Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

中文翻译：

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精准医学——网络来救援。

遗传变异通常不能预测表型结果。携带与孟德尔疾病或复杂疾病相关的相同致病变异的个体可能表现出不同程度，从严重到轻度到无疾病。提高遗传变异预测临床表现的准确性已成为精准医学的最大挑战之一，这只能通过了解基因型-表型关系的机制来解决。了解这些关系的分子基础的努力已经确定了细胞功能基础上相互作用的生物分子的复杂系统。在这里，我们回顾了将细胞系统建模为相互作用蛋白质网络如何促进疾病相关过程的识别、潜在分子机制的描述以及变异致病性预测的最新进展。这篇综述旨在为临床医生、遗传学家和网络生物学家等提供启发，他们的目标是共同增进我们对人类疾病的理解并加速精准医学的进展。