scholarly journals Peptide YY (PYY) Gene Polymorphisms in the 3′-Untranslated Region and Proximal Promoter Regions Regulate Cellular Gene Expression and PYY Secretion and Metabolic Syndrome Traits in Vivo

2009 ◽  
Vol 23 (10) ◽  
pp. 1715-1715
Author(s):  
Pei-an Betty Shih ◽  
Lei Wang ◽  
Stephane Chiron ◽  
Gen Wen ◽  
Caroline Nievergelt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Syndrome ◽  
Genetic Variation ◽  
Untranslated Region ◽  
Peptide Yy ◽  
Disease Risk ◽  
Association Studies ◽  
Neuroendocrine Cells ◽  
Proximal Promoter ◽  
Common Genetic Variants

ABSTRACT Rationale Obesity is a heritable trait that contributes to hypertension and subsequent cardiorenal disease risk; thus, the investigation of genetic variation that predisposes individuals to obesity is an important goal. Circulating peptide YY (PYY) is known for its appetite and energy expenditure-regulating properties; linkage and association studies have suggested that PYY genetic variation contributes to susceptibility for obesity, rendering PYY an attractive candidate for study of disease risk. Design To explore whether common genetic variation at the human PYY locus influences plasma PYY or metabolic traits, we systematically resequenced the gene for polymorphism discovery and then genotyped common single-nucleotide polymorphisms across the locus in an extensively phenotyped twin sample to determine associations. Finally, we experimentally validated the marker-on-trait associations using PYY 3′-untranslated region (UTR)/reporter and promoter/reporter analyses in neuroendocrine cells. Results Four common genetic variants were discovered across the locus, and three were typed in phenotyped twins. Plasma PYY was highly heritable (P < 0.0001), and genetic pleiotropy was noted between plasma PYY and body mass index (BMI) (P = 0.03). A PYY haplotype extending from the proximal promoter (A-23G, rs2070592) to the 3′-UTR (C+1134A, rs162431) predicted not only plasma PYY (P = 0.009) but also other metabolic syndrome traits. Functional studies with transfected luciferase reporters confirmed regulatory roles in altering gene expression for both 3′-UTR C+1134A (P < 0.001) and promoter A-23G (P = 0.0016). Conclusions Functional genetic variation at the PYY locus influences multiple heritable metabolic syndrome traits, likely conferring susceptibility to obesity and subsequent cardiorenal disease.

scholarly journals Peptide YY (PYY) Gene Polymorphisms in the 3′-Untranslated Region and Proximal Promoter Regions Regulate Cellular Gene Expression and PYY Secretion and Metabolic Syndrome Traits in Vivo

2009 ◽  
Vol 23 (12) ◽  
pp. 2120-2120
Author(s):  
Pei-an Betty Shih ◽  
Lei Wang ◽  
Stephane Chiron ◽  
Gen Wen ◽  
Caroline Nievergelt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Syndrome ◽  
Genetic Variation ◽  
Untranslated Region ◽  
Peptide Yy ◽  
Disease Risk ◽  
Association Studies ◽  
Neuroendocrine Cells ◽  
Proximal Promoter ◽  
Common Genetic Variants

