Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

了解遗传变异如何影响疾病风险和复杂性状（变异到功能）是人类遗传学的主要挑战之一。在这里，我们提出了一个模型驱动的框架，以利用人类基因组规模的代谢网络来定义遗传变异如何影响主要人体组织（包括骨骼肌、脂肪、肝脏、大脑和心脏）的生化反应通量。作为概念证明，我们为 INTERVAL 和 UK Biobank 队列中的 524,615 名个体建立了个性化的器官特异性代谢通量模型，并进行了全通量组关联研究 (FWAS)，以确定个性化通量值与血液中代谢物浓度之间的 4312 种关联. 此外，我们应用 FWAS 来识别与患冠状动脉疾病的风险相关的 92 种代谢通量，其中许多与先前描述的在疾病中起作用的过程有关。我们的工作表明，遗传个性化代谢模型可以阐明遗传变异对常见人类疾病中涉及的生化反应的下游影响。