Largest Genetic Study of Type 1 Diabetes Identifies New Causal Variants and Drug Targets

2021 ◽  
Vol 8 (5) ◽  
pp. 7-7
Keyword(s):  
Type 1 Diabetes ◽  
Genetic Study ◽  
Drug Targets ◽  
Causal Variants

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

2021 ◽  
Author(s):  
Catherine C. Robertson ◽  
Jamie R. J. Inshaw ◽  
Suna Onengut-Gumuscu ◽  
Wei-Min Chen ◽  
David Flores Santa Cruz ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Fine Mapping ◽  
Drug Targets ◽  
Genomic Analyses ◽  
Causal Variants

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

2020 ◽  
Author(s):  
C.C. Robertson ◽  
J.R.J. Inshaw ◽  
S. Onengut-Gumuscu ◽  
W.M. Chen ◽  
D. Flores Santa Cruz ◽  
...  
Keyword(s):  
T Cells ◽  
Type 1 Diabetes ◽  
Protein Interactions ◽  
Drug Targets ◽  
Immune Cell ◽  
Causal Variant ◽  
Chromatin Accessibility ◽  
High Yield ◽  
Causal Variants

AbstractWe 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.

scholarly journals Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes

2018 ◽  
Vol 50 (10) ◽  
pp. 1366-1374 ◽  
Author(s):  
Harm-Jan Westra ◽  
Marta Martínez-Bonet ◽  
Suna Onengut-Gumuscu ◽  
Annette Lee ◽  
Yang Luo ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Type 1 Diabetes ◽  
Fine Mapping ◽  
Functional Studies ◽  
Causal Variants

scholarly journals Fine-mapping identifies causal variants for RA and T1D in DNASE1L3, SIRPG, MEG3, TNFAIP3 and CD28/CTLA4 loci

2017 ◽  
Author(s):  
Harm-Jan Westra ◽  
Marta Martinez Bonet ◽  
Suna Onengut ◽  
Annette Lee ◽  
Yang Luo ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Functional Analysis ◽  
Type 1 Diabetes ◽  
Specific Binding ◽  
Common Variants ◽  
Enhancer Activity ◽  
Allele Specific ◽  
Causal Variants ◽  
Coding Variants

We fine-mapped 76 rheumatoid arthritis (RA) and type 1 diabetes (T1D) loci outside of the MHC. After sequencing 799 1kb regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We fine-mapped1,2 these loci in RA (11,475 cases, 15,870 controls)3, T1D (9,334 cases and 11,111 controls) 4 and combined datasets. We reduced the number of potential causal variants to ≤5 in 8 RA and 11 T1D loci. We identified causal missense variants in five loci (DNASE1L3, SIRPG, PTPN22, SH2B3 and TYK2) and likely causal non-coding variants in six loci (MEG3, TNFAIP3, CD28/CTLA4, ANKRD55, IL2RA, REL/PUS10). Functional analysis confirmed allele specific binding and differential enhancer activity for three variants: the CD28/CTLA4 rs117701653 SNP, the TNFAIP3 rs35926684 indel, and the MEG3 rs34552516 indel. This study demonstrates the potential for dense genotyping and imputation to pinpoint missense and non-coding causal alleles.

scholarly journals Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers

2015 ◽  
Vol 47 (4) ◽  
pp. 381-386 ◽  
Author(s):  
Suna Onengut-Gumuscu ◽  
◽  
Wei-Min Chen ◽  
Oliver Burren ◽  
Nick J Cooper ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Fine Mapping ◽  
Susceptibility Loci ◽  
Diabetes Susceptibility ◽  
Causal Variants

Type 1 diabetes genome-wide association analysis with imputation identifies five new risk regions

2017 ◽  
Author(s):  
Nicholas J. Cooper ◽  
Chris Wallace ◽  
Oliver Burren ◽  
Antony Cutler ◽  
Neil Walker ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Meta Analysis ◽  
Low Frequency ◽  
Candidate Snps ◽  
Phase 3 ◽  
Expression Studies ◽  
Genome Wide ◽  
Causal Genes ◽  
Causal Variants

AbstractType 1 diabetes genotype datasets have undergone several well powered genome wide analysis studies (GWAS), identifying 57 associated regions at the time of analysis. There are still many regions of smaller effect size or low frequency left to discover, and better exploitation of existing type 1 diabetes cohorts with meta analysis and imputation can precede the acquisition of new or larger cohorts. An existing dataset of 5,913 case and 8,828 control samples was analysed using genome-wide microarrays (Affymetrix GeneChip 500K and Illumina Infinium 550K) with imputation via IMPUTE2 with the 1000 Genomes Project (phase 3) reference panel. Genotyping coverage was doubled in known association regions, and increased by four fold in other regions compared to previous studies. Our analysis resulted in new index variants for 17/57 regions, an expanded set of plausible candidate SNPs for 17 regions, and five novel type 1 diabetes association regions at 1p31.3, 1q24.3, 1q31.2, 2q11.2 and 11q12.2. Candidate genes for the new loci included ITGB3BP, FASLG, RGS1, AFF3 and CD5/CD6. Further prioritisation of causal genes and causal variants will require detailed RNA and protein expression studies, in conjunction with genome annotation studies including analysis of physical promoter-enhancer interactions.

scholarly journals Enhanced genetic analysis of type 1 diabetes by selecting variants on both effect size and significance, and by integration with autoimmune thyroid disease

2021 ◽  
Author(s):  
Daniel J. M. Crouch ◽  
Jamie R.J. Inshaw ◽  
Catherine C. Robertson ◽  
Jia-Yuan Zhang ◽  
Wei-Min Chen ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Effect Size ◽  
Thyroid Disease ◽  
Autoimmune Thyroid Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Positive Association ◽  
Autoimmune Thyroid ◽  
Causal Variants

AbstractFor polygenic traits, associations with genetic variants can be detected over many chromosome regions, owing to the availability of large sample sizes. The majority of variants, however, have small effects on disease risk and, therefore, unraveling the causal variants, target genes, and biology of these variants is challenging. Here, we define the Bigger or False Discovery Rate (BFDR) as the probability that either a variant is a false-positive or a randomly drawn, true-positive association exceeds it in effect size. Using the BFDR, we identify new variants with larger effect associations with type 1 diabetes and autoimmune thyroid disease.

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