scholarly journals Epigenetic and Transcriptional Networks Underlying Atrial Fibrillation

2020 ◽  
Vol 127 (1) ◽  
pp. 34-50 ◽  
Author(s):  
Antoinette F. van Ouwerkerk ◽  
Amelia W. Hall ◽  
Zachary A. Kadow ◽  
Sonja Lazarevic ◽  
Jasmeet S. Reyat ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Transcription Factors ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association ◽  
Transcriptional Networks ◽  
Genome Wide Association Studies ◽  
Genome Wide

Genome-wide association studies have uncovered over a 100 genetic loci associated with atrial fibrillation (AF), the most common arrhythmia. Many of the top AF-associated loci harbor key cardiac transcription factors, including PITX2, TBX5, PRRX1, and ZFHX3. Moreover, the vast majority of the AF-associated variants lie within noncoding regions of the genome where causal variants affect gene expression by altering the activity of transcription factors and the epigenetic state of chromatin. In this review, we discuss a transcriptional regulatory network model for AF defined by effector genes in Genome-wide association studies loci. We describe the current state of the field regarding the identification and function of AF-relevant gene regulatory networks, including variant regulatory elements, dose-sensitive transcription factor functionality, target genes, and epigenetic states. We illustrate how altered transcriptional networks may impact cardiomyocyte function and ionic currents that impact AF risk. Last, we identify the need for improved tools to identify and functionally test transcriptional components to define the links between genetic variation, epigenetic gene regulation, and atrial function.

scholarly journals Post-GWAS era in genetics of schizophrenia

2019 ◽  
pp. 6-7
Author(s):  
V. E. Golimbet ◽  
A. K. Golov ◽  
N. V. Kondratyev
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Regulatory Elements ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Master Regulators ◽  
Genome Wide

Genome-wide association studies (GWASs) discovered multiple genetic variants associated with schizophrenia. Te next step (post-GWAS analysis) is aimed at identifying the causal genetic variants and biological mechanisms underlying the associations with disease risk. Te following strategies are considered: the study of transcriptional regulation in neuronal human cells and the use of epigenomic information for searching for regulatory elements involved in the pathogenesis of schizophrenia. Te frst strategy includes identifcation of neuronal enhancers, mapping of potential target genes and functional confrmation of enhancer-promoter interactions. Te second approach is focused on the identifcation of transcriptional factors, which appear to be master regulators of expression.

scholarly journals Addressing the Missing Heritability Problem With the Help of Regulatory Features

2019 ◽  
Vol 15 ◽  
pp. 117693431986086
Author(s):  
Shan-Shan Dong ◽  
Yan Guo ◽  
Tie-Lin Yang
Keyword(s):  
Target Genes ◽  
Association Studies ◽  
Complex Diseases ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Susceptibility Loci ◽  
Missing Heritability ◽  
Genome Wide ◽  
Missing Heritability Problem

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

scholarly journals Identification of atrial fibrillation associated genes and functional non-coding variants

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Antoinette F. van Ouwerkerk ◽  
Fernanda M. Bosada ◽  
Karel van Duijvenboden ◽  
Matthew C. Hill ◽  
Lindsey E. Montefiori ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Genetic Variants ◽  
Target Gene ◽  
Target Genes ◽  
Association Studies ◽  
Regulatory Region ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Cx43 Expression

Abstract Disease-associated genetic variants that lie in non-coding regions found by genome-wide association studies are thought to alter the functionality of transcription regulatory elements and target gene expression. To uncover causal genetic variants, variant regulatory elements and their target genes, here we cross-reference human transcriptomic, epigenomic and chromatin conformation datasets. Of 104 genetic variant regions associated with atrial fibrillation candidate target genes are prioritized. We optimize EMERGE enhancer prediction and use accessible chromatin profiles of human atrial cardiomyocytes to more accurately predict cardiac regulatory elements and identify hundreds of sub-threshold variants that co-localize with regulatory elements. Removal of mouse homologues of atrial fibrillation-associated regions in vivo uncovers a distal regulatory region involved in Gja1 (Cx43) expression. Our analyses provide a shortlist of genes likely affected by atrial fibrillation-associated variants and provide variant regulatory elements in each region that link genetic variation and target gene regulation, helping to focus future investigations.

scholarly journals Challenges and progress in interpretation of non-coding genetic variants associated with human disease

2017 ◽  
Vol 242 (13) ◽  
pp. 1325-1334 ◽  
Author(s):  
Yizhou Zhu ◽  
Cagdas Tazearslan ◽  
Yousin Suh
Keyword(s):  
Molecular Mechanisms ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Functional Interpretation ◽  
Genome Wide ◽  
Coding Variants

