Gene set enrichment analysis for genome-wide DNA methylation data.
Ontology highlight
ABSTRACT:
SUBMITTER: Maksimovic J
PROVIDER: S-EPMC8186068 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
Publications
Gene set enrichment analysis for genome-wide DNA methylation data.
Maksimovic Jovana J Oshlack Alicia A Phipson Belinda B
Genome biology 20210608 1
DNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking ...[more]
Similar Datasets
Project description:Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. Experimental Design Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas (SqCC), one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), and three small cell carcinomas (SCLC). Complimentary gene expression analyses was performed on 117 of the 124 tumors using Illumina HT12 V4 arrays (reported here). Gene expression profiling of 117 lung carcinomas using Illumina HT-12 V4 microarrays.
| S-ECPF-GEOD-60644 | biostudies-other
Project description:BackgroundIntermittent explosive disorder is defined as a recurrent, problematic, and impulsive aggression that affects 3% to 4% of the US population. While behavioral genetic studies report a substantial degree of genetic influence on aggression and impulsivity, epigenetic mechanisms underlying aggression and intermittent explosive disorder are not well known.MethodsThe sample included 44 subjects (22 with a DSM-5 diagnosis of intermittent explosive disorder and 22 comparable subjects without intermittent explosive disorder). Peripheral blood DNA methylome was profiled using the Illumina Infinium HumanMethylation450 Beadchip. Intermittent explosive disorder-associated genome-wide DNA methylation changes were analyzed using the CpGassoc R package, with covariates age, sex, and race being adjusted. A gene-based functional enrichment analysis was performed to identify pathways that were overrepresented by genes harboring highly differentially methylated CpG sites.ResultsA total of 27 CpG sites were differentially methylated in IED participants (P<5.0×10-5), but none reached genome-wide significant threshold. Functional enrichment analysis revealed that genes mapped by these CpG sites are involved in the inflammatory/immune system, the endocrine system, and neuronal differentiation.ConclusionsConsistent with our previous studies showing an association of inflammatory response with aggressive behavior in intermittent explosive disorder subjects, our gene-based pathway analysis using differentially methylated CpG sites supports inflammatory response as an important mechanism involved in intermittent explosive disorder and reveals other novel biological processes possibly associated with intermittent explosive disorder.
| S-EPMC5789263 | biostudies-literature
Project description:Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, among others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behavior and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia.
| S-EPMC4346504 | biostudies-literature
Project description:Complex diseases such as hypertension are inherently multifactorial and involve many factors of mild-to-minute effect sizes. A genome-wide association study (GWAS) typically tests hundreds of thousands of single-nucleotide polymorphisms (SNPs), and offers opportunity to evaluate aggregated effects of many genetic variants with effects that are too small to detect individually. The gene-set-enrichment analysis (GSEA) is a pathway-based approach that tests for such aggregated effects of genes that are linked by biological functions. A key step in GSEA is the summary statistic (gene score) used to measure the overall relevance of a gene based on all SNPs tested in the gene. Existing GSEA methods use maximum statistics sensitive to gene size and linkage equilibrium. We propose the approach of variable set enrichment analysis (VSEA) and study new gene score methods that are less dependent on gene size. The new method treats groups of variables (SNPs or other variants) as base units for summarizing gene scores and relies less on gene definition itself. The power of VSEA is analyzed by simulation studies modeling various scenarios of complex multiloci interactions. Results show that the new gene scores generally performed better, some substantially so, than existing GSEA extension to GWAS. The new methods are implemented in an R package and when applied to a real GWAS data set demonstrated its practical utility in a GWAS setting.
| S-EPMC3172928 | biostudies-literature
Project description:Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
| S-EPMC1239896 | biostudies-literature
Project description:BackgroundDNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed.ResultsWe developed "MeDEStrand," a method that uses a sigmoid function to estimate and correct the CpG bias from enrichment results to infer absolute DNA methylation levels. Unlike previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compared the performance of MeDEStrand to that of three other state-of-the-art methods "MEDIPS," "BayMeth," and "QSEA" on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human primary cells (foreskin fibroblasts and mammary epithelial cells). Based on the comparison of the inferred absolute methylation levels from MeDIP-seq data and the corresponding reduced-representation bisulfite sequencing data from each method, MeDEStrand showed the best performance at high resolution of 25, 50, and 100 base pairs.ConclusionsThe MeDEStrand tool can be used to infer whole-genome absolute DNA methylation levels at the same cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git .
