Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

Key Words Collapse

MESH Headings Collapse

Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

Collapse

Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

Collapse

Integrated Analysis of Genomic and Genome-Wide Association Studies Identified Candidate Genes for Nutrigenetic Studies in Flavonoids and Vascular Health: Path to Precision Nutrition for (Poly)phenols

Flavonoids exert vasculoprotective effects in humans, but interindividual variability in their action has also been reported. This study aims to identify genes that are associated with vascular health effects of flavonoids and whose polymorphisms could explain interindividual variability in response to their intake. Applying the predetermined literature search criteria, we identified five human intervention studies reporting positive effects of flavonoids on vascular function together with global genomic changes analyzed using microarray methods. Genes involved in vascular dysfunction were identified from genome-wide association studies (GWAS). By extracting data from the eligible human intervention studies, we obtained 5807 differentially expressed genes (DEGs). The number of identified upstream regulators (URs) varied across the studies, from 227 to 1407. The search of the GWAS Catalog revealed 493 genes associated with vascular dysfunction. An integrative analysis of transcriptomic data with GWAS genes identified 106 candidate DEGs and 42 candidate URs, while subsequent functional analyses and a search of the literature identified 20 top priority candidate genes: ALDH2, APOE, CAPZA1, CYP11B2, GNA13, IL6, IRF5, LDLR, LPL, LSP1, MKNK1, MMP3, MTHFR, MYO6, NCR3, PPARG, SARM1, TCF20, TCF7L2, and TNF. In conclusion, this integrated analysis identifies important genes to design future nutrigenetic studies for development of precision nutrition for polyphenols.

Identifying BMI-associated genes via a genome-wide multi-omics integrative approach using summary data

OBJECTIVE

This study aims to identify BMI-associated genes by integrating aggregated summary information from different omics data.

METHODS

We conducted a meta-analysis to leverage information from a genome-wide association study (n = 339 224), a transcriptome-wide association study (n = 5619), and an epigenome-wide association study (n = 3743). We prioritized the significant genes with a machine learning-based method, netWAS, which borrows information from adipose tissue-specific interaction networks. We also used the brain-specific network in netWAS to investigate genes potentially involved in brain-adipose interaction.

RESULTS

We identified 195 genes that were significantly associated with BMI through meta-analysis. The netWAS analysis narrowed down the list to 21 genes in adipose tissue. Among these 21 genes, six genes, including FUS, STX4, CCNT2, FUBP1, NDUFS3, and RAPSN, were not reported to be BMI-associated in PubMed or GWAS Catalog. We also identified 11 genes that were significantly associated with BMI in both adipose and whole brain tissues.

CONCLUSION

This study integrated three types of omics data and identified a group of genes that have not previously been reported to be associated with BMI. This strategy could provide new insights for future studies to identify molecular mechanisms contributing to BMI regulation.

Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that affect cardiovascular risks in the LLFS population, we developed a multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from rare-variant analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 64 significant genes after Bonferroni correction (p ≤ 2.8×10-7), 29 of which replicated in the Framingham Heart Study (FHS) cohort. Notably, 20 of the 29 replicated genes do not have a previously known trait-associated variant in the GWAS Catalog within 50 kb. Thirteen modules in Protein-Protein Interaction (PPI) networks are significantly enriched in genes with low meta-analysis p-values for at least one trait, three of which are replicated in the FHS cohort. The functional annotation of genes in these modules showed a significant over-representation of trait-related biological processes including sterol transport, protein-lipid complex remodeling, and immune response regulation. Among major findings, our results suggest a role of triglyceride-associated and mast-cell functional genes FCER1A, MS4A2, GATA2, HDC, and HRH4 in atherosclerosis risks. Our findings also suggest that lower expression of ATG2A, a gene we found to be associated with BMI, may be both a cause and consequence of obesity. Finally, our results suggest that ENPP3 may play an intermediary role in triglyceride-induced inflammation. Our pipeline is freely available and implemented in the Nextflow workflow language, making it easily runnable on any compute platform (https://nf-co.re/omicsgenetraitassociation).

