Zhou Yitian, Lauschke Volker M
Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm 171 77, Sweden.
Comput Struct Biotechnol J. 2019 Dec 5;18:52-58. doi: 10.1016/j.csbj.2019.11.010. eCollection 2020.
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 is involved in metabolism, transport or toxicity of a given drug under the condition that another gene is involved for all pharmacogene pairs (, ). 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.
近几十年来，药物基因组学基因-药物关联的识别取得了巨大进展。尽管有这一进展，但可遗传的个体间差异的很大一部分仍然难以捉摸。有人提出，诸如基因-基因-药物相互作用等更高维度的现象（其中多个基因的变异协同作用以产生可观察到的表型）至少可以解释部分这种缺失的遗传性。然而，识别这种复杂关系仍然很困难，部分原因是与问题的复杂性爆炸相关的分析挑战。为了便于识别这种组合药物遗传学关联，我们在此提出一种网络分析策略。具体而言，我们分析了100种畅销药物以及所有带有药物遗传学种系标签或给药指南的化合物的药物代谢酶和转运蛋白情况。基于这些数据，我们计算了在另一个基因 参与的条件下，基因 参与给定药物代谢、转运或毒性的后验概率，针对所有药物基因对（, ）。有趣的是，这些分析揭示了单个基因之间以及药物基因家族之间的显著模式，这些模式为代谢相互作用提供了见解。为了可视化基因-药物相互作用情况，我们使用多维缩放将这个相似性矩阵压缩成一个二维网络。我们认为节点之间的欧几里得距离可以反映上位性相互作用的可能性，因此可能提供一个有用的工具来缩小搜索空间并便于识别组合药物基因组学关联。
