Journal
DRUG DISCOVERY TODAY
Volume 28, Issue 11, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.drudis.2023.103790
Keywords
evolutionary game theory; epistatic network; pleiotropic network; complex trait; complex system
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This study proposes a statistical physics framework to analyze the influence of genetic architecture on drug response. By deconstructing drug response into multiple sub-traits and studying the interactions between genes, the multi-level effects of drug response outcomes are revealed. The analysis and interpretation of pleiotropic and epistatic effects provide practical implications for pharmacogenomic studies.
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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