Dataset Information

VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

ABSTRACT: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms.We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to identify enrichment of type 1 diabetes (T1D) GWAS associations near genes that are targets for the transcription factors IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, while BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for T1D.VSEAMS is available for download (http://github.com/ollyburren/vseams).

SUBMITTER: Burren OS

PROVIDER: S-EPMC4296156 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

Burren Oliver S OS Guo Hui H Wallace Chris C

Bioinformatics (Oxford, England) 20140827 23

<h4>Motivation</h4>Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms.<h4>Results</h4>We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software ...[more]

PMID: 25170024

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Project description:MotivationMany GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data.ResultsWe build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis.Availability and implementationWe have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:High-laying ducks are often fed high-energy, nutritious feeds to maintain high productivity, which predisposes them to lipid metabolism disorders and the development of fatty liver syndrome (FLS), which seriously affects production performance and has a substantial economic impact on the poultry industry. Therefore, it is necessary to elucidate the mechanisms underlying the development of fatty liver syndrome. In this study, seven Shan Partridge ducks, each with fatty liver syndrome and normal laying ducks, were selected, and Hematoxylin Eosin staining (HE staining), Masson staining, and transcriptome sequencing were performed on liver tissue. In addition to exploring key genes and pathways using conventional analysis methods, we constructed the first Kyoto Encyclopedia of Genes and Genomes (KEGG) database-based predefined gene set containing 12,764 pathways and 16,836 genes and further performed gene set enrichment analysis (GSEA) on the liver transcriptome data. Finally, key nodes and biological processes were identified via the protein-protein interaction (PPI) network. The results showed that the liver in the FL group exhibited steatosis and fibrosis, and a total of 3,663 genes with upregulated expression versus 2,296 downregulated genes were screened by conventional analysis. GSEA analysis and PPI network analysis revealed that the liver in the FL group exhibited disruption of the mitochondrial electron transport chain, leading to decreased oxidative phosphorylation and the secretion of excessive proinflammatory factors amid the continuous accumulation of lipids. Under continuous chronic inflammation, cell cycle arrest triggers apoptosis, while fibrosis becomes more severe, and procarcinogenic genes are activated, leading to the continuous development and deterioration of the liver. In conclusion, the predefined gene set constructed in this study can be used for GSEA, and the identified hub genes provide useful reference data and a solid foundation for the study of the genetic regulatory mechanism of fatty liver syndrome in ducks.

Dataset Information

VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

Publications

VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes.

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