A pleiotropic-epistatic entangelement model of drug response

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.

Automated summary

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.