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    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 32, No. 9 ( 2023-09-01), p. 1198-1207
    Abstract: Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility. Methods: 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. Results: Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein–cancer associations [false discovery rate (FDR) & lt; 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein–cancer associations (FDR & lt; 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 (P & lt; 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). Conclusions: We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN 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. Impact: PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
    detail.hit.zdb_id: 2036781-8
    detail.hit.zdb_id: 1153420-5
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