Next steps in the identification of gene targets for type 1 diabetes
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REVIEW
Next steps in the identification of gene targets for type 1 diabetes Struan F. A. Grant 1,2,3
&
Andrew D. Wells 1,4
&
Stephen S. Rich 5,6
Received: 14 February 2020 / Accepted: 16 June 2020 # The Author(s) 2020
Abstract The purpose of this review is to provide a view of the future of genomics and other omics approaches in defining the genetic contribution to all stages of risk of type 1 diabetes and the functional impact and clinical implementations of the associated variants. From the recognition nearly 50 years ago that genetics (in the form of HLA) distinguishes risk of type 1 diabetes from type 2 diabetes, advances in technology and sample acquisition through collaboration have identified over 60 loci harbouring SNPs associated with type 1 diabetes risk. Coupled with HLA region genes, these variants account for the majority of the genetic risk (~50% of the total risk); however, relatively few variants are located in coding regions of genes exerting a predicted protein change. The vast majority of genetic risk in type 1 diabetes appears to be attributed to regions of the genome involved in gene regulation, but the target effectors of those genetic variants are not readily identifiable. Although past genetic studies clearly implicated immune-relevant cell types involved in risk, the target organ (the beta cell) was left untouched. Through emergent technologies, using combinations of genetics, gene expression, epigenetics, chromosome conformation and gene editing, novel landscapes of how SNPs regulate genes have emerged. Furthermore, both the immune system and the beta cell and their biological pathways have been implicated in a context-specific manner. The use of variants from immune and beta cell studies distinguish type 1 diabetes from type 2 diabetes and, when they are combined in a genetic risk score, open new avenues for prediction and treatment.
Keywords Chromatin . Enhancers . eQTLs . Genetics . Prediction . Review . Target genes . Type 1 Diabetes Abbreviations eQTL Expression quantitative trait locus GRS Genetic risk score Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00125-020-05248-8) contains a slide of the figure for download, which is available to authorised users. * Stephen S. Rich [email protected] 1
Center for Spatial and Functional Genomics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
2
Departments of Pediatrics and Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
3
Divisions of Human Genetics and Endocrinology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
4
Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
5
Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
6
Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
GWAS IRE SLE T1DGC TFH WTCCC
Genome-wide association s
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