Untangling the genetic link between type 1 and type 2 diabetes using functional genomics

Nyaga, Denis M.; Vickers, Mark H.; Jefferies, Craig; Fadason, Tayaza; O’Sullivan, Justin M.

doi:10.1038/s41598-021-93346-x

Cited by 8 publications

(10 citation statements)

References 98 publications

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“…In addition to the rs7903146, we also identified SNP rs34855922 associated to T2D similar with (24), which again demonstrates the effectiveness of BayesR model combined with CSs. The rs7903146 and rs34855922 are two of the eight SNPs that mark regulatory elements within TCF7L2 locus (33). The rs7903146 coordinate regulation of TCF7L2 expression, and overlaps histone modification marks and an annotated enhancer in the pancreas (33).…”

Section: Discussionmentioning

confidence: 99%

“…The rs7903146 and rs34855922 are two of the eight SNPs that mark regulatory elements within TCF7L2 locus (33). The rs7903146 coordinate regulation of TCF7L2 expression, and overlaps histone modification marks and an annotated enhancer in the pancreas (33). Our study also identified an intronic variant (rs145034729) at the TCF7L2 locus.…”

Section: Discussionmentioning

confidence: 99%

“…However, it may function as an enhancer element, modulating the expression of distal genes without necessarily affecting the function of TCF7L2 itself. The discovery of multiple variants within the TCF7L2 locus is interesting, as (33) suggests that it acts as a regulatory hub for genes implicated in the etiology of T2D. Identifying these variants in this locus offers valuable insights into the biological mechanisms underlying T2D.…”

Section: Validation Of Blr Modelmentioning

confidence: 99%

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Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Shrestha,

Bai,

Gholipourshahraki

et al. 2023

Preprint

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Bayesian linear regression (BLR) models consider the underlying genetic architecture of complex phenotypes by specifying different prior distributions for SNP effects allowing heterogenous distribution of the true genetic signals. Our goal is to evaluate BLR models with BayesC and BayesR prior distributions for fine mapping on simulated and real binary and quantitative phenotypes, and compare them to the state-of-the-art external models: FINEMAP, SuSIE-RSS, SuSIE-Inf and FINEMAP-Inf. Evaluation of models was based on the F1 classification score for simulations, and predictive accuracy for the UK Biobank (UKB) phenotypes. We used over 533K genotyped SNPs (simulations) and 6.6 million imputed SNPs (UKB phenotypes), from over 335K White British Unrelated samples in the UK biobank. We simulated phenotypes from low (GA1) to moderate (GA2) polygenicity, heritability(h^2) of 30% and 10%, causal SNPs (π) of 0.1% and 1% sampled genome-wide, and prevalence (PV) of 5% and 15%. Summary statistics and in-sample linkage disequilibrium were used to fit models in regions defined by lead SNPs. BayesR improved the F1 score, averaged across all simulations, by by 27.26%, 26.96%, 18.40%, 15.42%, and 13.32% relative to FINEMAP-Inf, BayesC, FINEMAP, SUSIE-RSS and SUSIE-Inf. Prediction R2, averaged across all the UKB quantitative phenotypes, with BayesR decreased by 5.32% and 3.71% compared to SuSIE-Inf and FINEMAP-Inf, whereas increased by 7.93% and 8.3% compared to SuSIE-RSS and BayesC. Prediction AUC, averaged across all the UKB binary phenotypes, with BayesR increased by 0.40%, 0.16%, 0.08%., and 0.05% relative to SuSIE-RSS, BayesC, FINEMAP-Inf, and SuSIE-Inf. These findings suggest that the performance of the BLR models was comparable to the state-of-the-art external models. The performance of BayesR prior was closely aligned with SuSIE-Inf and FINEMAP-Inf models. Results from both simulations and application of the models in the UKB phenotypes suggest that the BLR models are efficient fine mapping tools.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Validation Of Blr Modelmentioning

confidence: 99%

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Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Shrestha,

Bai,

Gholipourshahraki

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…T1D and T2D disproportionally affect adults and youth, respectively, with T2D being more common in adults. However, it has been identified that ~15% of common genes between T1D and T2D encode for proteins targeted by FDA-approved drugs [53,54]. Some of those drugs cause diabetes or diabetes-like symptoms themselves, including hypoglycemia (i.e., streptozotocin, pembrolizumab, nivolumab, and doxorubicin).…”

Section: Treatment Of Diabetesmentioning

confidence: 99%

Recent Advances of Integrative Bio-Omics Technologies to Improve Type 1 Diabetes (T1D) Care

et al. 2021

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Type 1 diabetes (T1D) is a complex autoimmune disease that currently cannot be cured, only managed. Optimal treatment the of T1D symptoms, requires a multidisciplinary care team, including endocrinologists, educators, primary care providers, health care specialists, genetic counselors, and data scientists. This review summarizes how an integrative approach to T1D drives innovation and quality improvements in health care. Specifically, we highlight how “-omics” technologies facilitate the understanding of different aspects of the disease, including prevention, pathogenesis, diagnostics, and treatment. Furthermore, we explore how biological data can be combined with personal and electronic health records to tailor medical interventions to the individual’s biology and lifestyle. We conclude that truly personalized medicine will not be limited to one data source but will emerge from the integration of multiple sources and disciplines that together will support individuals with T1D in their everyday life.

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“…To this end, efforts have been made to develop analytical pipelines that integrate genetic, genomic and biological data to produce networks that indicate connectivity between GWAS loci and candidate causal genes. For example, Nyaga et al ( Nyaga et al, 2021 ) used integrated genomics to ask whether there were any shared genetic features of type I diabetes (T1D) and T2D; their functional approach integrated Hi-C and eQTL data to characterise the functional impacts of disease-associated SNPs, identifying genetic regulatory regions that alter regulation of genes common to both T1D and T2D, that are associated with disease development. Additionally, Fernández-Tajes et al ( Fernández-Tajes et al, 2019 ) present an analytical pipeline to define the transcriptional activity of T2D-associated SNPs, integrating genomic data to reveal connectivity between candidate genes at T2D GWAS loci.…”

Section: Introductionmentioning

confidence: 99%

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

et al. 2022

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Epidemiological and associative research from humans and animals identifies correlations between the environment and health impacts. The environment—health inter-relationship is effected through an individual’s underlying genetic variation and mediated by mechanisms that include the changes to gene regulation that are associated with the diversity of phenotypes we exhibit. However, the causal relationships have yet to be established, in part because the associations are reduced to individual interactions and the combinatorial effects are rarely studied. This problem is exacerbated by the fact that our genomes are highly dynamic; they integrate information across multiple levels (from linear sequence, to structural organisation, to temporal variation) each of which is open to and responds to environmental influence. To unravel the complexities of the genomic basis of human disease, and in particular non-communicable diseases that are also influenced by the environment (e.g., obesity, type II diabetes, cancer, multiple sclerosis, some neurodegenerative diseases, inflammatory bowel disease, rheumatoid arthritis) it is imperative that we fully integrate multiple layers of genomic data. Here we review current progress in integrated genomic data analysis, and discuss cases where data integration would lead to significant advances in our ability to predict how the environment may impact on our health. We also outline limitations which should form the basis of future research questions. In so doing, this review will lay the foundations for future research into the impact of the environment on our health.

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Untangling the genetic link between type 1 and type 2 diabetes using functional genomics

Cited by 8 publications

References 98 publications

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Recent Advances of Integrative Bio-Omics Technologies to Improve Type 1 Diabetes (T1D) Care

A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk

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