scholarly journals Statistical Colocalization of Genetic Risk Variants for Related Autoimmune Diseases in the Context of Common Controls

2015 ◽  
Author(s):  
Mary D Fortune ◽  
Hui Guo ◽  
Oliver Burren ◽  
Ellen Schofield ◽  
Neil M Walker ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Genetic Risk ◽  
Control Design ◽  
Causal Variant ◽  
The Other ◽  
Risk Variants ◽  
Immune Attack ◽  
Disease Associations ◽  
Causal Variants

Identifying whether potential causal variants for related diseases are shared can increase understanding of the shared etiology between diseases. Colocalization methods are designed to disentangle shared and distinct causal variants in regions where two diseases show association, but existing methods are limited by assuming independent datasets. We extended existing methods to allow for the shared control design common in GWAS and applied them to four autoimmune diseases: type 1 diabetes (T1D); rheumatoid arthritis; celiac disease (CEL) and multiple sclerosis (MS). Ninety regions associated with at least one disease. In 22 regions (24%), we identify association to precisely one of our four diseases and can find no published association of any other disease to the same region; some of these may reflect effects mediated by the target of immune attack. Thirty-three regions (37%) were associated with two or more, but in 14 of these there was evidence that causal variants differed between diseases. By leveraging information across datasets, we identified novel disease associations to 12 regions previously associated with one or more of the other three autoimmune disorders. For instance, we link the CEL-associatedFASLGregion to T1D and identify a single SNP, rs78037977, as a likely causal variant. We also highlight several particularly complex association patterns, including theCD28-CTLA4-ICOSregion, in which it appears that three distinct causal variants associate with three diseases in three different patterns. Our results underscore the complexity in genetic variation underlying related but distinct autoimmune diseases and help to approach its dissection.

scholarly journals De‐ coding genetic risk variants in type 1 diabetes

2021 ◽  
Author(s):  
Melanie R Shapiro ◽  
Puchong Thirawatananond ◽  
Leeana Peters ◽  
Robert C Sharp ◽  
Similoluwa Ogundare ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Genetic Risk ◽  
Risk Variants ◽  
Genetic Risk Variants

scholarly journals Functional genomics of autoimmune diseases

2021 ◽  
pp. annrheumdis-2019-216794
Author(s):  
Akari Suzuki ◽  
Matteo Maurizio Guerrini ◽  
Kazuhiko Yamamoto
Keyword(s):  
Autoimmune Diseases ◽  
Association Studies ◽  
Linkage Disequilibrium Block ◽  
Causal Variant ◽  
Genome Wide Association Studies ◽  
Coding Region ◽  
Risk Variants ◽  
Non Coding Rna ◽  
Genome Wide ◽  
Long Non Coding Rna

For more than a decade, genome-wide association studies have been applied to autoimmune diseases and have expanded our understanding on the pathogeneses. Genetic risk factors associated with diseases and traits are essentially causative. However, elucidation of the biological mechanism of disease from genetic factors is challenging. In fact, it is difficult to identify the causal variant among multiple variants located on the same haplotype or linkage disequilibrium block and thus the responsible biological genes remain elusive. Recently, multiple studies have revealed that the majority of risk variants locate in the non-coding region of the genome and they are the most likely to regulate gene expression such as quantitative trait loci. Enhancer, promoter and long non-coding RNA appear to be the main target mechanisms of the risk variants. In this review, we discuss functional genetics to challenge these puzzles.

scholarly journals Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls

2015 ◽  
Vol 47 (7) ◽  
pp. 839-846 ◽  
Author(s):  
Mary D Fortune ◽  
Hui Guo ◽  
Oliver Burren ◽  
Ellen Schofield ◽  
Neil M Walker ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Genetic Risk ◽  
Risk Variants ◽  
Genetic Risk Variants

scholarly journals CRISPRa screen on a genetic risk locus shared by multiple autoimmune diseases identifies a dysfunctional enhancer that affects IRF8 expression through cooperative lncRNA and DNA methylation machinery

