Gene-gene Interaction Analyses for Atrial Fibrillation

Decoding the PITX2-controlled genetic network in atrial fibrillation

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia and a major risk factor for stroke, often arises through ectopic electrical impulses derived from the pulmonary veins (PVs). Sequence variants in enhancers controlling expression of the transcription factor PITX2, which is expressed in the cardiomyocytes (CMs) of the PV and left atrium (LA), have been implicated in AF predisposition. Single nuclei multiomic profiling of RNA and analysis of chromatin accessibility combined with spectral clustering uncovered distinct PV- and LA-enriched CM cell states. Pitx2-mutant PV and LA CMs exhibited gene expression changes consistent with cardiac dysfunction through cell type-distinct, PITX2-directed, cis-regulatory grammars controlling target gene expression. The perturbed network targets in each CM were enriched in distinct human AF predisposition genes, suggesting combinatorial risk for AF genesis. Our data further reveal that PV and LA Pitx2-mutant CMs signal to endothelial and endocardial cells through BMP10 signaling with pathogenic potential. This work provides a multiomic framework for interrogating the basis of AF predisposition in the PVs of humans.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

Among the myriad of statistical methods that identify gene–gene interactions in the realm of qualitative genome-wide association studies, gene-based interactions are not only powerful statistically, but also they are interpretable biologically. However, they have limited statistical detection by making assumptions on the association between traits and single nucleotide polymorphisms. Thus, a gene-based method (GGInt-XGBoost) originated from XGBoost is proposed in this article. Assuming that log odds ratio of disease traits satisfies the additive relationship if the pair of genes had no interactions, the difference in error between the XGBoost model with and without additive constraint could indicate gene–gene interaction; we then used a permutation-based statistical test to assess this difference and to provide a statistical p-value to represent the significance of the interaction. Experimental results on both simulation and real data showed that our approach had superior performance than previous experiments to detect gene–gene interactions.

Genetics of Atrial Fibrillation in 2020: GWAS, Genome Sequencing, Polygenic Risk, and Beyond

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.

Inferior Colliculus Transcriptome After Status Epilepticus in the Genetically Audiogenic Seizure-Prone Hamster GASH/Sal

The Genetic Audiogenic Seizure Hamster from Salamanca (GASH/Sal), an animal model of reflex epilepsy, exhibits generalized tonic–clonic seizures in response to loud sound with the epileptogenic focus localized in the inferior colliculus (IC). Ictal events in seizure-prone strains cause gene deregulation in the epileptogenic focus, which can provide insights into the epileptogenic mechanisms. Thus, the present study aimed to determine the expression profile of key genes in the IC of the GASH/Sal after the status epilepticus. For such purpose, we used RNA-Seq to perform a comparative study between the IC transcriptome of GASH/Sal and that of control hamsters both subjected to loud sound stimulation. After filtering for normalization and gene selection, a total of 36 genes were declared differentially expressed from the RNA-seq analysis in the IC. A set of differentially expressed genes were validated by RT-qPCR showing significant differentially expression between GASH/Sal hamsters and Syrian control hamsters. The confirmed differentially expressed genes were classified on ontological categories associated with epileptogenic events similar to those produced by generalized tonic seizures in humans. Subsequently, based on the result of metabolomics, we found the interleukin-4 and 13-signaling, and nucleoside transport as presumably altered routes in the GASH/Sal model. This research suggests that seizures in GASH/Sal hamsters are generated by multiple molecular substrates, which activate biological processes, molecular processes, cellular components and metabolic pathways associated with epileptogenic events similar to those produced by tonic seizures in humans. Therefore, our study supports the use of the GASH/Sal as a valuable animal model for epilepsy research, toward establishing correlations with human epilepsy and searching new biomarkers of epileptogenesis.

The Genetic Puzzle of Familial Atrial Fibrillation

Atrial fibrillation (AF) is the most common clinical tachyarrhythmia. In Europe, AF is expected to reach a prevalence of 18 million by 2060. This estimate will increase hospitalization for AF to 4 million and 120 million outpatient visits. Besides being an independent risk factor for mortality, AF is also associated with an increased risk of morbidities. Although there are many well-defined risk factors for developing AF, no identifiable risk factors or cardiac pathology is seen in up to 30% of the cases. The heritability of AF has been investigated in depth since the first report of familial atrial fibrillation (FAF) in 1936. Despite the limited value of animal models, the advances in molecular genetics enabled identification of many common and rare variants related to FAF. The importance of AF heritability originates from the high prevalence of lone AF and the lack of clear understanding of the underlying pathophysiology. A better understanding of FAF will facilitate early identification of people at high risk of developing FAF and subsequent development of more effective management options. In this review, we reviewed FAF epidemiological studies, identified common and rare variants, and discussed their clinical implications and contributions to developing new personalized therapeutic strategies.

Atrial Fibrillation Genetics Update: Toward Clinical Implementation

Atrial fibrillation (AF) is the most common heart rhythm disorder worldwide and may have serious cardiovascular health consequences. AF is associated with increased risk of stroke, dementia, heart failure, and death. There are several known robust, clinical risk predictors for AF, such as male sex, increasing age, and hypertension; however, during the last couple of decades, a substantive genetic component has also been established. Over the last 10 years, the discovery of novel AF-related genetic variants has accelerated, increasing our understanding of mechanisms behind AF. Current studies are focusing on mapping the polygenic structure of AF, improving risk prediction, therapeutic development, and patient-specific management. Nevertheless, it is still difficult for clinicians to interpret the role of genetics in AF prediction and management. Here, we provide an overview of relevant topics within the genetics of AF and attempt to provide some guidance on how to interpret genetic advances and their implementation into clinical decision-making.

