Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility

Abstract

Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

“…RF has demonstrated practical usage within transcriptomics [25]. Optimizing RF with GridSearchCV ( Figure 4A ), using dataset-standard parameters, the decision tree classifier made the most accurate predictions.…”

“…Previously, we have created AI/ML models to investigate and identify genes associated with heart failure (HF), atrial fibrillation (AF), and other CVDs and successfully predict these diseases with high accuracy [24]. However, one of the major limitations of our and most of the other published disease specific research using AI/ML and bioinformatics approaches is the focus on genes known to be associated with disease [2, 24, 25]. In this study, we propose a new AI/ML model that adapts an innovative nexus of algorithms to predict CVDs using critical transcriptomic biomarkers determined using our comprehensive statistical analysis ( Figure 1 ).…”

Understanding genomic medicine for thoracic aortic disease through the lens of induced pluripotent stem cells

“…Functional genomics also allows for modelling stroke and other cerebral small vessel diseases where predicted risk loci are validated using iPSC-derived vascular smooth muscle cells and testing for classes of drugs such as HDAC inhibitors and MMP inhibitors ( 182 – 184 ). Recent studies have highlighted the ability to employ iPSCs-derived cells, genomics, and machine learning-based analysis for predicting risk of occurrences of conditions like arrhythmia and heart failure susceptibility in cardiomyocytes ( 185 , 186 ). Risk loci of coronary artery disease (CAD) ( 125 ) and schizophrenia severity ( 187 ) were also determined using iPSC-derived VSMCs and neurons respectively.…”

Impact of rs1805127 and rs55742440 Variants on Atrial Remodeling in Hypertrophic Cardiomyopathy Patients with Atrial Fibrillation: A Romanian Cohort Study

“…The between sodium channel variants and AFib has been extensively investigated, ndings revealing various alleles that are linked either to an increased susceptibility to AFib [47][48][49] or to potential protection against it [50].…”