Phosphorylation coordinates biological processes by regulating protein functions. Of the >100,000 human phosphorylation sites identified so far, around 95% do not have reported regulatory kinases or phosphatases. Kinases are effective drug targets, so delineating the connectivity of signalling networks will provide new therapeutic approaches. To accelerate this process, we aimed to leverage genetic variation to identify upstream signalling events. Since genetic variants are inherited before environmental exposures, we reasoned that identifying genomic regions associated with the of phosphosites, corrected for the total protein levels, could map to upstream regulators of those phosphosites. To test the genetic basis of phosphorylation in a proof-of-principle study, we performed a genome-wide association study of phosphosites measured in human induced pluripotent stem cells (iPSCs) from 137 unique donors, utilising mass spectra from a published total proteomics study (Mirauta et al. 2020). We searched the published mass spectra for phosphorylation, and despite no phosphopeptide enrichment being performed, we quantified 527 phosphosites in at least 30 samples. We identified quantitative trait loci (phosQTLs) in genes encoding kinases, phosphatases, and receptors such as MAPK10, PTPN14 and GPR83. A parallel genome-wide association study of protein abundances in the iPSCs enabled us to identify protein QTLs that colocalised with phosQTLs. Colocalised loci occurred in trans-, within genes for receptors, kinases, and phosphatases, demonstrating signal propagation from the upstream gene to intermediate steps in the signalling pathway. We identified phosQTLs in loci associated with diseases, including a phosQTL for the regulatory phosphosite PAK4 S181 also being reported to increase the risk of atrial fibrillation. The colocalised phosQTL and disease loci underpin the functional relevance of phosphorylation and provide a basis to understand how genetic variants rewire cells to increase disease risk. Our study demonstrates the potential for genetic approaches to delineate signalling networks in human cells. This method could be applied to map context-specific signalling events for diverse post-translational modifications in a range of cell types and tissues.