Atrial fibrillation: pathophysiology, genetic and epigenetic mechanisms

Atrial fibrillation (AF) is the most common supraventricular arrhythmia affecting up to 1% of the general population. Its prevalence dramatically increases with age and could reach up to ∼10% in the elderly. The management of AF is a complex issue that is object of extensive ongoing basic and clinical research, it depends on its genetic and epigenetic causes, and it varies considerably geographically and also according to the ethnicity. Mechanistically, over the last decade, Genome Wide Association Studies have uncovered over 100 genetic loci associated with AF, and have shown that European ancestry is associated with elevated risk of AF. These AF-associated loci revolve around different types of disturbances, including inflammation, electrical abnormalities, and structural remodeling. Moreover, the discovery of epigenetic regulatory mechanisms, involving non-coding RNAs, DNA methylation and histone modification, has allowed unravelling what modifications reshape the processes leading to arrhythmias. Our review provides a current state of the field regarding the identification and functional characterization of AF-related genetic and epigenetic regulatory networks, including ethnic differences. We discuss clear and emerging connections between genetic regulation and pathophysiological mechanisms of AF.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

Whole Blood-based Transcriptional Risk Score for Nonobese Type 2 Diabetes Predicts Dynamic Changes in Glucose Metabolism

CONTEXT

The performance of peripheral blood transcriptional markers in evaluating risk of type 2 diabetes (T2D) with normal body mass index (BMI) is unknown.

OBJECTIVE

We developed a whole blood-based transcriptional risk score (wb-TRS) for nonobese T2D and assessed its contributions on disease risk and dynamic changes in glucose metabolism.

METHODS

Using a community-based cohort with blood transcriptome data, we developed the wb-TRS in 1105 participants aged ≥40 years who maintained a normal BMI for up to 10 years, and we validated the wb-TRS in an external dataset. Potential biological significance was explored.

RESULTS

The wb-TRS included 144 gene transcripts. Compared to the lowest tertile, wb-TRS in tertile 3 was associated with 8.91-fold (95% CI, 3.53-22.5) higher risk and each 1-unit increment was associated with 2.63-fold (95% CI, 1.87-3.68) higher risk of nonobese T2D. Furthermore, baseline wb-TRS significantly associated with dynamic changes in average, daytime, nighttime, and 24-hour glucose, HbA1c values, and area under the curve of glucose measured by continuous glucose monitoring over 6 months of intervention. The wb-TRS improved the prediction performance for nonobese T2D, combined with fasting glucose, triglycerides, and demographic and anthropometric parameters. Multi-contrast gene set enrichment (Mitch) analysis implicated oxidative phosphorylation, mTORC1 signaling, and cholesterol metabolism involved in nonobese T2D pathogenesis.

CONCLUSION

A whole blood-based nonobese T2D-associated transcriptional risk score was validated to predict dynamic changes in glucose metabolism. These findings suggested several biological pathways involved in the pathogenesis of nonobese T2D.

Identification of a Novel Target Implicated in Chronic Obstructive Sleep Apnea-Related Atrial Fibrillation by Integrative Analysis of Transcriptome and Proteome

Objective

This study aimed to identify a newly identified target involved in atrial fibrillation (AF) linked to chronic obstructive sleep apnea (COSA) through an integrative analysis of transcriptome and proteome.

Methods

Fifteen beagle canines were randomly assigned to three groups: control (CON), obstructive sleep apnea (OSA), and OSA with superior left ganglionated plexi ablation (OSA+GP). A COSA model was established by intermittently obstructing the endotracheal cannula during exhalation for 12 weeks. Left parasternal thoracotomy through the fourth intercostal space allowed for superior left ganglionated plexi (SLGP) ablation. In vivo open-chest electrophysiological programmed stimulation was performed to assess AF inducibility. Histological, transcriptomic, and proteomic analyses were conducted on atrial samples.

Results

After 12 weeks, the OSA group exhibited increased AF inducibility and longer AF durations compared to the CON group. Integrated transcriptomic and proteomic analyses identified 2422 differentially expressed genes (DEGs) and 1194 differentially expressed proteins (DEPs) between OSA and CON groups, as well as between OSA+GP and OSA groups (1850 DEGs and 1418 DEPs). The analysis revealed that differentially regulated DEGs were primarily enriched in mitochondrial biological processes in the CON-vs.-OSA and OSA-vs.-GP comparisons. Notably, the key regulatory molecule GSTZ1 was activated in OSA and inhibited by GP ablation.

Conclusion

These findings suggest that GSTZ1 may play a pivotal role in mitochondrial damage, triggering AF substrate formation, and increasing susceptibility to AF in the context of COSA.

