From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

Cirillo, Elisa; Kutmon, Martina; Hernández, Manuel González; Hooimeijer, Tom; Adriaens, Michiel E.; Eijssen, Lars; Parnell, Laurence D.; Coort, Susan L.; Evelo, Chris T.

doi:10.1371/journal.pone.0193515

Cited by 42 publications

(34 citation statements)

References 50 publications

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“…Applying gene ontology (GO) enrichment analysis to this gene list, we discovered enrichment for biological processes related to lipid metabolic process ( Fig. 5 ), consistent with previous literature ( 28 ). These results provide some reassurance that the lack of specificity in the SCZ results can be attributed to the complex etiology of SCZ.…”

Section: Resultssupporting

confidence: 89%

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

Yurko

G’Sell

Roeder

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptiveP-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS associationPvalues play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

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Section: Resultssupporting

confidence: 89%

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

Yurko

G’Sell

Roeder

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…The R-package, called CONQUER collects up-to-date data, directed by SNPs of interest from a multitude of databases and repositories and analyses and visualizes the data. In contrast to previous studies that had similar approaches [12,13], we have developed open-source software that is available as an R-package where only the SNPs and tissues of interest have to be specified and that can be used with minimal programming experience.…”

Section: Discussionmentioning

confidence: 99%

Understanding functional consequences of type 2 diabetes risk loci using the universal data integration and visualization R package CONQUER

Bouland

Beulens

Nap

et al. 2020

Preprint

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BackgroundNumerous large genome-wide association studies (GWASs) have been performed to understand the genetic factors of numerous traits, including type 2 diabetes. Many identified risk loci are located in non-coding and intergenic regions, which complicates the understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. ResultsHere, we developed the R-package CONQUER. Data for SNPs of interest (build GRCh38/hg38) were acquired from static-and dynamic repositories, such as, GTExPortal, Epigenomics Project, 4D genome database and genome browsers such as ENSEMBL. CONQUER modularizes SNPs based on the underlying co-expression data and associates them with biological pathways in specific tissues.CONQUER was used to analyze 403 previously identified type 2 diabetes risk loci. In all tissues, the majority of SNPs (mean = 13.50, SD = 11.70) were linked to metabolism. A tissue-shared effect was found for four type 2 diabetes-associated SNPs (rs601945, rs1061810, rs13737, rs4932265) that were associated with differential expression of HLA-DQA2, HSD17B12, MAN2C1 and AP3S2 respectively.Seven SNPs were identified that influenced the expression of seven ribosomal proteins in multiple tissues. Finally, one SNP (rs601945) was found to influence multiple HLA genes in all twelve tissues investigated. ConclusionWe present an universal R-package that aggregates and visualizes data in order to better understand functional consequences of GWAS loci. Using CONQUER, we showed that type 2 diabetes risk loci have many tissue-shared effects on multiple pathways including metabolism, the ribosome and HLA pathway.

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“…This loss may be due a high secretory demand in overtly hyperglycemic or obese individuals, high oxidative stress, misfolding in [153][154][155][156] Involving different populations, GWAS data are providing an ever-increasing number of T2D susceptibility variants. Interestingly, and despite the fact that insulin resistance is the main etiological factor in T2D development, 61,63,131,140,141,[157][158][159][160]…”

Section: β-Cell Function and Insulin Secretionmentioning

confidence: 99%

“…GWAS and genetic architecture studies should be continued for T2D, as well as for other polygenetic multifactorial diseases, in diverse populations and including gene-environment interaction which would likely contribute crucially to widening the terrain ofshared and unique population genetic effects for T2D 138,139. The overarching goal of these studies should emphasize on the translation and utilization of the gathered genetic information into daily clinical practice as well as clarifying the molecular mechanism for early disease prediction, minimizing the complications and therapeutic decision-making [139][140][141][142][143].…”

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confidence: 99%

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Sirdah

Reading

2020

Clinical Genetics

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Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

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From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

Cited by 42 publications

References 50 publications

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

Understanding functional consequences of type 2 diabetes risk loci using the universal data integration and visualization R package CONQUER

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

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