生物
全基因组关联研究
多细胞生物
计算生物学
电池类型
基因调控网络
基因组
基因
功能(生物学)
疾病
人类基因组
遗传学
细胞
基因表达
单核苷酸多态性
病理
基因型
医学
作者
Casey S. Greene,Arjun Krishnan,Aaron K. Wong,Emanuela Ricciotti,René A. Zelaya,Daniel Himmelstein,Ran Zhang,Boris Hartmann,Elena Zaslavsky,Stuart C. Sealfon,Daniel I. Chasman,Garret A. FitzGerald,Kara Dolinski,Tilo Großer,Olga G. Troyanskaya
出处
期刊:Nature Genetics
[Springer Nature]
日期:2015-04-27
卷期号:47 (6): 569-576
被引量:746
摘要
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.
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