Beyond the Rhythm: In Silico Identification of Key Genes and Therapeutic Targets in Atrial Fibrillation

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia worldwide and is characterized by a high risk of thromboembolism, ischemic stroke, and fatality. The precise molecular mechanisms of AF pathogenesis remain unclear. The purpose of this study was to use bioinformatics tools to identify novel key genes in AF, provide deeper insights into the molecular pathogenesis of AF, and uncover potential therapeutic targets. Four publicly available raw RNA-Seq datasets obtained through the ENA Browser, as well as proteomic analysis results, both derived from atrial tissues, were used in this analysis. Differential gene expression analysis was performed and cross-validated with proteomics results to identify common genes/proteins between them. A functional enrichment pathway analysis was performed. Cross-validation analysis revealed five differentially expressed genes, namely FGL2, IGFBP5, NNMT, PLA2G2A, and TNC, in patients with AF compared with those with sinus rhythm (SR). These genes play crucial roles in various cardiovascular functions and may be part of the molecular signature of AF. Furthermore, functional enrichment analysis revealed several pathways related to the extracellular matrix, inflammation, and structural remodeling. This study highlighted five key genes that constitute promising candidates for further experimental exploration as biomarkers as well as therapeutic targets for AF.

Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network

Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.

Genome and atrial fibrillation

Atrial fibrillation (AF), the most common type of arrhythmia, can cause several adverse effects, such as stroke, heart failure, and cognitive dysfunction, also in addition to reducing quality of life and increasing mortality. Evidence suggests that AF is caused by a combination of genetic and clinical predispositions. In line with this, genetic studies on AF have progressed significantly through linkage studies, genome-wide association studies, use of polygenic risk scores, and studies on rare coding variations, gradually elucidating the relationship between genes and the pathogenesis and prognosis of AF. This article will review current trends in genetic analysis concerning AF.

Identification and validation of key genes associated with atrial fibrillation in the elderly

Background

Atrial fibrillation (AF) is the most common cardiac arrhythmia and significantly increases the risk of stroke and heart failure (HF), contributing to a higher mortality rate. Increasing age is a major risk factor for AF; however, the mechanisms of how aging contributes to the occurrence and progression of AF remain unclear. This study conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes and determine their potential associations with aging-related AF.

Materials and methods

WGCNA was performed using the AF dataset GSE2240 obtained from the Gene Expression Omnibus, which contained data from atrial myocardium in cardiac patients with permanent AF or sinus rhythm (SR). Hub genes were identified in clinical samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed.

Results

Green and pink were the most critical modules associated with AF, from which nine hub genes, PTGDS, COLQ, ASTN2, VASH1, RCAN1, AMIGO2, RBP1, MFAP4, and ALDH1A1, were hypothesized to play key roles in the AF pathophysiology in elderly and seven of them have high diagnostic value. Functional enrichment analysis demonstrated that the green module was associated with the calcium, cyclic adenosine monophosphate (cAMP), and peroxisome proliferator-activated receptors (PPAR) signaling pathways, and the pink module may be associated with the transforming growth factor beta (TGF-β) signaling pathway in myocardial fibrosis.

Conclusion

We identified nine genes that may play crucial roles in the pathophysiological mechanism of aging-related AF, among which six genes were associated with AF for the first time. This study provided novel insights into the impact of aging on the occurrence and progression of AF, and identified biomarkers and potential therapeutic targets for AF.

Computational systems biology in disease modeling and control, review and perspectives

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.

The Impact of Donor and Recipient Genetic Variation on Outcomes After Solid Organ Transplantation: A Scoping Review and Future Perspectives

At the outset of solid organ transplantation, genetic variation between donors and recipients was recognized as a major player in mechanisms such as allograft tolerance and rejection. Genome-wide association studies have been very successful in identifying novel variant-trait associations, but have been difficult to perform in the field of solid organ transplantation due to complex covariates, era effects, and poor statistical power for detecting donor-recipient interactions. To overcome a lack of statistical power, consortia such as the International Genetics and Translational Research in Transplantation Network have been established. Studies have focused on the consequences of genetic dissimilarities between donors and recipients and have reported associations between polymorphisms in candidate genes or their regulatory regions with transplantation outcomes. However, knowledge on the exact influence of genetic variation is limited due to a lack of comprehensive characterization and harmonization of recipients' or donors' phenotypes and validation using an experimental approach. Causal research in genetics has evolved from agnostic discovery in genome-wide association studies to functional annotation and clarification of underlying molecular mechanisms in translational studies. In this overview, we summarize how the recent advances and progresses in the field of genetics and genomics have improved the understanding of outcomes after solid organ transplantation.

Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model Construction Based on XGBoost Algorithm

Background

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management.

Materials and Methods

In this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein–protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes.

Results

A total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes, including CXCL12, LTBP1, LOXL1, and IGFBP3, were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of CXCL12 expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability.

Conclusion

Key genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.

Genetics of atrial fibrillation-an update of recent findings

Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature death. AF has a strong genetic predisposition. This review highlights the recent findings on the genetics of AF from genome-wide association studies (GWAS) and high-throughput sequencing studies. The consensus from GWAS implies that AF is both polygenic and pleiotropic in nature. With the advent of whole-genome sequencing and whole-exome sequencing, rare variants associated with AF pathogenesis have been identified. The recent studies have contributed towards better understanding of AF pathogenesis.

Tissue-specific multi-omics analysis of atrial fibrillation

Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript (cis-eQTL) and protein (cis-pQTL) abundance. We further establish a novel targeted trans-QTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans-eQTLs and five trans-pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans-acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Association between ZFHX3 and PRRX1 Polymorphisms and Atrial Fibrillation Susceptibility from Meta-Analysis

Background

Atrial fibrillation (AF) is a common, sustained cardiac arrhythmia. Recent studies have reported an association between ZFHX3/PRRX1 polymorphisms and AF. In this study, a meta-analysis was conducted to confirm these associations. Objective and Methods. The PubMed, Embase, and Wanfang databases were searched, covering all publications before July 20, 2020.

Results

Overall, seven articles including 3,674 cases and 8,990 healthy controls for ZFHX3 rs2106261 and 1045 cases and 1407 controls for PRRX1 rs3903239 were included. The odds ratio (OR) (95% confidence interval (CI)) was used to assess the associations. Publication bias was calculated using Egger's and Begg's tests. We found that the ZFHX3 rs2106261 polymorphism increased AF risk in Asians (for example, allelic contrast: OR [95% CI]: 1.39 [1.31–1.47], P < 0.001). Similarly, strong associations were detected through stratified analysis using source of control and genotype methods (for example, allelic contrast: OR [95% CI]: 1.51 [1.38–1.64], P < 0.001 for HB; OR [95% CI]: 1.31 [1.21–1.41], P < 0.001 for PB; OR [95% CI]: 1.55 [1.33–1.80], P < 0.001 for TaqMan; and OR [95% CI]: 1.31 [1.21–1.41], P < 0.001 for high-resolution melt). In contrast, an inverse relationship was observed between the PRRX1 rs3903239 polymorphism and AF risk (C-allele vs. T-allele: OR [95% CI]: 0.83 [0.77–0.99], P=0.036; CT vs. TT: OR [95% CI]: 0.79 [0.67–0.94], P=0.006). No obvious evidence of publication bias was observed.

Conclusions

In summary, our study suggests that the ZFHX3 rs2106261 and PRRX1 rs3903239 polymorphisms are associated with AF risk, and larger case-controls must be carried out to confirm the abovementioned conclusions.

Mendelian Randomization Integrating GWAS, eQTL, and mQTL Data Identified Genes Pleiotropically Associated With Atrial Fibrillation

Background: Atrial fibrillation (AF) is the most common arrhythmia. Genome-wide association studies (GWAS) have identified more than 100 loci associated with AF, but the underlying biological interpretation remains largely unknown. The goal of this study is to identify gene expression and DNA methylation (DNAm) that are pleiotropically or potentially causally associated with AF, and to integrate results from transcriptome and methylome.

Methods: We used the summary data-based Mendelian randomization (SMR) to integrate GWAS with expression quantitative trait loci (eQTL) studies and methylation quantitative trait loci (mQTL) studies. The HEIDI (heterogeneity in dependent instruments) test was introduced to test against the null hypothesis that there is a single causal variant underlying the association.

Results: We prioritized 22 genes by eQTL analysis and 50 genes by mQTL analysis that passed the SMR &amp; HEIDI test. Among them, 6 genes were overlapped. By incorporating consistent SMR associations between DNAm and AF, between gene expression and AF, and between DNAm and gene expression, we identified several mediation models at which a genetic variant exerted an effect on AF by altering the DNAm level, which regulated the expression level of a functional gene. One example was the genetic variant-cg18693985-CPEB4-AF axis.

