Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10–77% of the variation in plasma (median 19.88%, interquartile range 14.01–31.09%), independent of technical factors (median 2.48%, interquartile range 0.78–6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein–disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.

广谱捕获蛋白质组学平台现在可以同时评估数千种血浆蛋白，但其中大多数不是主动分泌的，它们的来源在很大程度上是未知的。在这里，我们将基因组与深层表型信息相结合，以确定与 ~8,000 名大多数健康个体的 4,775 种血浆蛋白相关的可修饰和不可修饰因素。我们创建了一个数据驱动的对人血浆蛋白质组影响的图谱，并展示了基于主要解释因素的蛋白质分离成簇。对于超过三分之一 （N = 1,575） 的蛋白质靶标，联合遗传和非遗传因素解释了血浆中 10-77% 的变化（中位数 19.88%，四分位数间距 14.01-31.09%），与技术因素无关（中位数 2.48%，四分位数间距 0.78-6.41%）。结合遗传锚定的因果推理方法，我们的地图突出了数百种蛋白质-疾病关联（例如 COL6A3）的可改变风险因素与血浆蛋白之间的潜在因果关联，这可能介导了肾功能下降与心血管疾病之间的关联。我们提供了对人类血浆蛋白质组的生物学和技术影响图谱，以帮助将蛋白质组学研究的结果置于上下文中。