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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P10. FROM SNPS TO PATHWAYS: AN APPROACH TO STRENGTHEN BIOLOGICAL INTERPRETATION OF GWAS RESULTS Elisa Cirillo 1,* , Michiel Adriaens 2 & Chris T Evelo 1,2 . 1 Department of Bioinformatics – BiGCaT, Maastricht University, The Netherlands 2 Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, The Netherlands * elisa.cirillo@maastrichtuniversity.nl Pathway and network analysis are established and powerful methods for providing a biological context for a variety of omics data, including transcriptomics, proteomics and metabolomics. These approaches could in theory also be a boon for the interpretation of genetic variation data, for instance in the context of Genome Wide Association Studies (GWAS), as it would allow the study of genetic variants in the context of the biological processes in which the implicated genes and proteins are involved. However, currently genetic variation data cannot easily be integrated into pathways. Additionally, it is not clear how to visualise and interpret genetic variation data once connected to pathway content. In this project we take up that challenge and aim to (i) visualise SNPs from a Type 2 Diabetes Mellitus (T2DM) GWAS dataset on pathways and (ii) generate and analyze a network of all associated genes and pathways. Together, this could enable a comprehensive pathway and network interpretation of genetic variations in the context of T2DM. INTRODUCTION GWAS has become a common approach for discovery of gene disease relationships, in particular for complex diseases like T2DM (Wellcome Trust Case Control, 2009). However, biological interpretation remains a challenge, especially when it concerns connecting genetic findings with known biological processes. We wish to improve the interpretation of GWAS results, using a meaningful network representation that links SNPs to biological processes. METHODS We selected a GWAS data set related to T2DM from a meta GWAS resource for diseases created by Jhonson et al. (2009), and we extracted 1971 SNPs associated with T2DM. We identified the location for each SNP using Variant Effect Prediction (VeP) (http://www.ensembl.org) and we classified them in 5 categories (Figure 1): exonic, 3' UTR, 5' UTR, intronic and intergenic. SNPs located in the first three categories are easily connected to genes using BioMart Ensembl (http://www.ensembl.org/). Pathways related with these genes are identified from the curated collection of WikiPathways (Kutmon et al., 2015). SNPs, genes and pathways are visualized in networks using Cytoscape (Shannon et al., 2003). RESULTS & DISCUSSION We analysed four gene related SNP categories: 3' and 5' UTR, intronic and exonic. The exonic category was divided into 8 SNP sub-categories based on sequence interpretation: up- and downstream, splice region, synonymous, missense, stop/gain, transcription factor binding, and non-coding transcript. For each of the 11 resulting categories we created a SNP-disease genepathway network. Disease related genes are not always included in pathways and this is also the case for disease genes in which GWAS resulting SNPs were found. For the SNPs that are related to genes in pathways we did a pathway gene set enrichment analysis and evaluated whether the resulting pathways were already known to be related to T2DM. SNPs in intergenic region need to be analysed and visualized differently. A possible approach might be using the expression quantitative trait locus (eQTL) data, which relates SNPs in intergenic regions to modulation of gene expression distally. Such datasets are available for many different human tissues and can provide additional regulatory information for pathways and the genes they comprise. FIGURE 1. Pie chart of the 5 SNPs categories. The total number of SNPs is 2767. REFERENCES Wellcome Trust Case Control Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661-78. Johnson A, O'Donnell C. An Open Access Database of Genome-wide Association Results. BMC Medical Genetics. 2009;10(1):6. Kutmon M, Riutta A, Nunes N, Hanspers K, Willighagen E, Bohler A, Mélius J, Waagmeester A, Sinha S, Miller R, Coort S, Cirillo E Smeets B, Evelo C, Pico A. WikiPathways: Capturing the Full Diversity of Pathway Knowledge . Accepted September 2015, NAR- 02735- E- Database issue 2016. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research. 2003;13(11):2498-504. 54
