Biology subjects

Crouch, D. J.

Publications and source records attributed to Crouch, D. J..

3 recordsLinked to original sources

bioRxiv2020

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

We report the largest and most ancestrally diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 152 regions associated to false discovery rate < 0.01, including 36 regions associated to genome-wide significance for the first time. Credible sets of disease-associated variants are specifically enriched in immune cell accessible chromatin, particularly in CD4+ effector T cells. Colocalization with chromatin accessibility quantitative trait loci (QTL) in CD4+ T cells identified five regions where differences in T1D risk and chromatin accessibility are potentially driven by the same causal variant. Allele-specific chromatin accessibility further refined the set of putative causal variants with functional relevance in CD4+ T cells and integration of whole blood expression QTLs identified candidate T1D genes, providing high-yield targets for mechanistic follow-up. We highlight rs72938038 in BACH2 as a candidate causal T1D variant, where the T1D risk allele leads to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritise 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, including proposed genetic regulatory mechanisms of T1D-associated variants and genetic support for therapeutic targets for immune intervention.

genetics↗

bioRxiv2019

Specific type 1 diabetes risk genes underpin age-at-diagnosis and indicate joint defects in immunity, beta-cell fragility and responses to viral infections in early-onset disease.

ObjectiveImmunohistological analyses of pancreata from patients with type 1 diabetes suggest a stratification of islet pathology of both B and T lymphocyte islet inflammation common in children diagnosed at <7 years (<7 group), whereas B cells are rare in those diagnosed age [≥]13 ([≥]13 group). Based on these observations, we sought to identify differences in genetic susceptibility between these age-at-diagnosis groups, to inform on the aetiology of the most aggressive form of type 1 diabetes that initiates in the first years of life.\n\nResearch Design and MethodsUsing multinomial logistic regression models, we tested if known type 1 diabetes loci (17 within the HLA region and 55 non-HLA regions) had significantly stronger effect sizes in the <7 group compared to the [≥]13 group, using genotype data from 27,075 individuals (18,488 controls, 3,109 cases diagnosed at <7, 3,754 at 7-13 and 1,724 at [≥]13).\n\nResultsSix HLA haplotypes/classical alleles and seven non-HLA regions, one of which functions specifically in beta cells (GLIS3), and the other six likely affecting key T cell (IL2RA, IL10, SIRPG, IKZF3, THEMIS), thymus (THEMIS) and B cell development/functions (IKZF3, IL10) or in both immune and beta cells (CTSH) had stronger effects in the <7 group.\n\nConclusionsIn newborn children with the greatest load of certain risk alleles, dysregulated response of immune and beta cells to environmental stresses such as virus infection, combine to cause a rapid loss of insulin production, driving down the age of type 1 diabetes diagnosis.

genetics↗

bioRxiv2017

The genetics of the human face: identification of large effect single gene variants

In order to discover specific variants with relatively large effects on the human face we have devised an approach to identifying facial features with high heritability. This is based on using twin data to estimate the additive genetic value of each point on a face, as provided by a 3D camera system. In addition, we have used the ethnic difference between East Asian and European faces as a further source of face genetic variation. We use principal components analysis to provide a fine definition of the surface features of human faces around the eyes and of the profile, and chose upper and lower 10% extremes of the most heritable PCs for looking for genetic associations. Using this strategy for the analysis of 3D images of 1832 unique volunteers from the well characterised People of the British Isles study [1, 2] and 1567 unique twin images from the TwinsUK cohort (www.twinsuk.ac.uk), together with genetic data for 500,000 SNPs, we have identified three specific genetic variants with notable effects on facial profiles and eyes.\n\nSignificance statementThe human face is extraordinarily variable and the extreme similarity of the faces of identical twins indicates that most of this variability is genetically determined. This level of genetic variability has probably arisen through natural selection, for example, for recognition of membership of a group or as a consequence of differential mate selection with respect to facial features. We have devised an approach to identifying specific genetic effects on particular facial features. This should enable the understanding, eventually at the molecular level, of the nature of this extraordinary genetic variability, which is such an important feature of our everyday human interactions.\n\nAuthor ContributionsWFB conceived the project. BW and TD organised collection of PoBI data and KH, DJMC, DM, AB and WFB assisted in data collection. TDS, PH and AN collected TwinsUK data. WPK and WJC conducted image registration analysis under supervision of JK. DJMC analysed registered image data and genetic data under supervision of WFB. WFB and DJMC wrote the manuscript, with WJC and WPK contributing additional technical material. WFB and BW supervised the project.

genetics↗