Conclusion: In conclusion, our integrative analysis identified multiple genes and DNAm sites that had potentially causal effects on AF. We also pinpointed plausible mechanisms in which the effect of a genetic variant on AF was mediated by genetic regulation of transcription through DNAm. Further experimental validation is necessary to translate the identified genes and possible mechanisms into clinical practice.

PRRX1 Loss-of-Function Mutations Underlying Familial Atrial Fibrillation

Background

Atrial fibrillation (AF) is the most common form of clinical cardiac dysrhythmia responsible for thromboembolic cerebral stroke, congestive heart failure, and death. Aggregating evidence highlights the strong genetic basis of AF. Nevertheless, AF is of pronounced genetic heterogeneity, and in an overwhelming majority of patients, the genetic determinants underpinning AF remain elusive.

Methods and Results

By genome‐wide screening with polymorphic microsatellite markers and linkage analysis in a 4‐generation Chinese family affected with autosomal‐dominant AF, a novel locus for AF was mapped to chromosome 1q24.2–q25.1, a 3.20‐cM (≈4.19 Mbp) interval between markers D1S2851 and D1S218, with the greatest 2‐point logarithm of odds score of 4.8165 for the marker D1S452 at recombination fraction=0.00. Whole‐exome sequencing and bioinformatics analyses showed that within the mapping region, only the mutation in the paired related homeobox 1 (PRRX1) gene, NM_022716.4:c.319C>T;(p.Gln107*), cosegregated with AF in the family. In addition, sequencing analyses of PRRX1 in another cohort of 225 unrelated patients with AF revealed a new mutation, NM_022716.4:c.437G>T; (p.Arg146Ile), in a patient. The 2 mutations were absent in 908 control subjects. Biological analyses in HeLa cells demonstrated that the 2 mutants had significantly diminished transactivation on the target genes ISL1 and SHOX2 and markedly decreased ability to bind the promoters of ISL1 and SHOX2 (2 genes causally linked to AF), although with normal intracellular distribution.

Conclusions

This study first indicates that PRRX1 loss‐of‐function mutations predispose to AF, which provides novel insight into the molecular pathogenesis underpinning AF, implying potential implications for precisive prophylaxis and management of AF.

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J-RHYTHM registry

Background

Machine learning (ML) has emerged as a promising tool for risk stratification. However, few studies have applied ML to risk assessment of patients with atrial fibrillation (AF).

Hypothesis

We aimed to compare the performance of random forest (RF), logistic regression (LR), and conventional risk schemes in predicting the outcomes of AF.

Methods

We analyzed data from 7406 nonvalvular AF patients (median age 71 years, female 29.2%) enrolled in a nationwide AF registry (J‐RHYTHM Registry) and who were followed for 2 years. The endpoints were thromboembolisms, major bleeding, and all‐cause mortality. Models were generated from potential predictors using an RF model, stepwise LR model, and the thromboembolism (CHADS₂ and CHA₂DS₂‐VASc) and major bleeding (HAS‐BLED, ORBIT, and ATRIA) scores.

Results

For thromboembolisms, the C‐statistic of the RF model was significantly higher than that of the LR model (0.66 vs. 0.59, p = .03) or CHA₂DS₂‐VASc score (0.61, p < .01). For major bleeding, the C‐statistic of RF was comparable to the LR (0.69 vs. 0.66, p = .07) and outperformed the HAS‐BLED (0.61, p < .01) and ATRIA (0.62, p < .01) but not the ORBIT (0.67, p = .07). The C‐statistic of RF for all‐cause mortality was comparable to the LR (0.78 vs. 0.79, p = .21). The calibration plot for the RF model was more aligned with the observed events for major bleeding and all‐cause mortality.

Conclusions

The RF model performed as well as or better than the LR model or existing clinical risk scores for predicting clinical outcomes of AF.

New biomarkers from multiomics approaches: improving risk prediction of atrial fibrillation

Atrial fibrillation (AF) is a common cardiac arrhythmia leading to many adverse outcomes and increased mortality. Yet the molecular mechanisms underlying AF remain largely unknown. Recent advances in high-throughput technologies make large-scale molecular profiling possible. In the past decade, multiomics studies of AF have identified a number of potential biomarkers of AF. In this review, we focus on the studies of multiomics profiles with AF risk. We summarize recent advances in the discovery of novel biomarkers for AF through multiomics studies. We also discuss limitations and future directions in risk assessment and discovery of therapeutic targets for AF.