ABSTRACT Rationale Obesity is a heritable trait that contributes to hypertension and subsequent cardiorenal disease risk; thus, the investigation of genetic variation that predisposes individuals to obesity is an important goal. Circulating peptide YY (PYY) is known for its appetite and energy expenditure-regulating properties; linkage and association studies have suggested that PYY genetic variation contributes to susceptibility for obesity, rendering PYY an attractive candidate for study of disease risk. Design To explore whether common genetic variation at the human PYY locus influences plasma PYY or metabolic traits, we systematically resequenced the gene for polymorphism discovery and then genotyped common single-nucleotide polymorphisms across the locus in an extensively phenotyped twin sample to determine associations. Finally, we experimentally validated the marker-on-trait associations using PYY 3′-untranslated region (UTR)/reporter and promoter/reporter analyses in neuroendocrine cells. Results Four common genetic variants were discovered across the locus, and three were typed in phenotyped twins. Plasma PYY was highly heritable (P < 0.0001), and genetic pleiotropy was noted between plasma PYY and body mass index (BMI) (P = 0.03). A PYY haplotype extending from the proximal promoter (A-23G, rs2070592) to the 3′-UTR (C+1134A, rs162431) predicted not only plasma PYY (P = 0.009) but also other metabolic syndrome traits. Functional studies with transfected luciferase reporters confirmed regulatory roles in altering gene expression for both 3′-UTR C+1134A (P < 0.001) and promoter A-23G (P = 0.0016). Conclusions Functional genetic variation at the PYY locus influences multiple heritable metabolic syndrome traits, likely conferring susceptibility to obesity and subsequent cardiorenal disease.

scholarly journals Peptide YY (PYY) Gene Polymorphisms in the 3′-Untranslated Region and Proximal Promoter Regions Regulate Cellular Gene Expression and PYY Secretion and Metabolic Syndrome Traits in Vivo

2009 ◽  
Vol 30 (7) ◽  
pp. 934-934
Author(s):  
Pei-an Betty Shih ◽  
Lei Wang ◽  
Stephane Chiron ◽  
Gen Wen ◽  
Caroline Nievergelt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Syndrome ◽  
Genetic Variation ◽  
Untranslated Region ◽  
Peptide Yy ◽  
Disease Risk ◽  
Association Studies ◽  
Neuroendocrine Cells ◽  
Proximal Promoter ◽  
Common Genetic Variants

Abstract Rationale Obesity is a heritable trait that contributes to hypertension and subsequent cardiorenal disease risk; thus, the investigation of genetic variation that predisposes individuals to obesity is an important goal. Circulating peptide YY (PYY) is known for its appetite and energy expenditure-regulating properties; linkage and association studies have suggested that PYY genetic variation contributes to susceptibility for obesity, rendering PYY an attractive candidate for study of disease risk. Design To explore whether common genetic variation at the human PYY locus influences plasma PYY or metabolic traits, we systematically resequenced the gene for polymorphism discovery and then genotyped common single-nucleotide polymorphisms across the locus in an extensively phenotyped twin sample to determine associations. Finally, we experimentally validated the marker-on-trait associations using PYY 3′-untranslated region (UTR)/reporter and promoter/reporter analyses in neuroendocrine cells. Results Four common genetic variants were discovered across the locus, and three were typed in phenotyped twins. Plasma PYY was highly heritable (P < 0.0001), and genetic pleiotropy was noted between plasma PYY and body mass index (BMI) (P = 0.03). A PYY haplotype extending from the proximal promoter (A-23G, rs2070592) to the 3′-UTR (C+1134A, rs162431) predicted not only plasma PYY (P = 0.009) but also other metabolic syndrome traits. Functional studies with transfected luciferase reporters confirmed regulatory roles in altering gene expression for both 3′-UTR C+1134A (P < 0.001) and promoter A-23G (P = 0.0016). Conclusions Functional genetic variation at the PYY locus influences multiple heritable metabolic syndrome traits, likely conferring susceptibility to obesity and subsequent cardiorenal disease.

scholarly journals The impact of rare variation on gene expression across tissues

Nature ◽  
2017 ◽  
Vol 550 (7675) ◽  
pp. 239-243 ◽  
Author(s):  
Xin Li ◽  
◽  
Yungil Kim ◽  
Emily K. Tsang ◽  
Joe R. Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Rare Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Sequencing Data ◽  
Common Genetic Variants ◽  
Coding Variants ◽  
The Impact

Abstract Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1,2,3,4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8,9,10,11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

