Greene Casey S, Krishnan Arjun, Wong Aaron K, Ricciotti Emanuela, Zelaya Rene A, Himmelstein Daniel S, Zhang Ran, Hartmann Boris M, Zaslavsky Elena, Sealfon Stuart C, Chasman Daniel I, FitzGerald Garret A, Dolinski Kara, Grosser Tilo, Troyanskaya Olga G
1] Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA. [2] Dartmouth-Hitchcock Norris Cotton Cancer Center, Lebanon, New Hampshire, USA. [3] Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire, USA.
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.
Nat Genet. 2015 Jun;47(6):569-76. doi: 10.1038/ng.3259. Epub 2015 Apr 27.
Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.
组织和细胞类型的特性是人类生理学和疾病的核心。了解复杂组织和单个细胞谱系的遗传基础对于开发更好的诊断方法和治疗手段至关重要。我们展示了使用数据驱动的贝叶斯方法开发的144种人类组织和细胞类型的全基因组功能相互作用网络,该方法整合了数千个跨越组织和疾病状态的不同实验。组织特异性网络预测对扰动的谱系特异性反应,识别基因在不同组织中的功能角色变化,并阐明疾病之间的关系。我们引入了NetWAS,它将具有名义上显著的全基因组关联研究(GWAS)P值的基因与组织特异性网络相结合,比单独使用GWAS更准确地识别疾病-基因关联。我们的网络服务器GIANT通过多基因查询、网络可视化、包括NetWAS在内的分析工具以及可下载的网络,提供了一个访问人类组织网络的界面。GIANT能够系统地探索塑造一百多种人类组织和细胞类型中特殊细胞功能的相互作用基因景观。