文献类型： 外文期刊
作者： Wang, Yu 1 ; Sang, Mengmeng 2 ; Feng, Li 3 ; Gragnoli, Claudia 4 ; Grif, Christopher 7 ; Wu, Rongling 1 ;
作者机构： 1.Beijing Forestry Univ, Ctr Computat Biol, Beijing 100083, Peoples R China
2.Med Sch Nantong Univ, Inst Reprod Med, Sch Med, Nantong 226019, Jiangsu, Peoples R China
3.Chinese Acad Fishery Sci, Fisheries Engn Inst, Beijing 1000141, Peoples R China
4.Penn State Coll Med, Dept Publ Hlth Sci, Hershey, PA 17033 USA
5.Creighton Univ, Sch Med, Dept Med, Omaha, NE 68124 USA
6.Bios Biotech Multidiagnost Hlth Ctr, Mol Biol Lab, I-00197 Rome, Italy
7.Penn State Univ, Appl Res Lab, University Pk, PA 16802 USA
8.Beijing Yanqi Lake Inst Math Sci & Applicat, Beijing 101408, Peoples R China
9.Tsinghua Univ, Yau Math Sci Ctr, Beijing 100084, Peoples R China
关键词： evolutionary game theory; epistatic network; pleiotropic network; complex trait; complex system
期刊名称：DRUG DISCOVERY TODAY （ 影响因子：7.4； 五年影响因子：8.1 ）
ISSN： 1359-6446
年卷期： 2023 年 28 卷 11 期
页码：
收录情况： SCI
摘要： Because drug response is multifactorial, graph models are uniquely powerful for comprehending genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set sub-traits to shape body-drug interactions. This model can dissect the topological architecture epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.
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