Dataset Information

Gene-gene Interaction Analyses for Atrial Fibrillation.

ABSTRACT: Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility. We performed a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65,237 AF-free referents collected from 15 studies for discovery. We examined putative interactions between genome-wide SNPs and 17 known AF-related SNPs. The top interactions were then tested for association in an independent cohort for replication, which included more than 2,363 AF cases and 114,746 AF-free referents. One interaction, between rs7164883 at the HCN4 locus and rs4980345 at the SLC28A1 locus, was found to be significantly associated with AF in the discovery cohorts (interaction OR?=?1.44, 95% CI: 1.27-1.65, P?=?4.3?×?10-8). Eight additional gene-gene interactions were also marginally significant (P?

SUBMITTER: Lin H

PROVIDER: S-EPMC5099695 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Gene-gene Interaction Analyses for Atrial Fibrillation.

Lin Honghuang H Mueller-Nurasyid Martina M Smith Albert V AV Arking Dan E DE Barnard John J Bartz Traci M TM Lunetta Kathryn L KL Lohman Kurt K Kleber Marcus E ME Lubitz Steven A SA Geelhoed Bastiaan B Trompet Stella S Niemeijer Maartje N MN Kacprowski Tim T Chasman Daniel I DI Klarin Derek D Sinner Moritz F MF Waldenberger Melanie M Meitinger Thomas T Harris Tamara B TB Launer Lenore J LJ Soliman Elsayed Z EZ Chen Lin Y LY Smith Jonathan D JD Van Wagoner David R DR Rotter Jerome I JI Psaty Bruce M BM Xie Zhijun Z Hendricks Audrey E AE Ding Jingzhong J Delgado Graciela E GE Verweij Niek N van der Harst Pim P Macfarlane Peter W PW Ford Ian I Hofman Albert A Uitterlinden André A Heeringa Jan J Franco Oscar H OH Kors Jan A JA Weiss Stefan S Völzke Henry H Rose Lynda M LM Natarajan Pradeep P Kathiresan Sekar S Kääb Stefan S Gudnason Vilmundur V Alonso Alvaro A Chung Mina K MK Heckbert Susan R SR Benjamin Emelia J EJ Liu Yongmei Y März Winfried W Rienstra Michiel M Jukema J Wouter JW Stricker Bruno H BH Dörr Marcus M Albert Christine M CM Ellinor Patrick T PT

Scientific reports 20161108

Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility. We performed a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65,237 AF-free referents collected from 15 studies for discovery. We examined putative interactions between ...[more]

PMID: 27824142

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Project description:BackgroundAtrial fibrillation (AF) often arises from structural abnormalities in the left atria (LA). Annotation of the noncoding genome in human LA is limited, as are effects on gene expression and chromatin architecture. Many AF-associated genetic variants reside in noncoding regions; this knowledge gap impairs efforts to understand the molecular mechanisms of AF and cardiac conduction phenotypes.MethodsWe generated a model of the LA noncoding genome by profiling 7 histone post-translational modifications (active: H3K4me3, H3K4me2, H3K4me1, H3K27ac, H3K36me3; repressive: H3K27me3, H3K9me3), CTCF binding, and gene expression in samples from 5 individuals without structural heart disease or AF. We used MACS2 to identify peak regions (P<0.01), applied a Markov model to classify regulatory elements, and annotated this model with matched gene expression data. We intersected chromatin states with expression quantitative trait locus, DNA methylation, and HiC chromatin interaction data from LA and left ventricle. Finally, we integrated genome-wide association data for AF and electrocardiographic traits to link disease-related variants to genes.ResultsOur model identified 21 epigenetic states, encompassing regulatory motifs, such as promoters, enhancers, and repressed regions. Genes were regulated by proximal chromatin states; repressive states were associated with a significant reduction in gene expression (P<2×10-16). Chromatin states were differentially methylated, promoters were less methylated than repressed regions (P<2×10-16). We identified over 15 000 LA-specific enhancers, defined by homeobox family motifs, and annotated several cardiovascular disease susceptibility loci. Intersecting AF and PR genome-wide association studies loci with long-range chromatin conformation data identified a gene interaction network dominated by NKX2-5, TBX3, ZFHX3, and SYNPO2L.ConclusionsProfiling the noncoding genome provides new insights into the gene expression and chromatin regulation in human LA tissue. These findings enabled identification of a gene network underlying AF; our experimental and analytic approach can be extended to identify molecular mechanisms for other cardiac diseases and traits.

Project description:Atrial fibrillation (AF) is the most common cardiac arrhythmia at the clinic. Recent GWAS identified several variants associated with AF, but they account for <10% of heritability. Gene-gene interaction is assumed to account for a significant portion of missing heritability. Among GWAS loci for AF, only three were replicated in the Chinese Han population, including SNP rs2106261 (G/A substitution) in ZFHX3, rs2200733 (C/T substitution) near PITX2c, and rs3807989 (A/G substitution) in CAV1. Thus, we analyzed the interaction among these three AF loci. We demonstrated significant interaction between rs2106261 and rs2200733 in three independent populations and combined population with 2,020 cases/5,315 controls. Compared to non-risk genotype GGCC, two-locus risk genotype AATT showed the highest odds ratio in three independent populations and the combined population (OR=5.36 (95% CI 3.87-7.43), P=8.00×10-24). The OR of 5.36 for AATT was significantly higher than the combined OR of 3.31 for both GGTT and AACC, suggesting a synergistic interaction between rs2106261 and rs2200733. Relative excess risk due to interaction (RERI) analysis also revealed significant interaction between rs2106261 and rs2200733 when exposed two copies of risk alleles (RERI=2.87, P<1.00×10-4) or exposed to one additional copy of risk allele (RERI=1.29, P<1.00×10-4). The INTERSNP program identified significant genotypic interaction between rs2106261 and rs2200733 under an additive by additive model (OR=0.85, 95% CI: 0.74-0.97, P=0.02). Mechanistically, PITX2c negatively regulates expression of miR-1, which negatively regulates expression of ZFHX3, resulting in a positive regulation of ZFHX3 by PITX2c; ZFHX3 positively regulates expression of PITX2C, resulting in a cyclic loop of cross-regulation between ZFHX3 and PITX2c. Both ZFHX3 and PITX2c regulate expression of NPPA, TBX5 and NKX2.5. These results suggest that cyclic cross-regulation of gene expression is a molecular basis for gene-gene interactions involved in genetics of complex disease traits.

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

Gene-gene Interaction Analyses for Atrial Fibrillation.

Publications

Gene-gene Interaction Analyses for Atrial Fibrillation.

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