a significant proportion of known 44 causative variants and their effector genes, which we show can be validated by direct 45 in vivo modelling.Identification of the variation of the genome that determines the risk of common chronic and 48 infectious diseases informs on their primary causes, which leads to preventative or 49 therapeutic approaches and insights. Whilst genome-wide association studies (GWASs) 50 have identified thousands of chromosome regions 1 , the identification of the causal genes, 51 variants and cell types remains a major bottleneck. This is due to three major features of the 52 genome and its complex association with disease susceptibility. Trait-associated variants 53 are often tightly associated, through linkage disequilibrium (LD), with tens or hundreds of 54 other variants, mostly single-nucleotide polymorphisms (SNPs), any one or more of which 55 could be causal; the majority (>85%) the variants identified in GWAS lie within the non-56 coding genome 2 . Although non-coding regions are increasingly well annotated, many 57 variants do not correspond to known regulatory elements, and even when they do, it is rarely 58 known which genes these elements control, and in which cell types. New technical 59 approaches to link variants to the genes they control are rapidly improving but are often 60 limited by their sensitivity and resolution [3][4][5][6] ; and because so few causal variants have been 61 unequivocally linked to the genes they affect, the mechanisms by which non-coding variants 62 alter gene expression remain unknown in all but a few cases; and, third, the complexity of 63 gene regulation and cell/cell interactions means that knowing when in development, in which 64 cell type, in which activation state, and within which pathway(s) a causal variant exerts its 65 effect is usually impossible to predict. Although significant progress is being made, currently, 66 none of these problems has been adequately solved. 68Here, we have developed an integrated platform of experimental and computational 69 methods to prioritise likely causal variants, link them to the genes they regulate, and 70 determine the mechanism by which they alter gene function. To illustrate the approach we 71 have initially focussed on a single haematopoietic lineage: the development of mature red 72 blood cells (RBC), for which all stages of lineage specification and differentiation from a 73 haematopoietic stem cell to a RBC are known, and can be r...

Background: The rising prevalence of childhood obesity has been postulated as an explanation for the increasing rate of individuals diagnosed with type 1 diabetes (T1D). However, robust causal evidence supporting this claim has been extremely challenging to uncover, particularly given the typical early onset of T1D. Methods: In this study, we used genetic variation to separate the direct effect of childhood body size on T1D risk from the effects of body size at different stages in the life course using univariable and multivariable Mendelian randomization (MR). Similar MR analyses were conducted on risk of seven other chronic immune-associated diseases. Findings: Childhood body size provided evidence of an effect on T1D (based on a sample of 5,913 cases and 8,282 controls) using a univariable model (OR=2.05 per change in body size category, 95% CI=1.20 to 3.50, P=0.008), which remained after accounting for body size at birth and during adulthood (OR=2.32, 95% CI=1.21 to 4.42, P=0.013). The direct effect of childhood body size was validated using data from a large-scale T1D meta-analysis based on n=15,573 cases and n=158,408 controls (OR=1.94, 95% CI=1.21 to 3.12, P=0.006). We also obtained evidence that childhood adiposity influences risk of asthma (OR=1.31, 95% CI=1.08 to 1.60, P=0.007), eczema (OR=1.25, 95% CI=1.03 to 1.51, P=0.024) and hypothyroidism (OR=1.42, 95% CI=1.12 to 1.80, P=0.004). However, these estimates all attenuated to the null when accounting for adult body size, suggesting that the effect of childhood adiposity on these outcomes is mediated by adiposity in later life. Interpretation: Our findings support a causal role for higher childhood adiposity on higher risk of being diagnosed with T1D. In contrast, the effect of childhood adiposity on the other immune-associated diseases studied was explained by a long-term effect of remaining overweight for many years over the life course.