In recent decades the identification of pharmacogenomic gene-drug associations has evolved tremendously. Despite this progress, a major fraction of the heritable inter-individual variability remains elusive. Higher-dimensional phenomena, such as gene-gene-drug interactions, in which variability in multiple genes synergizes to precipitate an observable phenotype have been suggested to account at least for part of this missing heritability. However, the identification of such intricate relationships remains difficult partly because of analytical challenges associated with the complexity explosion of the problem. To facilitate the identification of such combinatorial pharmacogenetic associations, we here propose a network analysis strategy. Specifically, we analyzed the landscape of drug metabolizing enzymes and transporters for 100 top selling drugs as well as all compounds with pharmacogenetic germline labels or dosing guidelines. Based on this data, we calculated the posterior probabilities that gene i is involved in metabolism, transport or toxicity of a given drug under the condition that another gene j is involved for all pharmacogene pairs (i, j). Interestingly, these analyses revealed significant patterns between individual genes and across pharmacogene families that provide insights into metabolic interactions. To visualize the gene-drug interaction landscape, we use multidimensional scaling to collapse this similarity matrix into a two-dimensional network. We suggest that Euclidian distance between nodes can inform about the likelihood of epistatic interactions and thus might provide a useful tool to reduce the search space and facilitate the identification of combinatorial pharmacogenomic associations.

在最近的几十年中，药物基因组基因-药物关联的鉴定已发生了巨大的发展。尽管取得了这一进展，但可遗传的个体间变异性的大部分仍然难以捉摸。已经提出了更高维度的现象，例如基因-基因-药物相互作用，其中多个基因的变异协同作用以沉淀出可观察的表型，这至少可以解释这种缺失的遗传力的至少一部分。然而，部分地由于与问题的复杂性爆炸相关联的分析挑战而仍然难以识别这种复杂的关系。为了促进这种组合药物遗传学关联的鉴定，我们在这里提出一种网络分析策略。特别，我们分析了100种最畅销药物以及所有具有药物遗传学种系标签或剂量指导原则的化合物的药物代谢酶和转运蛋白的状况。基于此数据，我们计算了基因的后验概率我是参与代谢，即另一种基因的条件下一个给定的药物的运输或毒性Ĵ涉及所有pharmacogene双（我，Ĵ）。有趣的是，这些分析揭示了单个基因之间和整个药物基因家族之间的重要模式，这些模式提供了对代谢相互作用的见解。为了可视化基因-药物相互作用的格局，我们使用多维标度将这种相似性矩阵折叠成二维网络。我们建议节点之间的欧几里得距离可以告知上位相互作用的可能性，从而可能提供一个有用的工具来减少搜索空间并促进组合药物基因组学关联的识别。