scholarly journals Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

2019 ◽  
Author(s):  
Ana Viñuela ◽  
Arushi Varshney ◽  
Martijn van de Bunt ◽  
Rashmi B. Prasad ◽  
Olof Asplund ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Selective Enrichment ◽  
Gwas Signal

AbstractMost signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

scholarly journals Mendelian randomization studies of biomarkers and type 2 diabetes

2015 ◽  
Vol 4 (4) ◽  
pp. 249-260 ◽  
Author(s):  
Ali Abbasi
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Current Evidence ◽  
Genome Wide Association Studies ◽  
Glycaemic Traits

Many biomarkers are associated with type 2 diabetes (T2D) risk in epidemiological observations. The aim of this study was to identify and summarize current evidence for causal effects of biomarkers on T2D. A systematic literature search in PubMed and EMBASE (until April 2015) was done to identify Mendelian randomization studies that examined potential causal effects of biomarkers on T2D. To replicate the findings of identified studies, data from two large-scale, genome-wide association studies (GWAS) were used: DIAbetes Genetics Replication And Meta-analysis (DIAGRAMv3) for T2D and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for glycaemic traits. GWAS summary statistics were extracted for the same genetic variants (or proxy variants), which were used in the original Mendelian randomization studies. Of the 21 biomarkers (from 28 studies), ten have been reported to be causally associated with T2D in Mendelian randomization. Most biomarkers were investigated in a single cohort study or population. Of the ten biomarkers that were identified, nominally significant associations with T2D or glycaemic traits were reached for those genetic variants related to bilirubin, pro-B-type natriuretic peptide, delta-6 desaturase and dimethylglycine based on the summary data from DIAGRAMv3 or MAGIC. Several Mendelian randomization studies investigated the nature of associations of biomarkers with T2D. However, there were only a few biomarkers that may have causal effects on T2D. Further research is needed to broadly evaluate the causal effects of multiple biomarkers on T2D and glycaemic traits using data from large-scale cohorts or GWAS including many different genetic variants.

scholarly journals Sequence Variants ofADIPOQand Association with Type 2 Diabetes Mellitus in Taiwan Chinese Han Population

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ming-Kai Tsai ◽  
Hui-Min David Wang ◽  
Jeng-Chuan Shiang ◽  
I-Hung Chen ◽  
Chih-Chiang Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Large Scale ◽  
Association Studies ◽  
Chinese Han Population ◽  
Genome Wide Association Studies ◽  
Chinese Han ◽  
Han Population

Diabetes is a serious global health problem. Large-scale genome-wide association studies identified loci for type 2 diabetes mellitus (T2DM), including adiponectin (ADIPOQ) gene and transcription factor 7-like 2 (TCF7L2), but few studies clarified the effect of genetic polymorphisms ofADIPOQandTCF7L2on risk of T2DM. We attempted to elucidate association between T2DM and polymorphic variations of both in Taiwan’s Chinese Han population, with our retrospective case-control study genotyping single nucleotide polymorphisms (SNPs) inADIPOQandTCF7L2genes both in 149 T2DM patients and in 139 healthy controls from Taiwan. Statistical analysis gauged association of these polymorphisms with risk of T2DM to showADIPOQrs1501299 polymorphism variations strongly correlated with T2DM risk(P=0.042), with rs2241766 polymorphism being not associated with T2DM(P=0.967). However, both polymorphisms rs7903146 and rs12255372 ofTCF7L2were rarely detected in Taiwanese people. This study avers thatADIPOQrs1501299 polymorphism contributes to risk of T2DM in the Taiwanese population.

Progress in the genetics of common obesity and type 2 diabetes

2010 ◽  
Vol 12 ◽  
Author(s):  
Karani S. Vimaleswaran ◽  
Ruth J.F. Loos
Keyword(s):  
Type 2 Diabetes ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Epidemiological Methods ◽  
Genome Wide Association Studies ◽  
Obesity And Diabetes ◽  
Genome Wide ◽  
Genomics And Proteomics

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

scholarly journals Identification of novel non-synonymous variants associated with type 2 diabetes-related metabolites in Korean population

2019 ◽  
Vol 39 (10) ◽  
Author(s):  
Tae-Joon Park ◽  
Heun-Sik Lee ◽  
Young Jin Kim ◽  
Bong-Jo Kim
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Multiple Testing ◽  
Association Studies ◽  
Linear Regression Analysis ◽  
Genetic Regulation ◽  
Genome Wide Association Studies ◽  
Functional Variants ◽  
Genome Wide

Abstract Metabolome-genome wide association studies (mGWASs) are useful for understanding the genetic regulation of metabolites in complex diseases, including type 2 diabetes (T2D). Numerous genetic variants associated with T2D-related metabolites have been identified in previous mGWASs; however, these analyses seem to have difficulty in detecting the genetic variants with functional effects. An exome array focussed on potentially functional variants is an alternative platform to obtain insight into the genetics of biochemical conversion processes. In the present study, we performed an mGWAS using 27,140 non-synonymous variants included in the Illumina HumanExome BeadChip and nine T2D-related metabolites identified by a targetted metabolomics approach to evaluate 2,338 Korean individuals from the Korea Association REsource (KARE) cohort. A linear regression analysis controlling for age, sex, BMI, and T2D status as covariates was performed to identify novel non-synonymous variants associated with T2D-related metabolites. We found significant associations between glycine and CPS1 (rs1047883) and PC ae C36:0 and CYP4F2 (rs2108622) variants (P<2.05 × 10−7, after the Bonferroni correction for multiple testing). One of the two significantly associated variants, rs1047883 was newly identified whereas rs2108622 had been previously reported to be associated with T2D-related traits. These findings expand our understanding of the genetic determinants of T2D-related metabolites and provide a basis for further functional validation.

