29Realising the potential of human induced pluripotent stem cell (iPSC) technology for drug 30 discovery, disease modelling and cell therapy requires an understanding of variability across 31 iPSC lines. While previous studies have characterized iPS cell lines genetically and 32 transcriptionally, little is known about the variability of the iPSC proteome. Here, we present 33 the first comprehensive proteomic iPSC dataset, analysing 202 iPSC lines derived from 151 34 donors. We characterise the major genetic determinants affecting proteome and transcriptome 35 variation across iPSC lines and identify key regulatory mechanisms affecting variation in 36 protein abundance. Our data identified >700 human iPSC protein quantitative trait loci 37 (pQTLs). We mapped trans regulatory effects, identifying an important role for protein-protein 38 interactions. We discovered that pQTLs show increased enrichment in disease-linked GWAS 39 variants, compared with RNA-based eQTLs. 40 41 42 Induced pluripotent stem cells (iPSC) hold enormous promise for advancing basic research 43 and biomedicine. By enabling the in vitro reconstitution of development and cell differentiation, 44 iPS cells allow the investigation of mechanisms underlying development and the aetiology of 45 many forms of genetic disease. To realize this potential, it is essential to characterize how 46 genetic and non-genetic effects in human iPSCs influence molecular and cellular phenotypes. 47 48 Recently, the establishment of population reference panels of normal human iPSC lines 1-3 49 have provided valuable resources for functional experiments in different genetic backgrounds.50 Additionally, these data have yielded detailed characterizations of the iPS transcriptome, 51 identifying thousands of cis expression Quantitative Trait Loci (eQTL) 1,4,5 , including at disease-52 relevant loci. While these RNA-based analyses are informative for studying mechanisms 53 affecting gene regulation at the transcriptional level, most cellular phenotypes involve 54 mechanisms acting downstream, at the protein level. Evidence in other contexts, including in 55 lymphoblast cell lines and in cancer, point to substantial differences in the genetic regulation 56 of protein and RNA traits, identifying protein QTL 6-9 and assessing the extent of buffering of 57 genetic effects between layers 10,11 . However, existing protein datasets have been limited by 58 scale (i.e. number of samples) or resolution (i.e. number of proteins, availability of RNA data).59 Importantly, no population-scale proteome datasets have been generated from human 60 pluripotent cells. 61 62 Here, we report on the first comprehensive, population-scale, combined proteomics and gene 63 expression analysis in human iPSC lines. Our data comprise matched quantitative proteomic 64 (TMT Mass Spectrometry) and transcriptomic (RNA-seq) profiles of 202 iPSC lines, derived 65 from 151 donors that are part of the HipSci project 1 . We identify both genetic and non-genetic 66 effects causing variability in protein expressi...