Abstract:
>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.
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Abstract:
>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.