Abstract Protein quantitative trait loci (pQTLs) are an invaluable source of information for drug target development as they provide genetic evidence to support protein function, suggest relationships between cis - and trans -associated proteins, and link proteins to disease where they collocate with genetic risk loci for clinical endpoints. Using the recently released Olink proteomics data for 1,463 proteins measured in over 54,000 samples of the UK Biobank we identified and replicated 4,248 associations with 2,821 ratios between protein levels (rQTLs) where the strengths of association at known pQTL loci increased by up to several hundred orders of magnitude. We attribute this increase in statistical power (p-gain) to accounting for genetic and non-genetic variance shared by the two proteins in the ratio pair. Protein pairs with a significant p-gain were 7.6-fold enriched in known protein-protein interactions, suggesting that their ratios reflect biological links between the implicated proteins. We then conducted a GWAS on the 2,821 ratios and identified 2,527 novel rQTLs, increasing the number of discovered genetic signals compared to the original protein-only GWAS by 24.7%. At examples we demonstrate that this approach can identify novel loci of clinical relevance, support causal gene identification, and reveal complex networks of interacting proteins. Taken together, our study adds significant value to the genetic insights that can be derived from the UKB proteomics data and motivates the wider use of ratios in large scale GWAS.

Genetic associations with ratios between protein levels detect new pQTLs and reveal protein-protein interactions