Epigenetic Remodeling in Obesity-Related Vascular Disease

Significance: The prevalence of obesity and cardiometabolic phenotypes is alarmingly increasing across the globe and is associated with atherosclerotic vascular complications and high mortality. In spite of multifactorial interventions, vascular residual risk remains high in this patient population, suggesting the need for breakthrough therapies. The mechanisms underpinning obesity-related vascular disease remain elusive and represent an intense area of investigation. Recent Advances: Epigenetic modifications-defined as environmentally induced chemical changes of DNA and histones that do not affect DNA sequence-are emerging as a potent modulator of gene transcription in the vasculature and might significantly contribute to the development of obesity-induced endothelial dysfunction. DNA methylation and histone post-translational modifications cooperate to build complex epigenetic signals, altering transcriptional networks that are implicated in redox homeostasis, mitochondrial function, vascular inflammation, and perivascular fat homeostasis in patients with cardiometabolic disturbances. Critical Issues: Deciphering the epigenetic landscape in the vasculature is extremely challenging due to the complexity of epigenetic signals and their function in regulating transcription. An overview of the most important epigenetic pathways is required to identify potential molecular targets to treat or prevent obesity-related endothelial dysfunction and atherosclerotic disease. This would enable the employment of precision medicine approaches in this setting. Future Directions: Current and future research efforts in this field entail a better definition of the vascular epigenome in obese patients as well as the unveiling of novel, cell-specific chromatin-modifying drugs that are able to erase specific epigenetic signals that are responsible for maladaptive transcriptional alterations and vascular dysfunction in obese patients. Antioxid. Redox Signal. 34, 1165-1199.

Genetics of atrial fibrillation

PURPOSE OF REVIEW

Atrial fibrillation is the most common sustained cardiac arrhythmia. In addition to traditional risk factors, it is increasingly recognized that a genetic component underlies atrial fibrillation development. This review aims to provide an overview of the genetic cause of atrial fibrillation and clinical applications, with a focus on recent developments.

RECENT FINDINGS

Genome-wide association studies have now identified around 140 genetic loci associated with atrial fibrillation. Studies into the effects of several loci and their tentative gene targets have identified novel pathways associated with atrial fibrillation development. However, further validations of causality are still needed for many implicated genes. Genetic variants at identified loci also help predict individual atrial fibrillation risk and response to different therapies.

SUMMARY

Continued advances in the field of genetics and molecular biology have led to significant insight into the genetic underpinnings of atrial fibrillation. Potential clinical applications of these studies include the identification of new therapeutic targets and development of genetic risk scores to optimize management of this common cardiac arrhythmia.

Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.

An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer's disease

BACKGROUND

Multiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology.

METHODS

We performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD.

RESULTS

Multi-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1).

CONCLUSIONS

Applying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.

Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation

Objective

This study is aimed at understanding the molecular mechanisms and exploring potential therapeutic targets for atrial fibrillation (AF) by multiomics analysis.

Methods

Transcriptomics and methylation data of AF patients were retrieved from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially methylated sites between AF and normal samples were screened. Then, highly expressed and hypomethylated and lowly expressed and hypermethylated genes were identified for AF. Weighted gene coexpression network analysis (WGCNA) was presented to construct AF-related coexpression networks. 52 AF blood samples were used for whole exome sequence. The mutation was visualized by the maftools package in R. Key genes were validated in AF using independent datasets.

Results

DEGs were identified between AF and controls, which were enriched in neutrophil activation and regulation of actin cytoskeleton. RHOA, CCR2, CASP8, and SYNPO2L exhibited abnormal expression and methylation, which have been confirmed to be related to AF. PCDHA family genes had high methylation and low expression in AF. We constructed two AF-related coexpression modules. Single-nucleotide polymorphism (SNP) was the most common mutation type in AF, especially T > C. MUC4 was the most frequent mutation gene, followed by PHLDA1, AHNAK2, and MAML3. There was no statistical difference in expression of AHNAK2 and MAML3, for AF. PHLDA1 and MUC4 were confirmed to be abnormally expressed in AF.

