Tissue-Specific Enrichment of Lymphoma Risk Loci in Regulatory Elements

• Published in: PloS one Vol. 10; no. 9; p. e0139360
• Main Authors: Hayes, James E, Trynka, Gosia, Vijai, Joseph, Offit, Kenneth, Raychaudhuri, Soumya, Klein, Robert J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.09.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Bioinformatics; Biology; Cancer; Chronic lymphocytic leukemia; Computer simulation; Datasets; Deoxyribonuclease; Enrichment; Gene expression; Gene regulation; Genetic Loci; Genome, Human; Genome-Wide Association Study; Genomes; Genomics; Hospitals; Humans; Hypersensitivity; Hypotheses; Immunology; Leukemia; Leukemia, Lymphocytic, Chronic, B-Cell - genetics; Linkage Disequilibrium; Localization; Lymphatic leukemia; Lymphocytes; Lymphoma; Lymphomas; Medicine; Monte Carlo Method; Monte Carlo simulation; Organ Specificity; Physiological aspects; Polymorphism, Single Nucleotide; Population genetics; Regulatory Elements, Transcriptional; Regulatory sequences; Rheumatology; Risk; Risk Factors; Segmentation; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Transcription factors; Tumor necrosis factor-TNF; Tumorigenesis; Womens health; New York; United Kingdom > UK; United States > US; Massachusetts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0139360

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.