Abstract 386: Identification of Genetic Regulatory Networks for Insulin Resistance in Multiple Populations of Diverse Ethnicities

2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Le Shu ◽  
Yuqi Zhao ◽  
Aldons J Lusis ◽  
Ke Hao ◽  
Thomas Quertermous ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Graphical Models ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Association Studies ◽  
Network Models ◽  
Mapk Signaling ◽  
Genetic Regulatory Networks ◽  
Specific Gene ◽  
Genome Wide Association Studies

Insulin resistance (IR) is a critical pathogenic factor for highly prevalent modern cardiometabolic diseases, including coronary artery disease (CAD) and type 2 diabetes (T2D). However, the molecular circuitries underlying IR remain to be elucidated. The GENEticS of Insulin Sensitivity Consortium (GENESIS) conducted genome-wide association studies (GWAS) for direct measures of IR using euglycemic clamp or insulin suppression test. We sought to identify gene networks and their key intervening drivers for IR by performing a comprehensive integrative analysis leveraging GWAS data from seven GENESIS cohorts representing three ethnic groups - Europeans, Asians and Hispanics, along with expression quantitative trait loci, ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from IR relevant tissues. Integration of the multi-ethnic GWAS with diverse functional genomics information captured shared IR pathways and networks across ethnicities that are independent of body mass index, including GLUT4 translocation regulation, insulin signaling, MAPK signaling, interleukin signaling, extracellular matrix, branched-chain amino acids metabolisms, cell cycle, and oxidative phosphorylation. Further integration of these GWAS-informed IR processes with graphical gene networks uncovered potential key regulators including HADH, COX5A, VCAN and TOP2A , whose network neighbors are consistently enriched for the genetic association signals of IR across ethnicities, and show significant correlation with IR, fasting glucose and insulin levels in the transcriptomic-wide association data from a Hybrid Mouse Diversity Panel comprised of >100 strains fed with high-fat diet. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide new understanding of the pathways, gene networks and potential regulators contributing to IR. These results will also facilitate future functional investigations to unveil how DNA variations translate into IR.

scholarly journals nMAGMA: a network enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

2020 ◽  
Author(s):  
Anyi Yang ◽  
Jingqi Chen ◽  
Xing-Ming Zhao
Keyword(s):  
Gene Networks ◽  
Association Studies ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Nucleotide Polymorphisms ◽  
Risk Genes ◽  
Tissue Specific ◽  
Genomic Annotation ◽  
Genome Wide

AbstractMotivationAnnotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multi-marker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. However, SNPs may also affect genes in a distance, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue-specificity of signals have not been taken into account.ResultsWe propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals, and tissue-specific gene networks. When applied to schizophrenia, nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are reasonably involved in schizophrenia compared to MAGMA and H-MAGMA. Some disease-related functions (e.g. the ATPase pathway in Cortex) tissues are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multi-tissue origins.

scholarly journals nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

2020 ◽  
Author(s):  
Anyi Yang ◽  
Jingqi Chen ◽  
Xing-Ming Zhao
Keyword(s):  
Gene Networks ◽  
Association Studies ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Nucleotide Polymorphisms ◽  
Risk Genes ◽  
Tissue Specific ◽  
Genomic Annotation ◽  
Dna Elements

Abstract Motivation: Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account. Results: We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

scholarly journals Multitrait genetic association analysis identifies 50 new risk loci for gastro-oesophageal reflux, seven new loci for Barrett’s oesophagus and provides insights into clinical heterogeneity in reflux diagnosis

Gut ◽  
2021 ◽  
pp. gutjnl-2020-323906
Author(s):  
Jue-Sheng Ong ◽  
Jiyuan An ◽  
Xikun Han ◽  
Matthew H Law ◽  
Priyanka Nandakumar ◽  
...  
Keyword(s):  
Risk Factors ◽  
Multiple Testing ◽  
Association Studies ◽  
Barrett’S Oesophagus ◽  
Oesophageal Reflux ◽  
Oesophageal Adenocarcinoma ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Disease Heterogeneity ◽  
Barrett's Oesophagus