Gene-Based Nonparametric Testing of Interactions Using Distance Correlation Coefficient in Case-Control Association Studies

Among the various statistical methods for identifying gene⁻gene interactions in qualitative genome-wide association studies (GWAS), gene-based methods have recently grown in popularity because they confer advantages in both statistical power and biological interpretability. However, most of these methods make strong assumptions about the form of the relationship between traits and single-nucleotide polymorphisms, which result in limited statistical power. In this paper, we propose a gene-based method based on the distance correlation coefficient called gene-based gene-gene interaction via distance correlation coefficient (GBDcor). The distance correlation (dCor) is a measurement of the dependency between two random vectors with arbitrary, and not necessarily equal, dimensions. We used the difference in dCor in case and control datasets as an indicator of gene⁻gene interaction, which was based on the assumption that the joint distribution of two genes in case subjects and in control subjects should not be significantly different if the two genes do not interact. We designed a permutation-based statistical test to evaluate the difference between dCor in cases and controls for a pair of genes, and we provided the p-value for the statistic to represent the significance of the interaction between the two genes. In experiments with both simulated and real-world data, our method outperformed previous approaches in detecting interactions accurately.

Effect of sex hormone-binding globulin polymorphisms on the outcome of in vitro fertilization-embryo transfer for polycystic ovary syndrome patients: A case-control study

Polycystic ovary syndrome (PCOS), known as a common endocrine disorder among females, plagues many PCOS patients. The current study aimed to explore the correlations of sex hormone-binding globulin (SHBG) polymorphisms with the outcome of in vitro fertilization-embryo transfer (IVF-ET) in PCOS patients. PCOS patients who underwent IVF-ET and patients with non-PCOS-related infertility were selected in the study. Correlations of SHBG rs6259 and rs727428 with the risk factors in PCOS were analyzed, followed by the evaluation of the effect of SHBG polymorphisms on the outcome of IVF-ET in PCOS patients. At last, unconditional logistic regression analysis was performed to study the risk factors for IVF-ET treatment outcome. Compared with SHBG rs6259 GG carriers, the incidence of PCOS was found to be elevated in SHBG rs6259 GA+AA carriers which indicated that the A allele was a risk factor for PCOS. Compared with SHBG rs6259 TT carriers, the number of retrieved oocytes and embryo as well as the fertility rate in SHBG rs6259 GA+AA carriers was found to be decreased, while the abortion rate, incidence of ovarian hyperstimulation syndrome, transplant rejection rate, estradiol, and testosterone in serum, as well as testosterone in follicular fluid were elevated. The luteal hormone, serum testosterone, and progesterone and GA+AA genotype of rs6259 were the risk factors for IVF-ET treatment outcome. Taken together, the study showed that SHBG rs6259 polymorphisms might be correlated with the risk of PCOS and the outcome of IVF-ET treatment.

Genetics of atrial fibrillation: an update

PURPOSE OF REVIEW

Atrial fibrillation is a common cardiac arrhythmia with a high morbidity and mortality affecting 34 million worldwide. Current therapies are inadequate and often fail to directly address molecular mechanisms of the disease. In this review, we will provide an overview of recent advances in our understanding of the genetic underpinnings of atrial fibrillation.

RECENT FINDINGS

Large-scale genetic association studies have more than doubled the number of genetic loci associated with atrial fibrillation during the last year. Studies examining how genes at or near these loci can affect the pathogenesis of atrial fibrillation are ongoing in cellular, animal, and computational models. In addition, several recent clinical studies have also demonstrated that variants at these loci can aid in risk stratification of patients.

SUMMARY

There are now over 30 genetic loci associated with atrial fibrillation. A better understanding of how these loci relate to disease pathogenesis may provide insight into novel therapeutic targets and ultimately lead to improved clinical care.

Update about atrial fibrillation genetics

PURPOSE OF REVIEW

Atrial fibrillation is an important cause of morbidity in the aging population. The mechanisms responsible for the triggering and maintenance of the chaotic atrial rhythm are still poorly understood. In this review, we will focus on the genetic aspects of atrial fibrillation, to understand causality, with special emphasis on recent studies published in the field.

RECENT FINDINGS

Diseases such as hypertension, valvular heart disease, and heart failure may induce atrial fibrillation, which increases the risk of stroke and sudden cardiac death. Clinical studies published in these last two decades have provided evidence that genetics play a key role in atrial fibrillation. Thus, a family history of the disease has been identified in up to 30% of clinically diagnosed patients. In those genotyped families, most carry rare genetic variants in genes associated with ionic channels, calcium handling protein, or predisposing to fibrosis, conduction system disease, and inflammatory processes.

SUMMARY

Currently, atrial fibrillation is the most common sustained arrhythmia in clinical practice. The pathophysiological mechanisms of atrial fibrillation are complex. A better understanding of the molecular basis will help improve both current risk stratification and clinical management.