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

scholarly journals Cross-cancer genome-wide association study of endometrial cancer and epithelial ovarian cancer identifies genetic risk regions associated with risk of both cancers

2020 ◽  
Author(s):  
Dylan M. Glubb ◽  
Deborah J. Thompson ◽  
Katja K.H. Aben ◽  
Ahmad Alsulimani ◽  
Frederic Amant ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Endometrial Cancer ◽  
Epithelial Ovarian Cancer ◽  
Genetic Correlation ◽  
Target Genes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Molecular Features ◽  
Genome Wide

AbstractAccumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10−7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.

scholarly journals Epigenetic Analyses of Human Left Atrial Tissue Identifies Gene Networks Underlying Atrial Fibrillation

2020 ◽  
Vol 13 (6) ◽  
Author(s):  
Amelia Weber Hall ◽  
Mark Chaffin ◽  
Carolina Roselli ◽  
Honghuang Lin ◽  
Steven A. Lubitz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Molecular Mechanisms ◽  
Regulatory Elements ◽  
Genome Wide Association ◽  
Cardiac Conduction ◽  
Chromatin Interaction ◽  
Genome Wide Association Studies ◽  
Chromatin States ◽  
Genome Wide

Background: Atrial fibrillation (AF) often arises from structural abnormalities in the left atria (LA). Annotation of the noncoding genome in human LA is limited, as are effects on gene expression and chromatin architecture. Many AF-associated genetic variants reside in noncoding regions; this knowledge gap impairs efforts to understand the molecular mechanisms of AF and cardiac conduction phenotypes. Methods: We generated a model of the LA noncoding genome by profiling 7 histone post-translational modifications (active: H3K4me3, H3K4me2, H3K4me1, H3K27ac, H3K36me3; repressive: H3K27me3, H3K9me3), CTCF binding, and gene expression in samples from 5 individuals without structural heart disease or AF. We used MACS2 to identify peak regions ( P <0.01), applied a Markov model to classify regulatory elements, and annotated this model with matched gene expression data. We intersected chromatin states with expression quantitative trait locus, DNA methylation, and HiC chromatin interaction data from LA and left ventricle. Finally, we integrated genome-wide association data for AF and electrocardiographic traits to link disease-related variants to genes. Results: Our model identified 21 epigenetic states, encompassing regulatory motifs, such as promoters, enhancers, and repressed regions. Genes were regulated by proximal chromatin states; repressive states were associated with a significant reduction in gene expression ( P <2×10 −16 ). Chromatin states were differentially methylated, promoters were less methylated than repressed regions ( P <2×10 −16 ). We identified over 15 000 LA-specific enhancers, defined by homeobox family motifs, and annotated several cardiovascular disease susceptibility loci. Intersecting AF and PR genome-wide association studies loci with long-range chromatin conformation data identified a gene interaction network dominated by NKX2-5 , TBX3 , ZFHX3 , and SYNPO2L . Conclusions: Profiling the noncoding genome provides new insights into the gene expression and chromatin regulation in human LA tissue. These findings enabled identification of a gene network underlying AF; our experimental and analytic approach can be extended to identify molecular mechanisms for other cardiac diseases and traits.

scholarly journals Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Sonia Shah ◽  
◽  
Albert Henry ◽  
Carolina Roselli ◽  
Honghuang Lin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Cardiac Development ◽  
Association Studies ◽  
Meta Analysis ◽  
Left Ventricular ◽  
Genome Wide Association ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractHeart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

scholarly journals Genome-wide association study implicates novel loci and reveals candidate effector genes for longitudinal pediatric bone accrual through variant-to-gene mapping

2020 ◽  
Author(s):  
Diana L. Cousminer ◽  
Yadav Wagley ◽  
James A. Pippin ◽  
Ahmed Elhakeem ◽  
Gregory P. Way ◽  
...  
Keyword(s):  
Gene Mapping ◽  
Cell Fate ◽  
Target Genes ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Promoters ◽  
Skeletal Health ◽  
Effector Genes ◽  
Genome Wide

Introductory paragraphBone accrual impacts lifelong skeletal health, but genetic discovery has been hampered by cross-sectional study designs and uncertainty about target effector genes. Here, we captured this dynamic phenotype by modeling longitudinal bone accrual across 11,000 bone scans followed by genome-wide association studies (GWAS). We revealed 40 loci (35 novel), half residing in topological associated domains harboring known bone genes. Variant-to-gene mapping identified contacts between GWAS loci and nearby gene promoters, and siRNA knockdown of gene expression clarified the putative effector gene at three specific loci in two osteoblast cell models. The resulting target genes highlight the cell fate decision between osteogenic and adipogenic lineages as important in normal bone accrual.

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