Conclusion

Our findings identified DEGs related to DNA methylation and mutation for AF, which may offer possible therapeutic targets and a new insight into the pathogenesis of AF from a multiomics perspective.

Deep learning in systems medicine

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

KLF15 Loss-of-Function Mutation Underlying Atrial Fibrillation as well as Ventricular Arrhythmias and Cardiomyopathy

Atrial fibrillation (AF) represents the most common type of clinical cardiac arrhythmia and substantially increases the risks of cerebral stroke, heart failure and death. Accumulating evidence has convincingly demonstrated the strong genetic basis of AF, and an increasing number of pathogenic variations in over 50 genes have been causally linked to AF. Nevertheless, AF is of pronounced genetic heterogeneity, and the genetic determinants underpinning AF in most patients remain obscure. In the current investigation, a Chinese pedigree with AF as well as ventricular arrhythmias and hypertrophic cardiomyopathy was recruited. Whole exome sequencing and bioinformatic analysis of the available family members were conducted, and a novel heterozygous variation in the KLF15 gene (encoding Krüppel-like factor 15, a transcription factor critical for cardiac electrophysiology and structural remodeling), NM_014079.4: c.685A>T; p.(Lys229*), was identified. The variation was verified by Sanger sequencing and segregated with autosomal dominant AF in the family with complete penetrance. The variation was absent from 300 unrelated healthy subjects used as controls. In functional assays using a dual-luciferase assay system, mutant KLF15 showed neither transcriptional activation of the KChIP2 promoter nor transcriptional inhibition of the CTGF promoter, alone or in the presence of TGFB1, a key player in the pathogenesis of arrhythmias and cardiomyopathies. The findings indicate KLF15 as a new causative gene responsible for AF as well as ventricular arrhythmias and hypertrophic cardiomyopathy, and they provide novel insight into the molecular mechanisms underlying cardiac arrhythmias and hypertrophic cardiomyopathy.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

ISL1 loss-of-function variation causes familial atrial fibrillation

Atrial fibrillation (AF) represents the most frequent form of sustained cardiac rhythm disturbance, affecting approximately 1% of the general population worldwide, and confers a substantially enhanced risk of cerebral stroke, heart failure, and death. Increasing epidemiological studies have clearly demonstrated a strong genetic basis for AF, and variants in a wide range of genes, including those coding for ion channels, gap junction channels, cardiac structural proteins and transcription factors, have been identified to underlie AF. Nevertheless, the genetic pathogenesis of AF is complex and still far from completely understood. Here, whole-exome sequencing and bioinformatics analyses of a three-generation family with AF were performed, and after filtering variants by multiple metrics, we identified a heterozygous variant in the ISL1 gene (encoding a transcription factor critical for embryonic cardiogenesis and postnatal cardiac remodeling), NM_002202.2: c.481G > T; p.(Glu161*), which was validated by Sanger sequencing and segregated with autosome-dominant AF in the family with complete penetrance. The nonsense variant was absent from 284 unrelated healthy individuals used as controls. Functional assays with a dual-luciferase reporter assay system revealed that the truncating ISL1 protein lost transcriptional activation on the verified target genes MEF2C and NKX2-5. Additionally, the variant nullified the synergistic transactivation between ISL1 and TBX5 as well as GATA4, two other transcription factors that have been implicated in AF. The findings suggest ISL1 as a novel gene contributing to AF, which adds new insight to the genetic mechanisms underpinning AF, implying potential implications for genetic testing and risk stratification of the AF family members.

The molecular genetic basis of atrial fibrillation

As the most common cardiac arrhythmia, atrial fibrillation (AF) is a major risk factor for stroke, heart failure, and premature death with considerable associated costs. However, no available treatment options have optimal benefit-harm profiles currently, reflecting an incomplete understanding of the biological mechanisms underlying this complex arrhythmia. Recently, molecular epidemiological studies, especially genome-wide association studies, have emphasized the substantial genetic component of AF etiology. A comprehensive mapping of the genetic underpinnings for AF can expand our knowledge of AF mechanism and further facilitate the process of locating novel therapeutics for AF. Here we provide a state-of-the-art review of the molecular genetics of AF incorporating evidence from linkage analysis and candidate gene, as well as genome-wide association studies of common variations and rare copy number variations; potential epigenetic modifications (e.g., DNA methylation, histone modification, and non-coding RNAs) are also involved. We also outline the challenges in mechanism investigation and potential future directions in this article.

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.