TY - DATA T1 - Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements PY - 2015/09/30 AU - James E. Hayes AU - Gosia Trynka AU - Joseph Vijai AU - Kenneth Offit AU - Soumya Raychaudhuri AU - Robert J. Klein UR - https://plos.figshare.com/articles/dataset/_Tissue_Specific_Enrichment_of_Lymphoma_Risk_Loci_in_Regulatory_Elements_/1560610 DO - 10.1371/journal.pone.0139360 L4 - https://ndownloader.figshare.com/files/2294147 L4 - https://ndownloader.figshare.com/files/2294148 L4 - https://ndownloader.figshare.com/files/2294149 L4 - https://ndownloader.figshare.com/files/2294150 L4 - https://ndownloader.figshare.com/files/2294152 L4 - https://ndownloader.figshare.com/files/2294153 L4 - https://ndownloader.figshare.com/files/2294154 L4 - https://ndownloader.figshare.com/files/2294155 L4 - https://ndownloader.figshare.com/files/2294156 L4 - https://ndownloader.figshare.com/files/2294157 L4 - https://ndownloader.figshare.com/files/2294158 KW - variants function KW - gene expression changes KW - multiscale approach KW - risk SNPs KW - Monte Carlo simulation approach KW - Gene regulation KW - lymphoma risk SNPs KW - ues KW - Lymphoma Risk Loci KW - GWAS results KW - Regulatory elements KW - ENCODE datasets KW - enrichment N2 - Though numerous polymorphisms have been associated with risk of developing lymphoma, how these variants function to promote tumorigenesis is poorly understood. Here, we report that lymphoma risk SNPs, especially in the non-Hodgkin’s lymphoma subtype chronic lymphocytic leukemia, are significantly enriched for co-localization with epigenetic marks of active gene regulation. These enrichments were seen in a lymphoid-specific manner for numerous ENCODE datasets, including DNase-hypersensitivity as well as multiple segmentation-defined enhancer regions. Furthermore, we identify putatively functional SNPs that are both in regulatory elements in lymphocytes and are associated with gene expression changes in blood. We developed an algorithm, UES, that uses a Monte Carlo simulation approach to calculate the enrichment of previously identified risk SNPs in various functional elements. This multiscale approach integrating multiple datasets helps disentangle the underlying biology of lymphoma, and more broadly, is generally applicable to GWAS results from other diseases as well. ER -