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Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set

Author

Listed:
  • S. N. Thibodeau

    (Mayo Clinic College of Medicine)

  • A. J. French

    (Mayo Clinic College of Medicine)

  • S. K. McDonnell

    (Mayo Clinic College of Medicine)

  • J. Cheville

    (Mayo Clinic College of Medicine)

  • S. Middha

    (Mayo Clinic College of Medicine)

  • L. Tillmans

    (Mayo Clinic College of Medicine)

  • S. Riska

    (Mayo Clinic College of Medicine)

  • S. Baheti

    (Mayo Clinic College of Medicine)

  • M. C. Larson

    (Mayo Clinic College of Medicine)

  • Z. Fogarty

    (Mayo Clinic College of Medicine)

  • Y. Zhang

    (University of Maryland School of Medicine)

  • N. Larson

    (Mayo Clinic College of Medicine)

  • A. Nair

    (Mayo Clinic College of Medicine)

  • D. O’Brien

    (Mayo Clinic College of Medicine)

  • L. Wang

    (Medical College of Wisconsin)

  • D J. Schaid

    (Mayo Clinic College of Medicine)

Abstract

Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP–gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa.

Suggested Citation

  • S. N. Thibodeau & A. J. French & S. K. McDonnell & J. Cheville & S. Middha & L. Tillmans & S. Riska & S. Baheti & M. C. Larson & Z. Fogarty & Y. Zhang & N. Larson & A. Nair & D. O’Brien & L. Wang & D , 2015. "Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9653
    DOI: 10.1038/ncomms9653
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    Cited by:

    1. Yihao Lu & Meritxell Oliva & Brandon L. Pierce & Jin Liu & Lin S. Chen, 2024. "Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Nikolaos Giannareas & Qin Zhang & Xiayun Yang & Rong Na & Yijun Tian & Yuehong Yang & Xiaohao Ruan & Da Huang & Xiaoqun Yang & Chaofu Wang & Peng Zhang & Aki Manninen & Liang Wang & Gong-Hong Wei, 2022. "Extensive germline-somatic interplay contributes to prostate cancer progression through HNF1B co-option of TMPRSS2-ERG," Nature Communications, Nature, vol. 13(1), pages 1-22, December.

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