A pleiotropic-epistatic entangelement model of drug response

Yu Wang; Mengmeng Sang; Li Feng; Claudia Gragnoli; Christopher Griffin; Rongling Wu

doi:10.1016/j.drudis.2023.103790

A pleiotropic-epistatic entangelement model of drug response

Drug Discov Today. 2023 Nov;28(11):103790. doi: 10.1016/j.drudis.2023.103790. Epub 2023 Sep 26.

Authors

Yu Wang¹, Mengmeng Sang², Li Feng³, Claudia Gragnoli⁴, Christopher Griffin⁵, Rongling Wu⁶

Affiliations

¹ Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.
² Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, Jiangsu 226019, China.
³ Fisheries Engineering Institute, Chinese Academy of Fishery Sciences, Beijing 1000141, China.
⁴ Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy.
⁵ Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.
⁶ Center for Computational Biology, Beijing Forestry University, Beijing 100083, China; Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China. Electronic address: ronglingwu@bimsa.cn.

PMID: 37758020
DOI: 10.1016/j.drudis.2023.103790

Abstract

Because drug response is multifactorial, graph models are uniquely powerful for comprehending its 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 of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of 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.

Keywords: complex system; complex trait; epistatic network; evolutionary game theory; pleiotropic network.

Publication types

Review

MeSH terms

Epistasis, Genetic*
Phenotype