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

2020 ◽  
Vol 117 (26) ◽  
pp. 15028-15035 ◽  
Author(s):  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin
Keyword(s):  
Gene Expression ◽  
Multiple Testing ◽  
Disease Risk ◽  
Association Studies ◽  
Auxiliary Information ◽  
Primary Data ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Hypothesis Tests ◽  
Selective Inference

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.

scholarly journals A novel quantile regression approach for eQTL discovery

2016 ◽  
Author(s):  
Xiaoyu Song ◽  
Gen Li ◽  
Iuliana Ionita-Laza ◽  
Ying Wei
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Linear Regression ◽  
Genetic Variants ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Expression Level ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractOver the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Indeed, several studies have already investigated higher order associations with a special focus on detecting heteroskedasticity. In this paper, we develop a Quantile Rank-score Based Test (QRBT) to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRBT to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRBT complements the existing methods, and identifies new eQTLs with heterogeneous effects genome-wideacross different quantile levels. Notably, we show that the eQTLs identified by QRBT but missed by linear regression are more likely to be tissue specific, and also associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog. An R package implementing QRBT is available on our website.

scholarly journals Associations of ‘Relative corticosterone deficiency’ with genetic variation in CYP17A1 and metabolic syndrome features

2019 ◽  
Author(s):  
Scott D Mackenzie ◽  
Andrew A Crawford ◽  
Daniel Ackermann ◽  
Katharina E Schraut ◽  
Caroline Hayward ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Metabolic Syndrome ◽  
Genetic Variation ◽  
Lipid Profile ◽  
Fasting Glucose ◽  
Health Study ◽  
Hydroxylase Activity ◽  
The Metabolic Syndrome ◽  
Common Genetic Variants ◽  
Dexamethasone Suppression

ABSTRACTContext and objectiveCommon genetic variants in CYP17A1 associate with higher blood pressure, putatively from impaired 17α-hydroxylase activity and mineralocorticoid excess. However, the same variants protect against obesity and insulin resistance. We tested whether CYP17A1 variants that enhance 17α-hydroxylase activity cause ‘relative corticosterone deficiency’. Since corticosterone is thought to contribute disproportionately to negative feedback in the hypothalamic-pituitary-adrenal axis, we also tested whether lower corticosterone associates with higher cortisol and hence with metabolic syndrome.DesignCross-sectional studies within the population-based Orkney Complex Disease Study (ORCADES; n=2018), VIKING Health Study Shetland (VIKING; n=2098), East Hertfordshire study (EHERTS; n=279), Edinburgh Type 2 Diabetes Study (ET2DS; n=903), and the Swiss Kidney Project on Genes in Hypertension (SKIPOGH; n=888).Outcome measuresCortisol and corticosterone in morning plasma samples in ORCADES, VIKING and ET2DS, and in EHERTS in plasma following overnight dexamethasone suppression (0.25mg) and 30 mins after ACTH1-24 (1µg); cortisol and corticosterone metabolites in day and night urine samples in SKIPOGH. Features of the metabolic syndrome including body mass index, systolic blood pressure, lipid profile, fasting glucose, fasting insulin and HOMA-IR.ResultsIn ORCADES, ET2DS and SKIPOGH, CYP17A1 variants were associated with corticosterone:cortisol ratio. In ORCADES, VIKING and ET2DS there were consistent associations of morning plasma cortisol and corticosterone with BMI, blood pressure, lipid profile, fasting glucose and HOMA-IR. In EHERTS, however, after dexamethasone suppression and ACTH1-24 stimulation, impaired glucose tolerance and insulin sensitivity were associated with higher cortisol but lower corticosterone. Similarly, in SKIPOGH, low corticosterone:cortisol metabolite ratios were associated with high BMI and dyslipidemia.Conclusions‘Relative corticosterone deficiency’, due to a primary alteration in adrenal steroidogenesis favouring cortisol over corticosterone, may mediate the associations of genetic variation in CYP17A1 with metabolic syndrome. However, additional determinants of variation in plasma corticosterone are likely to explain its generally positive associations with features of metabolic syndrome.

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