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: P Poster 10th Benelux Bioinformatics Conference bbc 2015 P11. IDENTIFICATION OF TRANSCRIPTION FACTOR CO-ASSOCIATIONS IN SETS OF FUNCTIONALLY RELATED GENES Pieter De Bleser 1,2,4* , Arne Soetens 1,2,4 & Yvan Saeys 1,3,4 . VIB Inflammation Research Center 1 ; Department of Biomedical Molecular Biology 2 , Department of Respiratory Medicine 3 , Ghent University 4 . * pieterdb@irc.vib-ugent.be Co-associations between transcription factors (TFs) have been studied genome-wide and resulted in the identification of frequently co-associated pairs of TFs. Co-association of TFs at distinct binding sites is contextual: different combinations of TFs co-associate at different genomic locations, producing a condition-dependent gene expression profile for a cell. Here, we present a novel method to identify these condition-dependent co-associations of TFs in sets of functionally related genes. INTRODUCTION The functional expression of genes is achieved by particular interactions of regulatory transcription factors (TFs) operating at specific DNA binding sites of their target genes. Dissecting the specific co-associations of TFs that bind each target gene represent a difficult challenge. Co-associations of transcription factor pairs have been studied genome-wide and resulted in the identification of frequently co-associated pairs of TFs (ENCODE Project Consortium, 2012). It was found that TFs co-associate in a context-specific fashion: different combinations of TFs bind different target sites and the binding of one TF might influence the preferred binding partners of other TFs. Here, we present a tool to identify these condition-dependent coassociations of TFs in sets of functionally related genes (e.g. metabolic pathways, tissues, sets of TF target genes, sets of differentially regulated genes). METHODS In a first step, we determine the set of regulatory TFs for each gene (Tang et al., 2011) in the set using the ChIP-Seq binding data for 237 TFs from the ReMap database (Griffon et al., 2015). This results in a number of regulatory ChIP-Seq binding regions per TF per gene, represented as a matrix in which each row corresponds to a gene while the columns correspond to the used TF. In a next step, this matrix is used as input to the distance difference matrix (DDM) algorithm, modified to accommodate this data. The DDM algorithm is a method that simultaneously integrates statistical over representation and co-association of TFs (De Bleser et al., 2007). The result matrix is subsequently reduced, retaining only the columns of over-represented and co-associated TFs. Visualization is done by (1) hierarchical clustering of the reduced result matrix and reordering of the columns and (2) conversion of the reduced result matrix into a SIF (simple interaction file format) file, summarizing the regulator-regulated relationships between transcription factors and target genes. This SIF file can be imported into CytoScape for visualization of the regulatory network. RESULTS & DISCUSSION FOXF1, TBX3, GATA6, IRX3, PITX2, DLL1 and NKX2-5 are experimentally verified target genes of the EZH2 transcription factor (Grote et al., 2013). Running the transcription factor co-association analysis method on this data set results in the clustering solution plot shown in Figure 1. The strongest associations between TFs are found between EZH2, POU5F1, SUZ12 and CTBP2. A secondary cluster of transcription factor associations is composed of EOMES, SMAD2+3 and NANOG. The finding of SUZ12 as a cofactor can be accounted for: EZH2 and SUZ12 are subunits of Polycomb repressive complex 2 (PRC2), which is responsible for the repressive histone 3 lysine 27 trimethylation (H3K27me3) chromatin modification (Yoo and Hennighausen, 2012). CTBP2 is a known transcriptional repressor (Turner and Crossley, 2001). The method has been applied previously for the identification of TFs associated with both high tissuespecificity and high gene expression levels (Rincon et al., 2015). The method will be made available as a web tool. FIGURE 1. Transcription factor co-associations in the EZH2 data set. Note the tendency of EZH2 to co-localize with POU5F1, SUZ12 and CTBP2. REFERENCES De Bleser,P. et al. (2007) A distance difference matrix approach to identifying transcription factors that regulate differential gene expression. Genome Biol., 8, R83. ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57–74. Griffon,A. et al. (2015) Integrative analysis of public ChIP-seq experiments reveals a complex multi-cell regulatory landscape. Nucleic Acids Res., 43, e27. Grote,P. et al. (2013) The tissue-specific lncRNA Fendrr is an essential regulator of heart and body wall development in the mouse. Dev. Cell, 24, 206–214. Rincon,M.Y. et al. (2015) Genome-wide computational analysis reveals cardiomyocyte-specific transcriptional Cis-regulatory motifs that enable efficient cardiac gene therapy. Mol. Ther. J. Am. Soc. Gene Ther., 23, 43–52. Tang,Q. et al. (2011) A comprehensive view of nuclear receptor cancer cistromes. Cancer Res., 71, 6940–6947. Turner,J. and Crossley,M. (2001) The CtBP family: enigmatic and enzymatic transcriptional co-repressors. BioEssays News Rev. Mol. Cell. Dev. Biol., 23, 683–690. Yoo,K.H. and Hennighausen,L. (2012) EZH2 methyltransferase and H3K27 methylation in breast cancer. Int. J. Biol. Sci., 8, 59–65. 55
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