Dataset Information

Integrating genetic, transcriptional, and functional analyses to identify 5 novel genes for atrial fibrillation.

ABSTRACT: BACKGROUND:Atrial fibrillation (AF) affects >30 million individuals worldwide and is associated with an increased risk of stroke, heart failure, and death. AF is highly heritable, yet the genetic basis for the arrhythmia remains incompletely understood. METHODS AND RESULTS:To identify new AF-related genes, we used a multifaceted approach, combining large-scale genotyping in 2 ethnically distinct populations, cis-eQTL (expression quantitative trait loci) mapping, and functional validation. Four novel loci were identified in individuals of European descent near the genes NEURL (rs12415501; relative risk [RR]=1.18; 95% confidence interval [CI], 1.13-1.23; P=6.5×10(-16)), GJA1 (rs13216675; RR=1.10; 95% CI, 1.06-1.14; P=2.2×10(-8)), TBX5 (rs10507248; RR=1.12; 95% CI, 1.08-1.16; P=5.7×10(-11)), and CAND2 (rs4642101; RR=1.10; 95% CI, 1.06-1.14; P=9.8×10(-9)). In Japanese, novel loci were identified near NEURL (rs6584555; RR=1.32; 95% CI, 1.26-1.39; P=2.0×10(-25)) and CUX2 (rs6490029; RR=1.12; 95% CI, 1.08-1.16; P=3.9×10(-9)). The top single-nucleotide polymorphisms or their proxies were identified as cis-eQTLs for the genes CAND2 (P=2.6×10(-19)), GJA1 (P=2.66×10(-6)), and TBX5 (P=1.36×10(-5)). Knockdown of the zebrafish orthologs of NEURL and CAND2 resulted in prolongation of the atrial action potential duration (17% and 45%, respectively). CONCLUSIONS:We have identified 5 novel loci for AF. Our results expand the diversity of genetic pathways implicated in AF and provide novel molecular targets for future biological and pharmacological investigation.

SUBMITTER: Sinner MF

PROVIDER: S-EPMC4190011 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Integrating genetic, transcriptional, and functional analyses to identify 5 novel genes for atrial fibrillation.

Sinner Moritz F MF Tucker Nathan R NR Lunetta Kathryn L KL Ozaki Kouichi K Smith J Gustav JG Trompet Stella S Bis Joshua C JC Lin Honghuang H Chung Mina K MK Nielsen Jonas B JB Lubitz Steven A SA Krijthe Bouwe P BP Magnani Jared W JW Ye Jiangchuan J Gollob Michael H MH Tsunoda Tatsuhiko T Müller-Nurasyid Martina M Lichtner Peter P Peters Annette A Dolmatova Elena E Kubo Michiaki M Smith Jonathan D JD Psaty Bruce M BM Smith Nicholas L NL Jukema J Wouter JW Chasman Daniel I DI Albert Christine M CM Ebana Yusuke Y Furukawa Tetsushi T Macfarlane Peter W PW Harris Tamara B TB Darbar Dawood D Dörr Marcus M Holst Anders G AG Svendsen Jesper H JH Hofman Albert A Uitterlinden Andre G AG Gudnason Vilmundur V Isobe Mitsuaki M Malik Rainer R Dichgans Martin M Rosand Jonathan J Van Wagoner David R DR Benjamin Emelia J EJ Milan David J DJ Melander Olle O Heckbert Susan R SR Ford Ian I Liu Yongmei Y Barnard John J Olesen Morten S MS Stricker Bruno H C BH Tanaka Toshihiro T Kääb Stefan S Ellinor Patrick T PT

Circulation 20140814 15

<h4>Background</h4>Atrial fibrillation (AF) affects >30 million individuals worldwide and is associated with an increased risk of stroke, heart failure, and death. AF is highly heritable, yet the genetic basis for the arrhythmia remains incompletely understood.<h4>Methods and results</h4>To identify new AF-related genes, we used a multifaceted approach, combining large-scale genotyping in 2 ethnically distinct populations, cis-eQTL (expression quantitative trait loci) mapping, and functional val ...[more]

PMID: 25124494

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Project description:ObjectiveTo explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.MethodsWe downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.ResultsThe data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.ConclusionThe m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

Dataset Information

Integrating genetic, transcriptional, and functional analyses to identify 5 novel genes for atrial fibrillation.

Publications

Integrating genetic, transcriptional, and functional analyses to identify 5 novel genes for atrial fibrillation.

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