Dataset Information

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.

ABSTRACT: Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association Study) of AF; and (3) a whole blood TWAS (Transcriptome-Wide Association Study) of AF. The variant-level GWAS results were collapsed into gene-level associations using fast set-based association analysis. The CpG-level EWAS results were also collapsed into gene-level associations by an adapted SNP-set Kernel Association Test approach. Both GWAS and EWAS gene-based associations were then meta-analyzed with TWAS using a fixed-effects model weighted by the sample size of each data set. A tissue-specific network was subsequently constructed using the NetWAS (Network-Wide Association Study). The identified genes were then compared with the AFGen 2018 GWAS that contained more than triple the number of AF cases compared with AFGen 2017 GWAS. We observed that the multiomics approach identified many more relevant AF-related genes than using AFGen 2018 GWAS alone (1931 versus 206 genes). Many of these genes are involved in the development and regulation of heart- and muscle-related biological processes. Moreover, the gene set identified by multiomics approach explained much more AF variance than those identified by GWAS alone (10.4% versus 3.5%). Conclusions: We developed a strategy to integrate multiple omics data to identify AF-related genes. Our integrative approach may be useful to improve the power of traditional GWAS, which might be particularly useful for rare traits and diseases with limited sample size.

SUBMITTER: Wang B

PROVIDER: S-EPMC7004281 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.

Wang Biqi B Lunetta Kathryn L KL Dupuis Josée J Lubitz Steven A SA Trinquart Ludovic L Yao Lixia L Ellinor Patrick T PT Benjamin Emelia J EJ Lin Honghuang H

Circulation research 20191205 3

Rationale: GWAS (Genome-Wide Association Studies) have identified hundreds of genetic loci associated with atrial fibrillation (AF). However, these loci explain only a small proportion of AF heritability. Objective: To develop an approach to identify additional AF-related genes by integrating multiple omics data. Methods and Results: Three types of omics data were integrated: (1) summary statistics from the AFGen 2017 GWAS; (2) a whole blood EWAS (Epigenome-Wide Association ...[more]

PMID: 31801406

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Project description:BackgroundAtrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism of AF and to discover feature genes by analyzing multi-omics data and by applying a machine learning approach.MethodsAt the transcriptomic level, four microarray datasets (GSE41177, GSE79768, GSE115574, GSE14975) were downloaded from the Gene Expression Omnibus database, which included 130 available atrial samples from AF and sinus rhythm (SR) patients with valvular heart disease. Microarray meta-analysis was adopted to identified differentially expressed genes (DEGs). At the proteomic level, a qualitative and quantitative analysis of proteomics in the left atrial appendage of 18 patients (9 with AF and 9 with SR) who underwent cardiac valvular surgery was conducted. The machine learning correlation-based feature selection (CFS) method was introduced to selected feature genes of AF using the training set of 130 samples involved in the microarray meta-analysis. The Naive Bayes (NB) based classifier constructed using training set was evaluated on an independent validation test set GSE2240.Results863 DEGs with FDR < 0.05 and 482 differentially expressed proteins (DEPs) with FDR < 0.1 and fold change > 1.2 were obtained from the transcriptomic and proteomic study, respectively. The DEGs and DEPs were then analyzed together which identified 30 biomarkers with consistent trends. Further, 10 features, including 8 upregulated genes (CD44, CHGB, FHL2, GGT5, IGFBP2, NRAP, SEPTIN6, YWHAQ) and 2 downregulated genes (TNNI1, TRDN) were selected from the 30 biomarkers through machine learning CFS method using training set. The NB based classifier constructed using the training set accurately and reliably classify AF from SR samples in the validation test set with a precision of 87.5% and AUC of 0.995.ConclusionTaken together, our present work might provide novel insights into the molecular mechanism and provide some promising diagnostic and therapeutic targets of AF.

Project description:BackgroundResistance to chemotherapy severely limits the effectiveness of chemotherapy drugs in treating cancer. Still, the mechanisms and critical pathways that contribute to chemotherapy resistance are relatively unknown. This study elucidates the chemoresistance-associated pathways retrieved from the integrated biological interaction networks and identifies signature genes relevant for chemotherapy resistance.MethodsAn integrated network was constructed by collecting multiple metabolic interactions from public databases and the k-shortest path algorithm was implemented to identify chemoresistant related pathways. The identified pathways were then scored using differential expression values from microarray data in chemosensitive and chemoresistant ovarian and lung cancers. Finally, another pathway database, Reactome, was used to evaluate the significance of genes within each filtered pathway based on topological characteristics.ResultsBy this method, we discovered pathways specific to chemoresistance. Many of these pathways were consistent with or supported by known involvement in chemotherapy. Experimental results also indicated that integration of pathway structure information with gene differential expression analysis can identify dissimilar modes of gene reactions between chemosensitivity and chemoresistance. Several identified pathways can increase the development of chemotherapeutic resistance and the predicted signature genes are involved in drug resistant during chemotherapy. In particular, we observed that some genes were key factors for joining two or more metabolic pathways and passing down signals, which may be potential key targets for treatment.ConclusionsThis study is expected to identify targets for chemoresistant issues and highlights the interconnectivity of chemoresistant mechanisms. The experimental results not only offer insights into the mode of biological action of drug resistance but also provide information on potential key targets (new biological hypothesis) for further drug-development efforts.

Dataset Information

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.

Publications

Integrative Omics Approach to Identifying Genes Associated With Atrial Fibrillation.

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