Hayes James E, Trynka Gosia, Vijai Joseph, Offit Kenneth, Raychaudhuri Soumya, Klein Robert J
Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Program in Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Cell and Developmental Biology Graduate Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, New York, United States of America; Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
Division of Rheumatology, Immunology and Allergy, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America; Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America; Partners Center for Personalized Genetic Medicine, Boston, Massachusetts, United States of America; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
PLoS One. 2015 Sep 30;10(9):e0139360. doi: 10.1371/journal.pone.0139360. eCollection 2015.
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.
尽管众多多态性与淋巴瘤发生风险相关，但这些变异如何促进肿瘤发生的机制却知之甚少。在此，我们报告淋巴瘤风险单核苷酸多态性（SNP），尤其是在非霍奇金淋巴瘤亚型慢性淋巴细胞白血病中，与活跃基因调控的表观遗传标记共定位显著富集。在众多ENCODE数据集（包括DNase超敏性以及多个分段定义的增强子区域）中，以淋巴特异性方式观察到了这些富集现象。此外，我们鉴定出既存在于淋巴细胞调控元件中又与血液中基因表达变化相关的推定功能性SNP。我们开发了一种算法UES，它使用蒙特卡罗模拟方法来计算先前鉴定的风险SNP在各种功能元件中的富集情况。这种整合多个数据集的多尺度方法有助于理清淋巴瘤的潜在生物学机制，更广泛地说，也普遍适用于其他疾病的全基因组关联研究（GWAS）结果。