2021 ◽  
Author(s):  
Tian Zhou ◽  
Xinyi Zhu ◽  
Zhizhong Ye ◽  
Yongfei Wang ◽  
Chao Yao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autoimmune Diseases ◽  
Genetic Variants ◽  
Genetic Risk ◽  
Drug Targets ◽  
Distal Enhancer ◽  
Enhancer Rna ◽  
Functional Variants ◽  
Risk Variants ◽  
A Cell

Dysregulated transcription factors represent a major class of drug targets that mediate the abnormal expression of many critical genes involved in SLE and other autoimmune diseases. Although strong evidence suggests that natural human genetic variation affects basal and inducible gene expression, it is still a considerable challenge to establish a biological link between GWAS-identified non-coding genetic risk variants and their regulated gene targets. Here, we combine genetic data, epigenomic data, and CRISPR activation (CRISPRa) assays to screen for functional variants regulating IRF8 expression. Using CRISPR-mediated deletion and 3D chromatin structure analysis, we demonstrate that the locus containing rs2280381 is a cell-type-specific distal enhancer for IRF8 that spatially interacts with the IRF8 promoter. Further, rs2280381 mediates IRF8 expression through enhancer RNA AC092723.1, which recruits TET1 to the IRF8 promoter to modulate IRF8 expression by affecting methylation levels. The alleles of rs2280381 modulate PU.1 binding and chromatin state to differentially regulate AC092723.1 and IRF8 expression. Our work illustrates a strategy to define the functional genetic variants modulating transcription factor gene expression levels and identifies the biologic mechanism by which autoimmune diseases risk genetic variants contribute to the pathogenesis of disease.

scholarly journals Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes

2012 ◽  
Vol 21 (12) ◽  
pp. 2815-2824 ◽  
Author(s):  
Chris Wallace ◽  
Maxime Rotival ◽  
Jason D. Cooper ◽  
Catherine M. Rice ◽  
Jennie H.M. Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Type 1 Diabetes ◽  
Genetic Risk ◽  
Risk Variants ◽  
Genetic Risk Variants

scholarly journals Improving the coverage of credible sets in Bayesian genetic fine-mapping

2019 ◽  
Author(s):  
Anna Hutchinson ◽  
Hope Watson ◽  
Chris Wallace
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Causal Variant ◽  
Human Diseases ◽  
Credible Sets ◽  
Causal Variants ◽  
Credible Set ◽  
Genomic Regions

AbstractGenome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some “coverage probability”.Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the “corrected coverage estimate”. This is extended to find “corrected credible sets”, which are the smallest set of variants such that their corrected coverage estimate meets the target coverage.We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants – a pattern matched in simulations of well powered GWAS.Crucially, our correction method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases.Author summaryPinpointing specific genetic variants within the genome that are causal for human diseases is difficult due to complex correlation patterns existing between variants. Consequently, researchers typically prioritise a set of plausible causal variants for functional validation - these sets of putative causal variants are called “credible sets”. We find that the probabilistic interpretation that these credible sets do indeed contain the true causal variant is variable, in that the reported probabilities often underestimate the true coverage of the causal variant in the credible set. We have developed a method to provide researchers with a “corrected coverage estimate” that the true causal variant appears in the credible set, and this has been extended to find “corrected credible sets”, allowing for more efficient allocation of resources in the expensive follow-up laboratory experiments. We used our method to reduce the number of genetic variants to consider as causal candidates for follow-up in 27 genomic regions that are associated with type 1 diabetes.

scholarly journals Large-scale genetic association and single cell accessible chromatin mapping defines cell type-specific mechanisms of type 1 diabetes risk

2021 ◽  
Author(s):  
Joshua Chiou ◽  
Ryan J Geusz ◽  
Mei-Lin Okino ◽  
Jee Yun Han ◽  
Michael Miller ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Complex Disease ◽  
Cell Types ◽  
Causal Variant ◽  
Cell Type ◽  
Ductal Cells ◽  
Risk Variants ◽  
Cell Type Specific

