scholarly journals Understanding PITX2-dependent Atrial Fibrillation Mechanisms through Computational Models

2021 ◽  
Author(s):  
Jieyun Bai ◽  
Yaosheng Lu ◽  
Yijie Zhu ◽  
Huijin Wang ◽  
Dechun Yin ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Computational Models ◽  
Association Studies ◽  
Basic Research ◽  
Genome Wide Association Studies ◽  
Gene Coding ◽  
Patient Health ◽  
Quantitative Understanding ◽  
Whole Heart ◽  
And Function

Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including a genetic predisposition to develop AF. Genome-wide association studies have identified genetic variants associated with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene coding for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research on PITX2-dependent AF is not sufficient for understanding atrial functional proprieties. Linking PITX2 to ion channels, cells, tissues, atria and the whole heart, computational models are necessary for achieving a quantitative understanding of atrial structure and function in PITX2-dependent AF. Computational approaches are used to capture all that we know about PITX2-dependent AF and to develop improved therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

scholarly journals Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

2021 ◽  
Vol 22 (14) ◽  
pp. 7681
Author(s):  
Jieyun Bai ◽  
Yaosheng Lu ◽  
Yijie Zhu ◽  
Huijin Wang ◽  
Dechun Yin ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Computational Models ◽  
Association Studies ◽  
Basic Research ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Patient Health ◽  
Quantitative Understanding ◽  
Whole Heart ◽  
And Function

Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

scholarly journals Loss-of-Function Variants in the SYNPO2L Gene Are Associated With Atrial Fibrillation

2021 ◽  
Vol 8 ◽  
Author(s):  
Alexander Guldmann Clausen ◽  
Oliver Bundgaard Vad ◽  
Julie Husted Andersen ◽  
Morten Salling Olesen
Keyword(s):  
Atrial Fibrillation ◽  
Cardiac Myocyte ◽  
Association Studies ◽  
Structural Development ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genome Wide ◽  
Increased Risk ◽  
Rare Genetic Variants ◽  
And Function

Multiple genome-wide association studies (GWAS) have identified numerous loci associated with atrial fibrillation (AF). However, the genes driving these associations and how they contribute to the AF pathogenesis remains poorly understood. To identify genes likely to be driving the observed association, we searched the FinnGen study consisting of 12,859 AF cases and 73,341 controls for rare genetic variants predicted to cause loss-of-function. A specific splice site variant was found in the SYNPO2L gene, located in an AF associated locus on chromosome 10. This variant was associated with an increased risk of AF with a relatively high odds ratio of 3.5 (p = 9.9 × 10−8). SYNPO2L is an important gene involved in the structural development and function of the cardiac myocyte and our findings thus support the recent suggestions that AF can present as atrial cardiomyopathy.

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

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

scholarly journals Atrial fibrillation associated common risk variants in SYNE2 lead to lower expression of nesprin-2α1 and increased nuclear stiffness

2019 ◽  
Author(s):  
Nana Liu ◽  
Jeffrey Hsu ◽  
Gautam Mahajan ◽  
Han Sun ◽  
John Barnard ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Association Studies ◽  
Dominant Negative ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Gene Encoding ◽  
Enhancer Activity ◽  
Risk Alleles ◽  
Risk Variants ◽  
Lower Expression

ABSTRACTRationaleAtrial fibrillation (AF) genome-wide association studies (GWAS) identified significant associations for rs1152591 and linked variants in the SYNE2 gene encoding the nesprin-2 protein that connects the nuclear membrane with the cytoskeletonObjectiveDetermine the effects of the AF-associated rs1152591 and rs1152595, two linked intronic single nucleotide polymorphisms (SNPs), on SYNE2 expression and investigate the mechanisms for their association with AF.Methods and ResultsRNA sequencing of human left atrial appendage (LAA) tissues indicated that rs1152591 and rs1152595 were significantly associated with the expressions of SYNE2α1, a short mRNA isoform, without an effect on the expression of the full-length SYNE2 mRNA. SYNE2α1 mRNA uses an alternative transcription start site and encodes an N-terminal deleted 62 kDa nesprin-2α1 isoform, which can act as a dominant-negative on nuclear-cytoskeleton connectivity. Western blot and qPCR assays confirmed that AF risk alleles of both SNPs were associated with lower expression of nesprin-2α1 in human LAA tissues. Reporter gene transfections demonstrated that the risk vs. reference alleles of rs1152591 and rs1152595 had decreased enhancer activity. SYNE2 siRNA knockdown (KD) or nesprin-2α1 overexpression studies in human stem cell-derived induced cardiomyocytes (iCMs) resulted in ~12.5 % increases in the nuclear area compared to controls (p<0.001). Atomic force microscopy demonstrated that SYNE2 KD or nesprin-2α1 overexpression led to 57.5% or 33.2% decreases, respectively, in nuclear stiffness compared to controls (p< 0.0001).ConclusionsAF-associated SNPs rs1152591 and rs1152595 downregulate the expression of SYNE2α1, increasing nuclear-cytoskeletal connectivity and nuclear stiffness. The resulting increase in mechanical stress may play a role in the development of AF.