ObjectiveGastro-oesophageal reflux disease (GERD) has heterogeneous aetiology primarily attributable to its symptom-based definitions. GERD genome-wide association studies (GWASs) have shown strong genetic overlaps with established risk factors such as obesity and depression. We hypothesised that the shared genetic architecture between GERD and these risk factors can be leveraged to (1) identify new GERD and Barrett’s oesophagus (BE) risk loci and (2) explore potentially heterogeneous pathways leading to GERD and oesophageal complications.DesignWe applied multitrait GWAS models combining GERD (78 707 cases; 288 734 controls) and genetically correlated traits including education attainment, depression and body mass index. We also used multitrait analysis to identify BE risk loci. Top hits were replicated in 23andMe (462 753 GERD cases, 24 099 BE cases, 1 484 025 controls). We additionally dissected the GERD loci into obesity-driven and depression-driven subgroups. These subgroups were investigated to determine how they relate to tissue-specific gene expression and to risk of serious oesophageal disease (BE and/or oesophageal adenocarcinoma, EA).ResultsWe identified 88 loci associated with GERD, with 59 replicating in 23andMe after multiple testing corrections. Our BE analysis identified seven novel loci. Additionally we showed that only the obesity-driven GERD loci (but not the depression-driven loci) were associated with genes enriched in oesophageal tissues and successfully predicted BE/EA.ConclusionOur multitrait model identified many novel risk loci for GERD and BE. We present strong evidence for a genetic underpinning of disease heterogeneity in GERD and show that GERD loci associated with depressive symptoms are not strong predictors of BE/EA relative to obesity-driven GERD loci.

scholarly journals Autocrine IGF2 programmes β-cell plasticity under conditions of increased metabolic demand

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ionel Sandovici ◽  
Constanze M. Hammerle ◽  
Sam Virtue ◽  
Yurena Vivas-Garcia ◽  
Adriana Izquierdo-Lahuerta ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Glucose Homeostasis ◽  
Association Studies ◽  
Susceptibility Gene ◽  
Chow Diet ◽  
Genome Wide Association Studies ◽  
Cell Plasticity ◽  
Β Cells ◽  
Β Cell ◽  
Pancreatic Β Cells

AbstractWhen exposed to nutrient excess and insulin resistance, pancreatic β-cells undergo adaptive changes in order to maintain glucose homeostasis. The role that growth control genes, highly expressed in early pancreas development, might exert in programming β-cell plasticity in later life is a poorly studied area. The imprinted Igf2 (insulin-like growth factor 2) gene is highly transcribed during early life and has been identified in recent genome-wide association studies as a type 2 diabetes susceptibility gene in humans. Hence, here we investigate the long-term phenotypic metabolic consequences of conditional Igf2 deletion in pancreatic β-cells (Igf2βKO) in mice. We show that autocrine actions of IGF2 are not critical for β-cell development, or for the early post-natal wave of β-cell remodelling. Additionally, adult Igf2βKO mice maintain glucose homeostasis when fed a chow diet. However, pregnant Igf2βKO females become hyperglycemic and hyperinsulinemic, and their conceptuses exhibit hyperinsulinemia and placentomegalia. Insulin resistance induced by congenital leptin deficiency also renders Igf2βKO females more hyperglycaemic compared to leptin-deficient controls. Upon high-fat diet feeding, Igf2βKO females are less susceptible to develop insulin resistance. Based on these findings, we conclude that in female mice, autocrine actions of β-cell IGF2 during early development determine their adaptive capacity in adult life.

scholarly journals Genetics and Epigenetics of Atrial Fibrillation

2020 ◽  
Vol 21 (16) ◽  
pp. 5717 ◽  
Author(s):  
Estefanía Lozano-Velasco ◽  
Diego Franco ◽  
Amelia Aranega ◽  
Houria Daimi
Keyword(s):  
Atrial Fibrillation ◽  
Regulatory Networks ◽  
Association Studies ◽  
Functional Characterization ◽  
The Elderly ◽  
Supraventricular Arrhythmia ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms

Atrial fibrillation (AF) is known to be the most common supraventricular arrhythmia affecting up to 1% of the general population. Its prevalence exponentially increases with age and could reach up to 8% in the elderly population. The management of AF is a complex issue that is addressed by extensive ongoing basic and clinical research. AF centers around different types of disturbances, including ion channel dysfunction, Ca2+-handling abnormalities, and structural remodeling. Genome-wide association studies (GWAS) have uncovered over 100 genetic loci associated with AF. Most of these loci point to ion channels, distinct cardiac-enriched transcription factors, as well as to other regulatory genes. Recently, the discovery of post-transcriptional regulatory mechanisms, involving non-coding RNAs (especially microRNAs), DNA methylation, and histone modification, has allowed to decipher how a normal heart develops and which modifications are involved in reshaping the processes leading to arrhythmias. This review aims to provide a current state of the field regarding the identification and functional characterization of AF-related epigenetic regulatory networks