scholarly journals circVAR database: genome-wide archive of genetic variants for human circular RNAs

2020 ◽  
Author(s):  
Min Zhao ◽  
Hong Qu
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Association Studies ◽  
Chromosome 17 ◽  
Circular Rnas ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background: Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results: We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research. Conclusion: We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar .

scholarly journals Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana Viñuela ◽  
Arushi Varshney ◽  
Martijn van de Bunt ◽  
Rashmi B. Prasad ◽  
Olof Asplund ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association ◽  
Regulatory Sequences ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Genome Wide ◽  
Gwas Signal ◽  
Using Data

Abstract Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.

scholarly journals A common variant in the CLDN7/ELP5 locus predicts adiponectin change with lifestyle intervention and improved fitness in obese individuals with diabetes

2015 ◽  
Vol 47 (6) ◽  
pp. 215-224 ◽  
Author(s):  
L. Maria Belalcazar ◽  
George D. Papandonatos ◽  
Jeanne M. McCaffery ◽  
Inga Peter ◽  
Nicholas M. Pajewski ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Weight Loss ◽  
Lifestyle Intervention ◽  
Genetic Variants ◽  
Association Studies ◽  
Lifestyle Changes ◽  
Genome Wide Association Studies ◽  
Look Ahead ◽  
Genome Wide

Overweight/obese individuals with Type 2 diabetes have low adiponectin levels, which may improve with lifestyle changes. We investigated whether genetic variants associated with adiponectin levels in genome-wide association studies (GWAS) would also be related with adiponectin changes in response to an intensive lifestyle intervention (ILI), potentially through mechanisms altering the adipose microenvironment via weight loss and/or improved cardiorespiratory fitness. Look AHEAD was a randomized trial comparing the cardiovascular benefits of ILI-induced weight loss and physical activity compared with diabetes support and education among overweight/obese individuals with Type 2 diabetes. In a subsample of Look AHEAD with adiponectin data and genetic consent ( n = 1,351), we evaluated the effects of 24 genetic variants, demonstrated by GWAS to be cross-sectionally associated with adiponectin, on adiponectin change 1-yr postintervention. We explored via mediational analyses whether any differential effects by treatment arm were occurring through weight loss and/or improved fitness. A variant, rs222857, in the CLDN7 locus, potentially associated with epithelial barrier integrity and tight junction physiology, and a putative cis expression quantitative trail locus for elongator acetyltransferase complex subunit 5 ( ELP5), predicted adiponectin increases within ILI (log-adiponectin in overall sample per copy: β ± SE = 0.05 ± 0.02, P = 0.008; in non-Hispanic whites: 0.06 ± 0.02, P = 0.009). The favorable effects of rs222857 (minor allele frequency 45.5%) appeared to be mediated by mechanisms associated with improved fitness, and not weight loss. This is the first study to identify a genetic variant that modifies adiponectin response to lifestyle intervention in overweight/obese diabetic individuals.

scholarly journals Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

2021 ◽  
Author(s):  
Karthik A. Jagadeesh ◽  
Kushal K Dey ◽  
Daniel T. Montoro ◽  
Steven Gazal ◽  
Jesse M Engreitz ◽  
...  
Keyword(s):  
Single Cell ◽  
Disease Progression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Disease Risk ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Cascade Process ◽  
Cellular Processes

Cellular dysfunction is a hallmark of disease. Genome-wide association studies (GWAS) have provided a powerful means to identify loci and genes contributing to disease risk, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important both for our understanding of disease, and for developing therapeutic interventions. Here, we introduce a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression in cell types, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N=297K). The inferred disease enrichments recapitulated known biology and highlighted novel relationships for different conditions, including GABAergic neurons in major depressive disorder (MDD), disease progression programs in M cells in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

scholarly journals A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types

2021 ◽  
Vol 11 ◽  
Author(s):  
Huanhuan Zhu ◽  
Lulu Shang ◽  
Xiang Zhou
Keyword(s):  
Statistical Methods ◽  
Genetic Variants ◽  
Statistical Models ◽  
Complex Traits ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Genomic Information ◽  
Biological Mechanisms ◽  
Regulated Gene Expression

Genome-wide association studies (GWASs) have identified and replicated many genetic variants that are associated with diseases and disease-related complex traits. However, the biological mechanisms underlying these identified associations remain largely elusive. Exploring the biological mechanisms underlying these associations requires identifying trait-relevant tissues and cell types, as genetic variants likely influence complex traits in a tissue- and cell type-specific manner. Recently, several statistical methods have been developed to integrate genomic data with GWASs for identifying trait-relevant tissues and cell types. These methods often rely on different genomic information and use different statistical models for trait-tissue relevance inference. Here, we present a comprehensive technical review to summarize ten existing methods for trait-tissue relevance inference. These methods make use of different genomic information that include functional annotation information, expression quantitative trait loci information, genetically regulated gene expression information, as well as gene co-expression network information. These methods also use different statistical models that range from linear mixed models to covariance network models. We hope that this review can serve as a useful reference both for methodologists who develop methods and for applied analysts who apply these methods for identifying trait relevant tissues and cell types.

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