| S-EPMC6303941 | biostudies-literature
Project description:Recent genome-wide association studies (GWAS) of chronic periodontitis (CP) offer rich data sources for the investigation of candidate genes, functional elements, and pathways. We used GWAS data of CP (n = 4,504) and periodontal pathogen colonization (n = 1,020) from a cohort of adult Americans of European descent participating in the Atherosclerosis Risk in Communities study and employed a MAGENTA approach (i.e., meta-analysis gene set enrichment of variant associations) to obtain gene-centric and gene set association results corrected for gene size, number of single-nucleotide polymorphisms, and local linkage disequilibrium characteristics based on the human genome build 18 (National Center for Biotechnology Information build 36). We used the Gene Ontology, Ingenuity, KEGG, Panther, Reactome, and Biocarta databases for gene set enrichment analyses. Six genes showed evidence of statistically significant association: 4 with severe CP (NIN, p = 1.6 × 10(-7); ABHD12B, p = 3.6 × 10(-7); WHAMM, p = 1.7 × 10(-6); AP3B2, p = 2.2 × 10(-6)) and 2 with high periodontal pathogen colonization (red complex-KCNK1, p = 3.4 × 10(-7); Porphyromonas gingivalis-DAB2IP, p = 1.0 × 10(-6)). Top-ranked genes for moderate CP were HGD (p = 1.4 × 10(-5)), ZNF675 (p = 1.5 × 10(-5)), TNFRSF10C (p = 2.0 × 10(-5)), and EMR1 (p = 2.0 × 10(-5)). Loci containing NIN, EMR1, KCNK1, and DAB2IP had showed suggestive evidence of association in the earlier single-nucleotide polymorphism-based analyses, whereas WHAMM and AP2B2 emerged as novel candidates. The top gene sets included severe CP ("endoplasmic reticulum membrane," "cytochrome P450," "microsome," and "oxidation reduction") and moderate CP ("regulation of gene expression," "zinc ion binding," "BMP signaling pathway," and "ruffle"). Gene-centric analyses offer a promising avenue for efficient interrogation of large-scale GWAS data. These results highlight genes in previously identified loci and new candidate genes and pathways possibly associated with CP, which will need to be validated via replication and mechanistic studies.
| S-EPMC4213253 | biostudies-literature
Project description:Despite progress in the treatment of hepatocellular carcinoma (HCC), 5-year survival rates remain low. Thus, a more comprehensive approach to explore the mechanism of HCC is needed to provide new leads for targeted therapy. We performed an integrated analysis to discover the relationship between DNA methylation and gene expression in hepatocellular carcinoma (HCC). DNA methylation and gene expression data for HCC were downloaded from The Cancer Genome Atlas (TCGA) database, and differential analysis was performed. Correlation analysis between DNA methylation and gene expression data was then performed in R language. Finally, we selected several crucial genes and evaluated their potential use as diagnostic biomarkers for HCC. In total, 1135 differentially DNA-methylated CpG sites (DMCs), 377 differentially methylated regions (DMRs), and 1194 differentially expressed genes (DEGs) were identified in HCC. Among the DEGs, 14 genes (ALX3, B4GALNT1,CTHRC1,DLX5,EMX1,IRX3,OTX1,SIX2,TLX1,VASH2,ZIC2,ZIC4,ZIC5, and ZNF695) exhibited changes in DNA methylation in terms of CpG sites or CpG island (CGI) level, of which TLX1 and ZIC4 had the most DMCs (12 and 13, respectively). Further analysis of CTHRC1,ZIC4,SIX2,VASH2,IL17D,TLX1,OTX1, and LART, examining alterations in both DNA methylation and gene expression level in HCC, showed their potential diagnostic value for HCC was better at the gene expression level than that the DNA methylation level. The DNA methylation status of CTHRC1,VASH2, and IL7D was significantly associated with HCC overall survival (P-value <0.05). This systemic analysis identified a group of novel gene signatures (CTHRC1,ZIC4, and OTX1) that may be regulated by DNA hypermethylation, which may be closely associated with HCC.
| S-EPMC6026698 | biostudies-literature
Project description:Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) as well as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea).
| S-EPMC5389680 | biostudies-literature
Project description:UnlabelledHere we present the open-source R/Bioconductor software package BEAT (BS-Seq Epimutation Analysis Toolkit). It implements all bioinformatics steps required for the quantitative high-resolution analysis of DNA methylation patterns from bisulfite sequencing data, including the detection of regional epimutation events, i.e. loss or gain of DNA methylation at CG positions relative to a reference. Using a binomial mixture model, the BEAT package aggregates methylation counts per genomic position, thereby compensating for low coverage, incomplete conversion and sequencing errors.Availability and implementationBEAT is freely available as part of Bioconductor at www.bioconductor.org/packages/devel/bioc/html/BEAT.html. The package is distributed under the GNU Lesser General Public License 3.0.
| S-EPMC4071208 | biostudies-literature