Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Nicholas Mancuso (), Simon Gayther, Alexander Gusev, Wei Zheng, Kathryn L. Penney, Zsofia Kote-Jarai, Rosalind Eeles, Matthew Freedman, Christopher Haiman and Bogdan Pasaniuc
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Nicholas Mancuso: University of California, Los Angeles
Simon Gayther: The Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center
Alexander Gusev: Dana Farber Cancer Institute
Wei Zheng: Vanderbilt University School of Medicine
Kathryn L. Penney: Department of Epidemiology, Harvard T.H. Chan School of Public Health
Zsofia Kote-Jarai: Division of Genetics and Epidemiology, The Institute of Cancer Research
Rosalind Eeles: Division of Genetics and Epidemiology, The Institute of Cancer Research
Matthew Freedman: Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School
Christopher Haiman: University of Southern California
Bogdan Pasaniuc: University of California, Los Angeles

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2 Mb. 23 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at a pre-defined level; this reduced the list of 217 associations to 109 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

Date: 2018
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DOI: 10.1038/s41467-018-06302-1

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