Role of Genetic Interactions in Lung Diseases Detection using Computational Approaches: A Review

2021 ◽  
Vol 01 ◽  
Author(s):  
S Priya ◽  
R Manavalan
Keyword(s):  
Lung Cancer ◽  
Lung Diseases ◽  
Computational Models ◽  
Association Studies ◽  
Genetic Interactions ◽  
Genome Wide Association Studies ◽  
Computational Approaches ◽  
Genome Wide ◽  
Human Disorders ◽  
Brca1 Brca2

: Genome-wide Association Studies (GWAS) give special insight into genetic differences and environmental influences that are part of different human disorders and provide prognostic help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key causes of lung-related diseases are genetic factors, environmental and social behaviors. The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated with lung diseases. The manual examination of the enormous genetic interactions is complicated to detect the lungs syndromes for diagnosis of acute respiratory. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2006 and 2020. In this critical review, various computational approaches are extensively discussed in detecting respondent Epistasis effects for various lung diseases such as Asthma, Tuberculosis, lung cancer, and Nicotine drug dependence. The analysis shows that different computational models identified candidate genes such as CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational approaches' strengths and limitations are described. The issues behind the computational methods while identifying the lung diseases through epistasis effects and the parameters used by various researchers for their evaluation are presented.

Abstract 18374: Targeted Sequencing and Massively Parallel Reporter Assay Identify the Functional Variation Underlying the 4q25 Locus for Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Nathan R Tucker ◽  
Jiangchuan Ye ◽  
Honghuang Lin ◽  
Michael A McLellan ◽  
Emelia J Benjamin ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Association Studies ◽  
Targeted Sequencing ◽  
Massively Parallel ◽  
Genome Wide Association Studies ◽  
Sequencing Analysis ◽  
Reporter Assay ◽  
Enhancer Activity ◽  
Functional Variants ◽  
Massively Parallel Reporter Assay

Introduction: Genome-wide association studies have identified 14 independent loci for atrial fibrillation (AF). The 4q25 locus upstream of the left-right asymmetry gene PITX2 is, by far, the strongest association signal for AF. However, as with most GWAS loci, the functional variants are noncoding, presumed to be regulatory, and remain unknown. We therefore sought to rapidly identify the functional variants at an AF locus by combining high throughput sequencing and massively parallel reporter assays. Methods and Results: We sequenced a ~750kb region encompassing the PITX2 locus in 462 individuals with early-onset AF from the MGH AF Study and 464 referents from the Framingham Heart Study. The SNP most significantly associated with AF in our sequenced sample was rs2129983, which is 140kb from PITX2 (OR=2.43, P =8.9X10 -16 ). rs2129983 is approximately 1.7kb from the most significantly associated SNP in a prior AF GWAS, rs6817105 (r 2 =0.52). From the targeted sequencing analysis, we identified 262 SNVs with a MAF >0.5% within a genomic region bounded by SNPs with an r2 greater than 0.4 with the top variant. To identify functional variants, we then utilized a massively parallel reporter assay (MPRA) in order to measure enhancer activity at each SNP across the entire AF locus. In both HL-1 and C2C12 myoblasts, MPRA identified many distinct SNP regions with differential enhancer activity. Using AF-association status as a standard, we were able to identify a series of variants that have both differential activity in either cell line tested and also a high level of association (rs17042076, rs4469143). Mechanistically, these functional SNPs are predicted to alter transcription factor binding. Conclusions: We have comprehensively identified the AF-associated variation at 4q25 and determined which of these variants are functional through differential enhancer activity. Here, in addition to identifying the causative variation for AF at 4q25, we provide a generalizable pathway for translating this work to other loci, a method that could expedite the identification of causative genetic variants at other disease loci.

scholarly journals Genetics of Atrial Fibrillation in 2020

2020 ◽  
Vol 127 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Carolina Roselli ◽  
Michiel Rienstra ◽  
Patrick T. Ellinor
Keyword(s):  
Atrial Fibrillation ◽  
Complex Disease ◽  
Association Studies ◽  
Heart Rhythm ◽  
Genome Wide Association Studies ◽  
Future Directions ◽  
Genetics Research ◽  
Genome Wide ◽  
Increased Risk ◽  
Rhythm Disorder

Atrial fibrillation is a common heart rhythm disorder that leads to an increased risk for stroke and heart failure. Atrial fibrillation is a complex disease with both environmental and genetic risk factors that contribute to the arrhythmia. Over the last decade, rapid progress has been made in identifying the genetic basis for this common condition. In this review, we provide an overview of the primary types of genetic analyses performed for atrial fibrillation, including linkage studies, genome-wide association studies, and studies of rare coding variation. With these results in mind, we aim to highlighting the existing knowledge gaps and future directions for atrial fibrillation genetics research.

GWAS contribution to atrial fibrillation and atrial fibrillation-related stroke: pathophysiological implications

2019 ◽  
Vol 20 (10) ◽  
pp. 765-780 ◽  
Author(s):  
Diana Cruz ◽  
Ricardo Pinto ◽  
Margarida Freitas-Silva ◽  
José Pedro Nunes ◽  
Rui Medeiros
Keyword(s):  
Atrial Fibrillation ◽  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
Cardioembolic Stroke ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Genome Wide Significance

Atrial fibrillation (AF) and stroke are included in a group of complex traits that have been approached regarding of their study by susceptibility genetic determinants. Since 2007, several genome-wide association studies (GWAS) aiming to identify genetic variants modulating AF risk have been conducted. Thus, 11 GWAS have identified 26 SNPs (p < 5 × 10-2), of which 19 reached genome-wide significance (p < 5 × 10-8). From those variants, seven were also associated with cardioembolic stroke and three reached genome-wide significance in stroke GWAS. These associations may shed a light on putative shared etiologic mechanisms between AF and cardioembolic stroke. Additionally, some of these identified variants have been incorporated in genetic risk scores in order to elucidate new approaches of stroke prediction, prevention and treatment.

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