A Variant Noncoding Region Regulates Prrx1 and Predisposes to Atrial Arrhythmias

2021 ◽  
Author(s):  
Fernanda M Bosada ◽  
Mathilde R Rivaud ◽  
Jae-Sun Uhm ◽  
Sander Verheule ◽  
Karel van Duijvenboden ◽  
...  
Keyword(s):  
Sodium Current ◽  
Association Studies ◽  
Noncoding Region ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Enhancer Activity ◽  
Atrial Cardiomyocytes ◽  
The Impact ◽  
Lineage Specific Gene

Rationale: Atrial Fibrillation (AF) is the most common cardiac arrhythmia diagnosed in clinical practice. Genome-wide association studies have identified AF-associated common variants across 100+ genomic loci, but the mechanism underlying the impact of these variant loci on AF susceptibility in vivo has remained largely undefined. One such variant region, highly associated with AF, is found at 1q24, close to PRRX1, encoding the Paired Related Homeobox 1 transcription factor. Objective: To identify the mechanistic link between the variant region at 1q24 and AF predisposition. Methods and Results: The mouse orthologue of the noncoding variant genomic region (R1A) at 1q24 was deleted using CRISPR genome editing. Among the genes sharing the topologically associated domain with the deleted R1A region (Kifap3, Prrx1, Fmo2, Prrc2c), only the broadly expressed gene Prrx1 was downregulated in mutants, and only in cardiomyocytes. Expression and epigenetic profiling revealed that a cardiomyocyte lineage-specific gene program (Mhrt, Myh6, Rbm20, Tnnt2, Ttn, Ckm) was upregulated in R1A-/- atrial cardiomyocytes, and that Mef2 binding motifs were significantly enriched at differentially accessible chromatin sites. Consistently, Prrx1 suppressed Mef2-activated enhancer activity in HL-1 cells. Mice heterozygous or homozygous for the R1A deletion were susceptible to atrial arrhythmia induction, had atrial conduction slowing and more irregular RR intervals. Isolated R1A-/- mouse left atrial cardiomyocytes showed lower action potential upstroke velocities and sodium current, as well as increased systolic and diastolic calcium concentrations compared to controls. Conclusions: The noncoding AF variant region at 1q24 modulates Prrx1 expression in cardiomyocytes. Cardiomyocyte-specific reduction of Prrx1 expression upon deletion of the noncoding region leads to a profound induction of a cardiac lineage-specific gene program and to propensity for AF. These data indicate that AF-associated variants in humans may exert AF predisposition through reduced PRRX1 expression in cardiomyocytes.

scholarly journals From blastocyst to gastrula: gene regulatory networks of embryonic stem cells and early mouse embryogenesis

2014 ◽  
Vol 369 (1657) ◽  
pp. 20130542 ◽  
Author(s):  
David-Emlyn Parfitt ◽  
Michael M. Shen
Keyword(s):  
Stem Cells ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Cell Lineage ◽  
Network Models ◽  
Cell Types ◽  
Mammalian Development ◽  
A Genome

To date, many regulatory genes and signalling events coordinating mammalian development from blastocyst to gastrulation stages have been identified by mutational analyses and reverse-genetic approaches, typically on a gene-by-gene basis. More recent studies have applied bioinformatic approaches to generate regulatory network models of gene interactions on a genome-wide scale. Such models have provided insights into the gene networks regulating pluripotency in embryonic and epiblast stem cells, as well as cell-lineage determination in vivo . Here, we review how regulatory networks constructed for different stem cell types relate to corresponding networks in vivo and provide insights into understanding the molecular regulation of the blastocyst–gastrula transition.

scholarly journals Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

2021 ◽  
Author(s):  
Abhishek Nag ◽  
Lawrence Middleton ◽  
Ryan S Dhindsa ◽  
Dimitrios Vitsios ◽  
Eleanor M Wigmore ◽  
...  
Keyword(s):  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Low Frequency ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Protein Coding ◽  
The Uk ◽  
Metabolic Biomarkers ◽  
Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

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