Abstract
<div>AbstractBackground:<p>Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility.</p>Methods:<p>We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis.</p>Results:<p>Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein–cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein–cancer associations (FDR < 0.05). Ten of 15 protein–cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (<i>P</i> < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62).</p>Conclusions:<p>We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in <i>SERPINA3</i> and <i>SNUPN</i> did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect.</p>Impact:<p>PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.</p></div>
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