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

Author

Listed:
  • 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
    Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital/Harvard Medical School)

  • Zsofia Kote-Jarai

    (Division of Genetics and Epidemiology, The Institute of Cancer Research
    Royal Marsden NHS Foundation Trust)

  • Rosalind Eeles

    (Division of Genetics and Epidemiology, The Institute of Cancer Research
    Royal Marsden NHS Foundation Trust)

  • 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
    University of California, Los Angeles
    University of California, Los Angeles)

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.

Suggested Citation

  • Nicholas Mancuso & Simon Gayther & Alexander Gusev & Wei Zheng & Kathryn L. Penney & Zsofia Kote-Jarai & Rosalind Eeles & Matthew Freedman & Christopher Haiman & Bogdan Pasaniuc, 2018. "Large-scale transcriptome-wide association study identifies new prostate cancer risk regions," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06302-1
    DOI: 10.1038/s41467-018-06302-1
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    Cited by:

    1. Arjun Bhattacharya & Anastasia N. Freedman & Vennela Avula & Rebeca Harris & Weifang Liu & Calvin Pan & Aldons J. Lusis & Robert M. Joseph & Lisa Smeester & Hadley J. Hartwell & Karl C. K. Kuban & Car, 2022. "Placental genomics mediates genetic associations with complex health traits and disease," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Qile Dai & Geyu Zhou & Hongyu Zhao & Urmo Võsa & Lude Franke & Alexis Battle & Alexander Teumer & Terho Lehtimäki & Olli T. Raitakari & Tõnu Esko & Michael P. Epstein & Jingjing Yang, 2023. "OTTERS: a powerful TWAS framework leveraging summary-level reference data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Luchang Ming & Debao Fu & Zhaona Wu & Hu Zhao & Xingbing Xu & Tingting Xu & Xiaohu Xiong & Mu Li & Yi Zheng & Ge Li & Ling Yang & Chunjiao Xia & Rongfang Zhou & Keyan Liao & Qian Yu & Wenqi Chai & Sij, 2023. "Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Jingni He & Wanqing Wen & Alicia Beeghly & Zhishan Chen & Chen Cao & Xiao-Ou Shu & Wei Zheng & Quan Long & Xingyi Guo, 2022. "Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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