ABSTRACTTranslating genome-wide association studies (GWAS) of complex disease into mechanistic insight requires a comprehensive understanding of risk variant effects on disease-relevant cell types. To uncover cell type-specific mechanisms of type 1 diabetes (T1D) risk, we combined genetic association mapping and single cell epigenomics. We performed the largest to-date GWAS of T1D in 489,679 samples imputed into 59.2M variants, which identified 74 novel association signals including several large-effect rare variants. Fine-mapping of 141 total signals substantially improved resolution of causal variant credible sets, which primarily mapped to non-coding sequence. To annotate cell type-specific regulatory mechanisms of T1D risk variants, we mapped 448,142 candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood mononuclear cell types using snATAC-seq of 131,554 nuclei. T1D risk variants were enriched in cCREs active in CD4+ T cells as well as several additional cell types including pancreatic exocrine acinar and ductal cells. High-probability T1D risk variants at multiple signals mapped to exocrine-specific cCREs including novel loci near CEL, GP2 and CFTR. At the CFTR locus, the likely causal variant rs7795896 mapped in a ductal-specific distal cCRE which regulated CFTR and the risk allele reduced transcription factor binding, enhancer activity and CFTR expression in ductal cells. These findings support a role for the exocrine pancreas in T1D pathogenesis and highlight the power of combining large-scale GWAS and single cell epigenomics to provide insight into the cellular origins of complex disease.

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 Shared genetic contribution to type 1 and type 2 diabetes risk

2018 ◽  
Author(s):  
Anthony Aylward ◽  
Joshua Chiou ◽  
Mei-Lin Okino ◽  
Nikita Kadakia ◽  
Kyle J Gaulton
Keyword(s):  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Genetic Risk ◽  
Islet Function ◽  
Risk Variants ◽  
Association Data ◽  
Shared Risk ◽  
Shared Genetic

AbstractThe role of shared genetic risk in the etiology of type 1 diabetes (T1D) and type 2 diabetes (T2D) and the mechanisms of these effects is unknown. In this study, we generated T1D association data of 15k samples imputed into the HRC reference panel which we compared to T2D association data of 159k samples imputed into 1000 Genomes. The effects of genetic variants on T1D and T2D risk at known loci and genome-wide were positively correlated, which we replicated using data from the UK Biobank and clinically-defined diabetes in the WTCCC. Increased risk of T1D and T2D was correlated with higher fasting insulin and fasting glucose level and decreased birth weight, among T1D- and T2D-specifc correlations, and T1D and T2D associated variants were enriched in regulatory elements for pancreatic, insulin resistance (adipose, CD19+ B cell), and developmental (CD184+ endoderm) cell types. We fine-mapped causal variants at known T1D and T2D loci and found evidence for co-localization at five signals, four of which had same direction of effect, including CENPW and GLIS3. Shared risk variants at GLIS3 and other signals were associated with measures of islet function, while CENPW was associated with early growth, and we identified shared risk variants at GLIS3 in islet accessible chromatin with allelic effects on islet regulatory activity. Our findings support shared genetic risk involving variants affecting islet function as well as insulin resistance, growth and development in the etiology of T1D and T2D.

scholarly journals Genetic risk variants for autoimmune diseases that influence gene expression in thymus

2016 ◽  
pp. ddw152 ◽  
Author(s):  
Ingvild S.M. Gabrielsen ◽  
Silja Svanstrøm Amundsen ◽  
Hanna Helgeland ◽  
Siri Tennebø Flåm ◽  
Nimo Hatinoor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autoimmune Diseases ◽  
Genetic Risk ◽  
Risk Variants ◽  
Influence Gene Expression ◽  
Genetic Risk Variants
Sign in / Sign up

Export Citation Format

Share Document