From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

Baxter, Alan G.; Jordan, Margaret A.

doi:10.1900/rds.2012.9.201

Cited by 14 publications

(12 citation statements)

References 175 publications

(222 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…, were uniformly sampled from 0.04 to 0.08 for continuous traits or uniformly sampled from log (1.05) to log (1.15) for binary traits. This simulation setting for binary trait (odds ratios uniformly sampled from 1.05 to 1.15) concurs with broad GWAS findings, where most odds ratios < 1.5 and many odds ratios < 1.2 (Baxter and Jordan, 2012;Hodge and Greenberg, 2016).…”

Section: Simulation Studysupporting

confidence: 80%

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

Lin

Chan

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Some candidate genes have been robustly reported to be associated with complex traits, such as the fat mass and obesity-associated (FTO) gene on body mass index (BMI), and the fibroblast growth factor 5 (FGF5) gene on blood pressure levels. It is of interest to know whether an environmental factor (E) can attenuate or exacerbate the adverse influence of a candidate gene. To this end, we here evaluate the performance of "genetic risk score" (GRS) approaches to detect "gene-environment interactions" (G × E). In the first stage, a GRS is calculated according to the genotypes of variants in a candidate gene. In the second stage, we test whether E can significantly modify this GRS effect. This two-stage procedure can not only provide a p-value for a G × E test but also guide inferences on how E modifies the adverse effect of a gene. With systematic simulations, we compared several ways to construct a GRS. If E exacerbates the adverse influence of a gene, GRS formed by the elastic net (ENET) or the least absolute shrinkage and selection operator (LASSO) is recommended. However, the performance of ENET or LASSO will be compromised if E attenuates the adverse influence of a gene, and using the ridge regression (RIDGE) can be more powerful in this situation. Applying RIDGE to 18,424 subjects in the Taiwan Biobank, we showed that performing regular exercise can attenuate the adverse influence of the FTO gene on four obesity measures: BMI (p = 0.0009), body fat percentage (p = 0.0031), waist circumference (p = 0.0052), and hip circumference (p = 0.0001). As another example, we used RIDGE and found the FGF5 gene has a stronger effect on blood pressure in Han Chinese with a higher waistto-hip ratio [p = 0.0013 for diastolic blood pressure (DBP) and p = 0.0027 for systolic blood pressure (SBP)]. This study provides an evaluation on the GRS approaches, which is important to infer whether E attenuates or exacerbates the adverse influence of a candidate gene.

show abstract

Section: Simulation Studysupporting

confidence: 80%

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

Lin

Chan

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…This is a goal currently being pursued by our lab and others. Additional optimization, such as further genetic manipulation of TCR transgenic Tregs, could be implemented to correct intrinsic single-nucleotide polymorphisms (SNPs) with putative implications for Treg function and known associations with type 1 diabetes as identified by genome-wide association studies (GWAS) ( 56 ). Beyond this, there is potential for delivery of tissue repair factors directly to the pancreas via production by antigen-specific Tregs or via conjugation to the Treg surface using poly lactic-co-glycolic acid (PLGA) nanoparticles ( 57 – 59 ).…”

Section: Discussionmentioning

confidence: 99%

Avidity and Bystander Suppressive Capacity of Human Regulatory T Cells Expressing De Novo Autoreactive T-Cell Receptors in Type 1 Diabetes

et al. 2017

View full text Add to dashboard Cite

The ability to alter antigen specificity by T-cell receptor (TCR) or chimeric antigen receptor (CAR) gene transfer has facilitated personalized cellular immune therapies in cancer. Inversely, this approach can be harnessed in autoimmune settings to attenuate inflammation by redirecting the specificity of regulatory T cells (Tregs). Herein, we demonstrate efficient protocols for lentiviral gene transfer of TCRs that recognize type 1 diabetes-related autoantigens with the goal of tissue-targeted induction of antigen-specific tolerance to halt β-cell destruction. We generated human Tregs expressing a high-affinity GAD555–567-reactive TCR (clone R164), as well as the lower affinity clone 4.13 specific for the same peptide. We demonstrated that de novo Treg avatars potently suppress antigen-specific and bystander responder T-cell (Tresp) proliferation in vitro in a process that requires Treg activation (P < 0.001 versus unactivated Tregs). When Tresp were also glutamic acid decarboxylase (GAD)-reactive, the high-affinity R164 Tregs exhibited increased suppression (P < 0.01) with lower Tresp-division index (P < 0.01) than the lower affinity 4.13 Tregs. These data demonstrate the feasibility of rapid expansion of antigen-specific Tregs for applications in attenuating β-cell autoimmunity and emphasize further opportunities for engineering cellular specificities, affinities, and phenotypes to tailor Treg activity in adoptive cell therapies for the treatment of type 1 diabetes.

show abstract

“…They reported the presence of HLA-DR4 in 76% of these patients, which was significantly higher than in the general population (17%) and in a population with type-1 diabetes (42%). It is known that HLA-DR4 is associated with a higher risk to develop diabetes type 1 ( 73 ). It is therefore not surprising that patients expressing this HLA type have a higher risk to develop type 1 diabetes after ICI treatment.…”

Section: Biomarkersmentioning

confidence: 99%

Biomarkers of Checkpoint Inhibitor Induced Immune-Related Adverse Events—A Comprehensive Review

et al. 2021

View full text Add to dashboard Cite

Immune checkpoint inhibitors (ICIs) have substantially improved the prognosis of patients with different types of cancer. Through blockade of cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1), negative feedback mechanisms of the immune system are inhibited, potentially resulting in very durable anti-tumor responses. Despite their promise, ICIs can also elicit auto-immune toxicities. These immune-related adverse events (irAEs) can be severe and sometimes even fatal. Therefore, being able to predict severe irAEs in patients would be of added value in clinical decision making. A search was performed using “adverse events”, “immune checkpoint inhibitor”, “biomarker”, and synonyms in PubMed, yielding 3580 search results. After screening title and abstract on the relevance to the review question, statistical significance of reported potential biomarkers, and evaluation of the remaining full papers, 35 articles were included. Five additional reports were obtained by means of citations and by using the similar article function on PubMed. The current knowledge is presented in comprehensive tables summarizing blood-based, immunogenetic and microbial biomarkers predicting irAEs prior to and during ICI therapy. Until now, no single biomarker has proven to be sufficiently predictive for irAE development. Recommendations for further research on this topic are presented.

show abstract

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

Cited by 14 publications

References 175 publications

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene

Avidity and Bystander Suppressive Capacity of Human Regulatory T Cells Expressing De Novo Autoreactive T-Cell Receptors in Type 1 Diabetes

Biomarkers of Checkpoint Inhibitor Induced Immune-Related Adverse Events—A Comprehensive Review

Contact Info

Product

Resources

About