Peters Lauren A, Perrigoue Jacqueline, Mortha Arthur, Iuga Alina, Song Won-Min, Neiman Eric M, Llewellyn Sean R, Di Narzo Antonio, Kidd Brian A, Telesco Shannon E, Zhao Yongzhong, Stojmirovic Aleksandar, Sendecki Jocelyn, Shameer Khader, Miotto Riccardo, Losic Bojan, Shah Hardik, Lee Eunjee, Wang Minghui, Faith Jeremiah J, Kasarskis Andrew, Brodmerkel Carrie, Curran Mark, Das Anuk, Friedman Joshua R, Fukui Yoshinori, Humphrey Mary Beth, Iritani Brian M, Sibinga Nicholas, Tarrant Teresa K, Argmann Carmen, Hao Ke, Roussos Panos, Zhu Jun, Zhang Bin, Dobrin Radu, Mayer Lloyd F, Schadt Eric E
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Icahn Institute of Genomics and Multi-scale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Nat Genet. 2017 Oct;49(10):1437-1449. doi: 10.1038/ng.3947. Epub 2017 Sep 11.
A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.
炎症性肠病（IBD）面临的一个主要挑战是整合各种IBD数据集以构建IBD预测模型。我们提出了一种IBD免疫成分的预测模型，该模型利用与IBD的细胞、组织和病理生理学相关的功能和调控注释，揭示了先前通过全基因组关联研究（GWAS）与IBD相关的基因座之间的因果关系。我们的模型由使用从处于疾病不同阶段的三个IBD患者群体分离的肠道样本生成的分子数据构建的个体网络组成。我们进行了关键驱动因素分析，以识别预测可调节与IBD相关的网络调控状态的基因，通过实验对前12个关键驱动因素进行了优先排序和前瞻性验证。这个经过验证的关键驱动因素集不仅引入了IBD核心过程的新调节因子，还提供了遗传、分子和临床特征的整合电路，可直接用于查询和完善定义IBD的调控框架。
