Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.

解释遗传变异的影响是了解个体对疾病的易感性和设计个性化治疗方法的关键。现代实验技术能够生成大量人类基因组序列数据和相关分子和表型特征，以及基因组规模表达、表观基因组学和其他功能基因组数据。综合计算模型可以利用这些数据来了解变异的影响，阐明失调基因对特定疾病和组织环境中生物途径的影响，并解释超出单独实验可行范围的疾病风险。在这篇综述中，我们讨论了用于基因组解释和细胞综合分子水平建模的机器学习算法的最新进展，与疾病相关的组织和器官。更具体地说，我们强调了识别特定致病遗传变异并将它们与分子途径以及最终与疾病表型联系起来的现有方法和关键挑战和机遇。