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Integrative regulatory genomics for target gene prioritisation in SLE

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Integrative regulatory genomics for target gene prioritisation in SLE

  1. 1. Integrative regulatory genomics for target gene prioritisation in SLE Enrico Ferrero1,2 1Autoimmunity Transplantation and Inflammation Bioinformatics, Novartis Institutes for BioMedical Research, Novartis Campus, 4056 Basel, Switzerland 2Previous address: Computational Biology, GSK, GSK Medicine Research Centre, Stevenage SG1 2NY, United Kingdom 01. Background  Several drug discovery programmes fail because of a weak linkage between target and disease.  Genetic variation in disease can be used to identify promising targets, but our understanding of how genetic variation influences gene expression is limited.  Regulatory genomic data such as expression quantitative trait loci (eQTL), correlations and physical interactions between enhancers and promoters can be used to map non-coding genetic variants to their target genes, highlighting potential therapeutic targets (Figure 1). 02. Data  RNA-seq data from blood of systemic lupus erythematosus (SLE) patients and healthy controls [1];  Single nucleotide polymorphisms (SNPs) from SLE genome-wide association studies (GWASs) from the GWAS catalog [2];  Blood eQTL data from GTEx [3];  FANTOM5 correlations between enhancers and promoters across cell types and tissues [4];  Promoter-capture Hi-C interactions between enhancers and promoters from blood cell types [5]. 04. References 1. Hung et al. (2015) The Ro60 autoantigen binds endogenous retroelements and regulates inflammatory gene expression. Science. 2. MacArthur et al. (2017) The new NHGRI-EBI Catalog of published genome-wide association studies. Nucleic Acids Res. 3. GTEx Consortium (2017) Genetic effects on gene expression across human tissues. Nature. 4. Andersson et al. (2014) An atlas of active enhancers across human cell types and tissues. Nature. 5. Javierre et al. (2016) Lineage-specific genome architecture links enhancers and non-coding disease variants to target gene promoters. Cell. Gene Direction SNP P-value Location Method JAK2 Upregulated rs1887428 1 x 10-6 JAK2 5’UTR Direct overlap C2 Upregulated rs1270942 2 x 10-165 CFB intron GTEx eQTL TAX1BP1 Upregulated rs849142 1 x 9-11 JAZF1 intron Promoter capture Hi-C 03. Results  Several thousands of genes are differentially expressed in the blood of SLE patients when compared to healthy controls (Figure 2).  Most SLE GWAS SNPs are found in non-coding regions of genes (Figure 3).  Four methods are used to map SLE GWAS variants to differentially expressed genes (DEGs) in the blood of SLE patients (Figure 4):  Direct overlap: DEGs in SLE vs healthy blood with SLE GWAS SNPs in their coding regions (14);  GTEx eQTL: DEGs in SLE vs healthy blood with blood eQTL that are SLE non-coding GWAS SNPs (17);  FANTOM5 correlations: DEGs in SLE vs healthy blood with non-coding SLE GWAS SNPs in enhancers correlated with gene promoter (7);  Promoter-capture Hi-C: DEGs in SLE vs healthy blood with non-coding SLE GWAS SNPs in mapped enhancers physically interacting with gene promoter (13).  The set of genes differentially expressed in and genetically associated with SLE are highly enriched for well-known SLE pathological processes such as interferon response, antigen processing and presentation and co-stimulatory pathways (Figure 5).  DEGs linked to SLE GWAS SNPs through different approaches can be prioritized and followed up on as potential therapeutic targets for SLE (Table1). Figure 1. Overview of the integrative regulatory genomics workflow. Four approaches (direct overlap, GTEx eQTL, FANTOM5 correlations and promoter- capture Hi-C) are used sequentially to map SLE GWAS SNPs to genes differentially expressed in SLE, leveraging public regulatory genomic data. Figure 2. RNA-seq differential expression analysis. MA plot of the differential expression analysis of RNA extracted from the blood of SLE patients and healthy controls, showing a large numbers of genes being differentially expressed (4829 upregulated and 2709 downregulated at 5% FDR). Figure 3. Genomic location of SLE GWAS SNPs. Bar plot of SLE GWAS SNPs genomic locations, highlighting that a large majority number of variants fall in non-coding regions. Figure 4. Number of DEGs genetically linked to SLE as identified by the four approaches. Bar plot summarizing results of the mapping of SLE GWAS SNPs to SLE DEGs, showing that the great majority of genes (~66%) was retrieved using integrative regulatory genomics approaches. Figure 5. Gene Ontology biological process functional enrichment. Genes differentially expressed in SLE and genetically linked to the disease are highly enriched for biological mechanisms known to be dysregulated in SLE such as interferon response and immune cell activation. Table 1. Some examples of SLE GWAS variants mapped to SLE DEGs using the four approaches. The table reports the putative target gene; the directionality of the target gene expression in SLE patients; the SNP; the p-value of the association of the SNP with SLE; the genomic location of the SNP; the method used to assign the SNP to the target gene.

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